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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# CLIP Classification"
+      ],
+      "metadata": {
+        "id": "WvC6JWH_TGKm"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Р’ СЌС‚РѕРј Р±Р»РѕРєРЅРѕС‚Рµ РјС‹ Р±СѓРґРµРј РёСЃРїРѕР»СЊР·РѕРІР°С‚СЊ CLIP РґР»СЏ zero-shot РєР»Р°СЃСЃРёС„РёРєР°С†РёРё РЅР° РЅР°Р±РѕСЂРµ РґР°РЅРЅС‹С… CIFAR-10. Zero-shot РєР»Р°СЃСЃРёС„РёРєР°С†РёСЏ вЂ” СЌС‚Рѕ РјРµС‚РѕРґ, РїСЂРё РєРѕС‚РѕСЂРѕРј РјРѕРґРµР»СЊ РєР»Р°СЃСЃРёС„РёС†РёСЂСѓРµС‚ РґР°РЅРЅС‹Рµ, РїСЂРёРјРµСЂРѕРІ РєРѕС‚РѕСЂС‹С… РѕРЅР° РЅРёРєРѕРіРґР° РЅРµ РІРёРґРµР»Р° РІРѕ РІСЂРµРјСЏ РѕР±СѓС‡РµРЅРёСЏ. РўР°РєРёРј РѕР±СЂР°Р·РѕРј, РѕРЅР° РґРѕР»Р¶РЅР° РїРѕР»Р°РіР°С‚СЊСЃСЏ РЅР° РєР°С‡РµСЃС‚РІРѕ С‚РµРєСЃС‚РѕРІС‹С… РѕРїРёСЃР°РЅРёР№ РєР»Р°СЃСЃРѕРІ Рё СЃРІРѕСЋ СЃРїРѕСЃРѕР±РЅРѕСЃС‚СЊ Рє РѕР±РѕР±С‰РµРЅРёСЋ."
+      ],
+      "metadata": {
+        "id": "1rFdV4UiTH0u"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "53N4k0pj_9qL"
+      },
+      "source": [
+        "РЈР±РµРґРёС‚РµСЃСЊ, С‡С‚Рѕ РІС‹ РёСЃРїРѕР»СЊР·СѓРµС‚Рµ СЃСЂРµРґСѓ РІС‹РїРѕР»РЅРµРЅРёСЏ СЃ GPU:  \n",
+        "- Р’С‹Р±РµСЂРёС‚Рµ В«GPUВ» РІ РєР°С‡РµСЃС‚РІРµ Р°РїРїР°СЂР°С‚РЅРѕРіРѕ СѓСЃРєРѕСЂРёС‚РµР»СЏ РІ СЂР°Р·РґРµР»Рµ **РЎСЂРµРґР° РІС‹РїРѕР»РЅРµРЅРёСЏ > РЎРјРµРЅРёС‚СЊ СЃСЂРµРґСѓ РІС‹РїРѕР»РЅРµРЅРёСЏ**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0BpdJkdBssk9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b238a097-8259-4889-9bf0-3c20838a6cd7"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting git+https://github.com/openai/CLIP.git\n",
+            "  Cloning https://github.com/openai/CLIP.git to /tmp/pip-req-build-mahomhnj\n",
+            "  Running command git clone --filter=blob:none --quiet https://github.com/openai/CLIP.git /tmp/pip-req-build-mahomhnj\n",
+            "  Resolved https://github.com/openai/CLIP.git to commit dcba3cb2e2827b402d2701e7e1c7d9fed8a20ef1\n",
+            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n",
+            "Collecting ftfy (from clip==1.0)\n",
+            "  Downloading ftfy-6.3.1-py3-none-any.whl.metadata (7.3 kB)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from clip==1.0) (24.2)\n",
+            "Requirement already satisfied: regex in /usr/local/lib/python3.11/dist-packages (from clip==1.0) (2024.11.6)\n",
+            "Requirement already satisfied: torch in /usr/local/lib/python3.11/dist-packages (from clip==1.0) (2.5.1+cu121)\n",
+            "Requirement already satisfied: torchvision in /usr/local/lib/python3.11/dist-packages (from clip==1.0) (0.20.1+cu121)\n",
+            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.11/dist-packages (from ftfy->clip==1.0) (0.2.13)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch->clip==1.0) (3.17.0)\n",
+            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.11/dist-packages (from torch->clip==1.0) (4.12.2)\n",
+            "Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch->clip==1.0) (3.4.2)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch->clip==1.0) (3.1.5)\n",
+            "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch->clip==1.0) (2024.10.0)\n",
+            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.11/dist-packages (from torch->clip==1.0) (12.1.105)\n",
+            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.11/dist-packages (from torch->clip==1.0) (12.1.105)\n",
+            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.11/dist-packages (from torch->clip==1.0) (12.1.105)\n",
+            "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.11/dist-packages (from torch->clip==1.0) (9.1.0.70)\n",
+            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.11/dist-packages (from torch->clip==1.0) (12.1.3.1)\n",
+            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.11/dist-packages (from torch->clip==1.0) (11.0.2.54)\n",
+            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.11/dist-packages (from torch->clip==1.0) (10.3.2.106)\n",
+            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.11/dist-packages (from torch->clip==1.0) (11.4.5.107)\n",
+            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.11/dist-packages (from torch->clip==1.0) (12.1.0.106)\n",
+            "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch->clip==1.0) (2.21.5)\n",
+            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.11/dist-packages (from torch->clip==1.0) (12.1.105)\n",
+            "Requirement already satisfied: triton==3.1.0 in /usr/local/lib/python3.11/dist-packages (from torch->clip==1.0) (3.1.0)\n",
+            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch->clip==1.0) (1.13.1)\n",
+            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.11/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->clip==1.0) (12.6.85)\n",
+            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch->clip==1.0) (1.3.0)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from torchvision->clip==1.0) (1.26.4)\n",
+            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.11/dist-packages (from torchvision->clip==1.0) (11.1.0)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch->clip==1.0) (3.0.2)\n",
+            "Downloading ftfy-6.3.1-py3-none-any.whl (44 kB)\n",
+            "\u001b[2K   \u001b[90mв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓв”Ѓ\u001b[0m \u001b[32m44.8/44.8 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hBuilding wheels for collected packages: clip\n",
+            "  Building wheel for clip (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for clip: filename=clip-1.0-py3-none-any.whl size=1369489 sha256=e2e1f2eb71b82d206740e55fde221dfaf3322a0e4d52827a76915ba2162c098b\n",
+            "  Stored in directory: /tmp/pip-ephem-wheel-cache-cvfolwbe/wheels/3f/7c/a4/9b490845988bf7a4db33674d52f709f088f64392063872eb9a\n",
+            "Successfully built clip\n",
+            "Installing collected packages: ftfy, clip\n",
+            "Successfully installed clip-1.0 ftfy-6.3.1\n"
+          ]
+        }
+      ],
+      "source": [
+        "! pip install tqdm git+https://github.com/openai/CLIP.git\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import torch\n",
+        "import clip\n",
+        "from tqdm.notebook import tqdm\n",
+        "import os\n",
+        "from torchvision.datasets import CIFAR10\n",
+        "from torchvision import transforms"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eFxgLV5HAEEw"
+      },
+      "source": [
+        "# Р—Р°РіСЂСѓР·РєР° РјРѕРґРµР»Рё\n",
+        "\n",
+        "РЎРєР°С‡Р°Р№С‚Рµ Рё СЃРѕР·РґР°Р№С‚Рµ СЌРєР·РµРјРїР»СЏСЂ РјРѕРґРµР»Рё CLIP СЃ РёСЃРїРѕР»СЊР·РѕРІР°РЅРёРµРј РјРѕРґСѓР»СЏ `clip`, РєРѕС‚РѕСЂС‹Р№ РјС‹ С‚РѕР»СЊРєРѕ С‡С‚Рѕ СѓСЃС‚Р°РЅРѕРІРёР»Рё."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cboKZocQlSYX"
+      },
+      "outputs": [],
+      "source": [
+        "# Load in a ViT CLIP model\n",
+        "model, preprocess = clip.load(\"ViT-B/32\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Р—Р°РіСЂСѓР·РєР° РґР°С‚Р°СЃРµС‚Р° CIFAR-10"
+      ],
+      "metadata": {
+        "id": "KyJFNTN9rKzh"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-sMBlsyRv967",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8e2314eb-9d93-451f-f980-ed3acbe16853"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to /root/.cache/cifar-10-python.tar.gz\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 170M/170M [00:05<00:00, 31.2MB/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Extracting /root/.cache/cifar-10-python.tar.gz to /root/.cache\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Load in CIFAR-10 dataset\n",
+        "cifar10 = CIFAR10(root=os.path.expanduser(\"~/.cache\"), download=True, train=False, transform=preprocess)\n",
+        "loader = torch.utils.data.DataLoader(cifar10, batch_size=32, shuffle=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "РЎР»РµРґСѓСЋС‰РёР№ РєРѕРґ РѕРїСЂРµРґРµР»СЏРµС‚ С€Р°Р±Р»РѕРЅС‹ Рё РєР»Р°СЃСЃС‹ CIFAR-10, С‡С‚РѕР±С‹ СѓС‡РµСЃС‚СЊ РІРѕР·РјРѕР¶РЅС‹Рµ СЏР·С‹РєРѕРІС‹Рµ СЂР°СЃС€РёСЂРµРЅРёСЏ РґР»СЏ РѕРїРёСЃР°РЅРёСЏ РёР·РѕР±СЂР°Р¶РµРЅРёР№."
+      ],
+      "metadata": {
+        "id": "W7R3Wf2jTRkR"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zczgkbGrv967"
+      },
+      "outputs": [],
+      "source": [
+        "# РћРїСЂРµРґРµР»СЏРµРј С€Р°Р±Р»РѕРЅС‹ Рё РєР»Р°СЃСЃС‹ CIFAR-10, С‡С‚РѕР±С‹ РѕС‚СЂР°Р·РёС‚СЊ РІРѕР·РјРѕР¶РЅС‹Рµ СЏР·С‹РєРѕРІС‹Рµ Р°СѓРіРјРµРЅС‚Р°С†РёРё РґР»СЏ РѕРїРёСЃР°РЅРёСЏ РёР·РѕР±СЂР°Р¶РµРЅРёР№\n",
+        "cifar10_templates = [\n",
+        "    'a photo of a {}.',\n",
+        "    'a blurry photo of a {}.',\n",
+        "    'a black and white photo of a {}.',\n",
+        "    'a low contrast photo of a {}.',\n",
+        "    'a high contrast photo of a {}.',\n",
+        "    'a bad photo of a {}.',\n",
+        "    'a good photo of a {}.',\n",
+        "    'a photo of a small {}.',\n",
+        "    'a photo of a big {}.',\n",
+        "    'a photo of the {}.',\n",
+        "    'a blurry photo of the {}.',\n",
+        "    'a black and white photo of the {}.',\n",
+        "    'a low contrast photo of the {}.',\n",
+        "    'a high contrast photo of the {}.',\n",
+        "    'a bad photo of the {}.',\n",
+        "    'a good photo of the {}.',\n",
+        "    'a photo of the small {}.',\n",
+        "    'a photo of the big {}.',\n",
+        "]\n",
+        "\n",
+        "cifar10_classes = [\n",
+        "    'airplane',\n",
+        "    'automobile',\n",
+        "    'bird',\n",
+        "    'cat',\n",
+        "    'deer',\n",
+        "    'dog',\n",
+        "    'frog',\n",
+        "    'horse',\n",
+        "    'ship',\n",
+        "    'truck',\n",
+        "]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fz6D-F-Wbrtp"
+      },
+      "source": [
+        "# РЎРѕР·РґР°РЅРёРµ РІРµСЃРѕРІ РґР»СЏ zero-shot РєР»Р°СЃСЃРёС„РёРєР°С‚РѕСЂР°"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Our goal is to generate zero-shot classification weights for the list of CIFAR-10 classes using text templates and the CLIP model. These weights can be used for zero-shot classification tasks where you want to classify images or other data into one of the specified classes, even if the model hasnвЂ™t been explicitly trained on those classes.\n",
+        "\n",
+        "\n",
+        "1. In the zeroshot_classifier function, the code iterates through each class name in the provided class_names list (e.g., 'airplane,' 'automobile,' etc.)\n",
+        "2. For each class name, it uses the templates to create formatted textual descriptions.\n",
+        "3. These formatted texts are tokenized and converted to embeddings using the CLIP model's text encoder, then normalized to ensure consistent scale.\n",
+        "4. The embeddings for each description are averaged to create a representative embedding for the class, and this average embedding is again normalized.\n",
+        "5. The code collects all these embeddings and stacks them into a tensor, resulting in a matrix where each column corresponds to the averaged, normalized embeddings for a specific class.\n",
+        "\n",
+        "РќР°С€Р° С†РµР»СЊ вЂ” СЃРіРµРЅРµСЂРёСЂРѕРІР°С‚СЊ РІРµСЃР° РґР»СЏ zero-shot РєР»Р°СЃСЃРёС„РёРєР°С†РёРё СЃРїРёСЃРєР° РєР»Р°СЃСЃРѕРІ CIFAR-10, РёСЃРїРѕР»СЊР·СѓСЏ С‚РµРєСЃС‚РѕРІС‹Рµ С€Р°Р±Р»РѕРЅС‹ Рё РјРѕРґРµР»СЊ CLIP. РС‚Рё РІРµСЃР° РјРѕР¶РЅРѕ РёСЃРїРѕР»СЊР·РѕРІР°С‚СЊ РґР»СЏ Р·Р°РґР°С‡ zero-shot РєР»Р°СЃСЃРёС„РёРєР°С†РёРё, РїРѕР·РІРѕР»СЏСЏ РєР»Р°СЃСЃРёС„РёС†РёСЂРѕРІР°С‚СЊ РёР·РѕР±СЂР°Р¶РµРЅРёСЏ РёР»Рё РґСЂСѓРіРёРµ РґР°РЅРЅС‹Рµ РІ РѕРґРёРЅ РёР· СѓРєР°Р·Р°РЅРЅС‹С… РєР»Р°СЃСЃРѕРІ, РґР°Р¶Рµ РµСЃР»Рё РјРѕРґРµР»СЊ СЏРІРЅРѕ РЅРµ РѕР±СѓС‡Р°Р»Р°СЃСЊ РЅР° РЅРёС….\n",
+        "\n",
+        "1. Р’ С„СѓРЅРєС†РёРё `zeroshot_classifier` РєРѕРґ РїСЂРѕС…РѕРґРёС‚ РїРѕ РєР°Р¶РґРѕРјСѓ РёРјРµРЅРё РєР»Р°СЃСЃР° РёР· СЃРїРёСЃРєР° `class_names` (РЅР°РїСЂРёРјРµСЂ, 'airplane', 'automobile' Рё С‚. Рґ.).\n",
+        "2. Р”Р»СЏ РєР°Р¶РґРѕРіРѕ РёРјРµРЅРё РєР»Р°СЃСЃР° РёСЃРїРѕР»СЊР·СѓСЋС‚СЃСЏ С€Р°Р±Р»РѕРЅС‹ РґР»СЏ СЃРѕР·РґР°РЅРёСЏ С„РѕСЂРјР°С‚РёСЂРѕРІР°РЅРЅС‹С… С‚РµРєСЃС‚РѕРІС‹С… РѕРїРёСЃР°РЅРёР№.\n",
+        "3. РС‚Рё С‚РµРєСЃС‚РѕРІС‹Рµ РѕРїРёСЃР°РЅРёСЏ С‚РѕРєРµРЅРёР·РёСЂСѓСЋС‚СЃСЏ Рё РїСЂРµРѕР±СЂР°Р·СѓСЋС‚СЃСЏ РІ СЌРјР±РµРґРґРёРЅРіРё СЃ РїРѕРјРѕС‰СЊСЋ С‚РµРєСЃС‚РѕРІРѕРіРѕ СЌРЅРєРѕРґРµСЂР° РјРѕРґРµР»Рё CLIP, Р·Р°С‚РµРј РЅРѕСЂРјР°Р»РёР·СѓСЋС‚СЃСЏ РґР»СЏ РѕР±РµСЃРїРµС‡РµРЅРёСЏ РєРѕРЅСЃРёСЃС‚РµРЅС‚РЅРѕРіРѕ РјР°СЃС€С‚Р°Р±Р° (СЂР°Р·РјРµСЂР°).\n",
+        "4. РРјР±РµРґРґРёРЅРіРё РґР»СЏ РєР°Р¶РґРѕРіРѕ РѕРїРёСЃР°РЅРёСЏ СѓСЃСЂРµРґРЅСЏСЋС‚СЃСЏ РґР»СЏ СЃРѕР·РґР°РЅРёСЏ СЂРµРїСЂРµР·РµРЅС‚Р°С‚РёРІРЅС‹Р№ СЌРјР±РµРґРґРёРЅРіР° РєР»Р°СЃСЃР°, РєРѕС‚РѕСЂС‹Р№ Р·Р°С‚РµРј СЃРЅРѕРІР° РЅРѕСЂРјР°Р»РёР·СѓРµС‚СЃСЏ.\n",
+        "5. Р’СЃРµ СЌРјР±РµРґРґРёРЅРіРё СЃРѕР±РёСЂР°СЋС‚СЃСЏ Рё РѕР±СЉРµРґРёРЅСЏСЋС‚СЃСЏ РІ С‚РµРЅР·РѕСЂ, РІ СЂРµР·СѓР»СЊС‚Р°С‚Рµ С‡РµРіРѕ РїРѕР»СѓС‡Р°РµС‚СЃСЏ РјР°С‚СЂРёС†Р°, РіРґРµ РєР°Р¶РґС‹Р№ СЃС‚РѕР»Р±РµС† СЃРѕРѕС‚РІРµС‚СЃС‚РІСѓРµС‚ СѓСЃСЂРµРґРЅРµРЅРЅС‹Рј Рё РЅРѕСЂРјР°Р»РёР·РѕРІР°РЅРЅС‹Рј СЌРјР±РµРґРґРёРЅРіР°Рј РѕРїСЂРµРґРµР»РµРЅРЅРѕРіРѕ РєР»Р°СЃСЃР°."
+      ],
+      "metadata": {
+        "id": "cI3-f94lTWtM"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sRqDoz1Gbsii",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ade19e06-343b-4d39-cdce-5fe1b3befee7"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "['a photo of a airplane.', 'a blurry photo of a airplane.', 'a black and white photo of a airplane.', 'a low contrast photo of a airplane.', 'a high contrast photo of a airplane.', 'a bad photo of a airplane.', 'a good photo of a airplane.', 'a photo of a small airplane.', 'a photo of a big airplane.', 'a photo of the airplane.', 'a blurry photo of the airplane.', 'a black and white photo of the airplane.', 'a low contrast photo of the airplane.', 'a high contrast photo of the airplane.', 'a bad photo of the airplane.', 'a good photo of the airplane.', 'a photo of the small airplane.', 'a photo of the big airplane.']\n",
+            "torch.Size([18, 77])\n",
+            "torch.Size([18, 512])\n",
+            "torch.Size([512])\n",
+            "['a photo of a automobile.', 'a blurry photo of a automobile.', 'a black and white photo of a automobile.', 'a low contrast photo of a automobile.', 'a high contrast photo of a automobile.', 'a bad photo of a automobile.', 'a good photo of a automobile.', 'a photo of a small automobile.', 'a photo of a big automobile.', 'a photo of the automobile.', 'a blurry photo of the automobile.', 'a black and white photo of the automobile.', 'a low contrast photo of the automobile.', 'a high contrast photo of the automobile.', 'a bad photo of the automobile.', 'a good photo of the automobile.', 'a photo of the small automobile.', 'a photo of the big automobile.']\n",
+            "torch.Size([18, 77])\n",
+            "torch.Size([18, 512])\n",
+            "torch.Size([512])\n",
+            "['a photo of a bird.', 'a blurry photo of a bird.', 'a black and white photo of a bird.', 'a low contrast photo of a bird.', 'a high contrast photo of a bird.', 'a bad photo of a bird.', 'a good photo of a bird.', 'a photo of a small bird.', 'a photo of a big bird.', 'a photo of the bird.', 'a blurry photo of the bird.', 'a black and white photo of the bird.', 'a low contrast photo of the bird.', 'a high contrast photo of the bird.', 'a bad photo of the bird.', 'a good photo of the bird.', 'a photo of the small bird.', 'a photo of the big bird.']\n",
+            "torch.Size([18, 77])\n",
+            "torch.Size([18, 512])\n",
+            "torch.Size([512])\n",
+            "['a photo of a cat.', 'a blurry photo of a cat.', 'a black and white photo of a cat.', 'a low contrast photo of a cat.', 'a high contrast photo of a cat.', 'a bad photo of a cat.', 'a good photo of a cat.', 'a photo of a small cat.', 'a photo of a big cat.', 'a photo of the cat.', 'a blurry photo of the cat.', 'a black and white photo of the cat.', 'a low contrast photo of the cat.', 'a high contrast photo of the cat.', 'a bad photo of the cat.', 'a good photo of the cat.', 'a photo of the small cat.', 'a photo of the big cat.']\n",
+            "torch.Size([18, 77])\n",
+            "torch.Size([18, 512])\n",
+            "torch.Size([512])\n",
+            "['a photo of a deer.', 'a blurry photo of a deer.', 'a black and white photo of a deer.', 'a low contrast photo of a deer.', 'a high contrast photo of a deer.', 'a bad photo of a deer.', 'a good photo of a deer.', 'a photo of a small deer.', 'a photo of a big deer.', 'a photo of the deer.', 'a blurry photo of the deer.', 'a black and white photo of the deer.', 'a low contrast photo of the deer.', 'a high contrast photo of the deer.', 'a bad photo of the deer.', 'a good photo of the deer.', 'a photo of the small deer.', 'a photo of the big deer.']\n",
+            "torch.Size([18, 77])\n",
+            "torch.Size([18, 512])\n",
+            "torch.Size([512])\n",
+            "['a photo of a dog.', 'a blurry photo of a dog.', 'a black and white photo of a dog.', 'a low contrast photo of a dog.', 'a high contrast photo of a dog.', 'a bad photo of a dog.', 'a good photo of a dog.', 'a photo of a small dog.', 'a photo of a big dog.', 'a photo of the dog.', 'a blurry photo of the dog.', 'a black and white photo of the dog.', 'a low contrast photo of the dog.', 'a high contrast photo of the dog.', 'a bad photo of the dog.', 'a good photo of the dog.', 'a photo of the small dog.', 'a photo of the big dog.']\n",
+            "torch.Size([18, 77])\n",
+            "torch.Size([18, 512])\n",
+            "torch.Size([512])\n",
+            "['a photo of a frog.', 'a blurry photo of a frog.', 'a black and white photo of a frog.', 'a low contrast photo of a frog.', 'a high contrast photo of a frog.', 'a bad photo of a frog.', 'a good photo of a frog.', 'a photo of a small frog.', 'a photo of a big frog.', 'a photo of the frog.', 'a blurry photo of the frog.', 'a black and white photo of the frog.', 'a low contrast photo of the frog.', 'a high contrast photo of the frog.', 'a bad photo of the frog.', 'a good photo of the frog.', 'a photo of the small frog.', 'a photo of the big frog.']\n",
+            "torch.Size([18, 77])\n",
+            "torch.Size([18, 512])\n",
+            "torch.Size([512])\n",
+            "['a photo of a horse.', 'a blurry photo of a horse.', 'a black and white photo of a horse.', 'a low contrast photo of a horse.', 'a high contrast photo of a horse.', 'a bad photo of a horse.', 'a good photo of a horse.', 'a photo of a small horse.', 'a photo of a big horse.', 'a photo of the horse.', 'a blurry photo of the horse.', 'a black and white photo of the horse.', 'a low contrast photo of the horse.', 'a high contrast photo of the horse.', 'a bad photo of the horse.', 'a good photo of the horse.', 'a photo of the small horse.', 'a photo of the big horse.']\n",
+            "torch.Size([18, 77])\n",
+            "torch.Size([18, 512])\n",
+            "torch.Size([512])\n",
+            "['a photo of a ship.', 'a blurry photo of a ship.', 'a black and white photo of a ship.', 'a low contrast photo of a ship.', 'a high contrast photo of a ship.', 'a bad photo of a ship.', 'a good photo of a ship.', 'a photo of a small ship.', 'a photo of a big ship.', 'a photo of the ship.', 'a blurry photo of the ship.', 'a black and white photo of the ship.', 'a low contrast photo of the ship.', 'a high contrast photo of the ship.', 'a bad photo of the ship.', 'a good photo of the ship.', 'a photo of the small ship.', 'a photo of the big ship.']\n",
+            "torch.Size([18, 77])\n",
+            "torch.Size([18, 512])\n",
+            "torch.Size([512])\n",
+            "['a photo of a truck.', 'a blurry photo of a truck.', 'a black and white photo of a truck.', 'a low contrast photo of a truck.', 'a high contrast photo of a truck.', 'a bad photo of a truck.', 'a good photo of a truck.', 'a photo of a small truck.', 'a photo of a big truck.', 'a photo of the truck.', 'a blurry photo of the truck.', 'a black and white photo of the truck.', 'a low contrast photo of the truck.', 'a high contrast photo of the truck.', 'a bad photo of the truck.', 'a good photo of the truck.', 'a photo of the small truck.', 'a photo of the big truck.']\n",
+            "torch.Size([18, 77])\n",
+            "torch.Size([18, 512])\n",
+            "torch.Size([512])\n",
+            "torch.Size([512, 10])\n"
+          ]
+        }
+      ],
+      "source": [
+        "def zeroshot_classifier(class_names, templates):\n",
+        "    \"\"\"\n",
+        "    Generates zero-shot classification weights by averaging the embedded templates of class names.\n",
+        "\n",
+        "    Parameters:\n",
+        "    class_names (list): A list of names of the classes.\n",
+        "    templates (list): A list of templates to be used in the formatting of class names.\n",
+        "\n",
+        "    Returns:\n",
+        "    Tensor: A tensor containing the zero-shot classification weights.\n",
+        "    \"\"\"\n",
+        "    \"\"\"\n",
+        "    Р“РµРЅРµСЂРёСЂСѓРµС‚ РІРµСЃР° РґР»СЏ zero-shot РєР»Р°СЃСЃРёС„РёРєР°С†РёРё РїСѓС‚РµРј СѓСЃСЂРµРґРЅРµРЅРёСЏ СЌРјР±РµРґРґРёРЅРіРѕРІ\n",
+        "    С€Р°Р±Р»РѕРЅРѕРІ СЃ РЅР°Р·РІР°РЅРёСЏРјРё РєР»Р°СЃСЃРѕРІ.\n",
+        "\n",
+        "    РџР°СЂР°РјРµС‚СЂС‹:\n",
+        "    class_names (list): РЎРїРёСЃРѕРє РЅР°Р·РІР°РЅРёР№ РєР»Р°СЃСЃРѕРІ.\n",
+        "    templates (list): РЎРїРёСЃРѕРє С€Р°Р±Р»РѕРЅРѕРІ, РёСЃРїРѕР»СЊР·СѓРµРјС‹С… РґР»СЏ С„РѕСЂРјР°С‚РёСЂРѕРІР°РЅРёСЏ РЅР°Р·РІР°РЅРёР№ РєР»Р°СЃСЃРѕРІ.\n",
+        "\n",
+        "    Р’РѕР·РІСЂР°С‰Р°РµС‚:\n",
+        "    Tensor: РўРµРЅР·РѕСЂ, СЃРѕРґРµСЂР¶Р°С‰РёР№ РІРµСЃР° РґР»СЏ zero-shot РєР»Р°СЃСЃРёС„РёРєР°С†РёРё.\n",
+        "    \"\"\"\n",
+        "    with torch.no_grad():\n",
+        "        classification_weights = []\n",
+        "        for class_name in class_names:\n",
+        "            formatted_texts = [template.format(class_name) for template in templates]\n",
+        "            print(formatted_texts)\n",
+        "            tokenized_texts = clip.tokenize(formatted_texts).cuda()\n",
+        "            print(tokenized_texts.shape)\n",
+        "\n",
+        "            # TODO: Get embeddings using the CLIP model's text encoder and normalize\n",
+        "            # TODO: РџРѕР»СѓС‡РёС‚СЊ СЌРјР±РµРґРґРёРЅРіРё СЃ РёСЃРїРѕР»СЊР·РѕРІР°РЅРёРµРј С‚РµРєСЃС‚РѕРІРѕРіРѕ СЌРЅРєРѕРґРµСЂР° РјРѕРґРµР»Рё CLIP Рё РЅРѕСЂРјР°Р»РёР·РѕРІР°С‚СЊ\n",
+        "            # class_embeddings = ...\n",
+        "            class_embeddings = model.encode_text(tokenized_texts)\n",
+        "            class_embeddings /= class_embeddings.norm(dim=-1, keepdim=True)\n",
+        "            print(class_embeddings.shape)\n",
+        "\n",
+        "\n",
+        "            # TODO: Average the embeddings and normalize again\n",
+        "            # TODO: РЈСЃСЂРµРґРЅРёС‚СЊ СЌРјР±РµРґРґРёРЅРіРё Рё СЃРЅРѕРІР° РЅРѕСЂРјР°Р»РёР·РѕРІР°С‚СЊ\n",
+        "            # average_embedding = ...\n",
+        "            average_embedding = class_embeddings.mean(dim=0)\n",
+        "            average_embedding /= average_embedding.norm()\n",
+        "            print(average_embedding.shape)\n",
+        "\n",
+        "            # Store the averaged and normalized embedding\n",
+        "            # РЎРѕС…СЂР°РЅРёС‚СЊ СѓСЃСЂРµРґРЅРµРЅРЅС‹Р№ Рё РЅРѕСЂРјР°Р»РёР·РѕРІР°РЅРЅС‹Р№ СЌРјР±РµРґРґРёРЅРі\n",
+        "            classification_weights.append(average_embedding)\n",
+        "\n",
+        "        classification_weights = torch.stack(classification_weights, dim=1).cuda()\n",
+        "        print(classification_weights.shape)\n",
+        "\n",
+        "    return classification_weights\n",
+        "\n",
+        "zeroshot_weights = zeroshot_classifier(cifar10_classes, cifar10_templates)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1fZo7hG8iJP5"
+      },
+      "source": [
+        "# Zero-shot РїСЂРµРґСЃРєР°Р·Р°РЅРёРµ"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "WeвЂ™re now going to calculate the accuracy of a model's predictions, specifically for the top-k predictions. It's common to measure the accuracy of not just the top-1 prediction (the most confident prediction) but also the top-k predictions (the k most confident predictions), to see if the model is generally leaning toward predicting the right class, just maybe not with the highest certainty.\n",
+        "\n",
+        "HereвЂ™s how it works:\n",
+        "* Identifies the top-k predictions by selecting the indices of the k highest values in the predictions tensor.\n",
+        "* Compares these top-k predictions with the ground truth labels to check if they are correct. The results are stored in the correct_comparisons tensor.\n",
+        "* For each topk, it calculates the number of correct predictions in the top-k predictions.\n",
+        "* The number of correct predictions for each k is returned\n",
+        "\n",
+        "РЎРµР№С‡Р°СЃ РјС‹ Р±СѓРґРµРј РІС‹С‡РёСЃР»СЏС‚СЊ С‚РѕС‡РЅРѕСЃС‚СЊ РїСЂРµРґСЃРєР°Р·Р°РЅРёР№ РјРѕРґРµР»Рё, РІ С‡Р°СЃС‚РЅРѕСЃС‚Рё РґР»СЏ top-k РїСЂРµРґСЃРєР°Р·Р°РЅРёР№. Р§Р°СЃС‚Рѕ РёР·РјРµСЂСЏРµС‚СЃСЏ С‚РѕС‡РЅРѕСЃС‚СЊ РЅРµ С‚РѕР»СЊРєРѕ РґР»СЏ top-1 РїСЂРµРґСЃРєР°Р·Р°РЅРёСЏ (СЃ РЅР°РёР±РѕР»СЊС€РµР№ СѓРІРµСЂРµРЅРЅРѕСЃС‚СЊСЋ), РЅРѕ Рё РґР»СЏ top-k РїСЂРµРґСЃРєР°Р·Р°РЅРёР№ (k СЃ РЅР°РёР±РѕР»СЊС€РµР№ СѓРІРµСЂРµРЅРЅРѕСЃС‚СЊСЋ), С‡С‚РѕР±С‹ РїРѕРЅСЏС‚СЊ, СЃРєР»РѕРЅРЅР° Р»Рё РјРѕРґРµР»СЊ Рє РїСЂР°РІРёР»СЊРЅРѕР№ РєР»Р°СЃСЃРёС„РёРєР°С†РёРё, РґР°Р¶Рµ РµСЃР»Рё РЅРµ РІС‹Р±РёСЂР°РµС‚ РїСЂР°РІРёР»СЊРЅС‹Р№ РєР»Р°СЃСЃ СЃ РЅР°РёР±РѕР»СЊС€РµР№ СѓРІРµСЂРµРЅРЅРѕСЃС‚СЊСЋ.\n",
+        "\n",
+        "РљР°Рє СЌС‚Рѕ СЂР°Р±РѕС‚Р°РµС‚:\n",
+        "* РћРїСЂРµРґРµР»СЏСЋС‚СЃСЏ top-k РїСЂРµРґСЃРєР°Р·Р°РЅРёСЏ РїСѓС‚РµРј РІС‹Р±РѕСЂР° РёРЅРґРµРєСЃРѕРІ k РЅР°РёР±РѕР»СЊС€РёС… Р·РЅР°С‡РµРЅРёР№ РІ С‚РµРЅР·РѕСЂРµ РїСЂРµРґСЃРєР°Р·Р°РЅРёР№.\n",
+        "* РС‚Рё top-k РїСЂРµРґСЃРєР°Р·Р°РЅРёСЏ СЃСЂР°РІРЅРёРІР°СЋС‚СЃСЏ СЃ РёСЃС‚РёРЅРЅС‹РјРё РјРµС‚РєР°РјРё (ground truth), С‡С‚РѕР±С‹ РїСЂРѕРІРµСЂРёС‚СЊ РёС… РїСЂР°РІРёР»СЊРЅРѕСЃС‚СЊ. Р РµР·СѓР»СЊС‚Р°С‚С‹ СЃРѕС…СЂР°РЅСЏСЋС‚СЃСЏ РІ С‚РµРЅР·РѕСЂРµ `correct_comparisons`.\n",
+        "* Р”Р»СЏ РєР°Р¶РґРѕРіРѕ `top-k` РІС‹С‡РёСЃР»СЏРµС‚СЃСЏ РєРѕР»РёС‡РµСЃС‚РІРѕ РїСЂР°РІРёР»СЊРЅС‹С… РїСЂРµРґСЃРєР°Р·Р°РЅРёР№.\n",
+        "* РљРѕР»РёС‡РµСЃС‚РІРѕ РїСЂР°РІРёР»СЊРЅС‹С… РїСЂРµРґСЃРєР°Р·Р°РЅРёР№ РґР»СЏ РєР°Р¶РґРѕРіРѕ k РІРѕР·РІСЂР°С‰Р°РµС‚СЃСЏ."
+      ],
+      "metadata": {
+        "id": "choKBH5PTZlC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "j4kPSZoShQxN"
+      },
+      "outputs": [],
+      "source": [
+        "def accuracy(predictions, targets, topk=(1,)):\n",
+        "    \"\"\"\n",
+        "    Calculate the accuracy of the top k predictions\n",
+        "\n",
+        "    Parameters:\n",
+        "    predictions (Tensor): The model output.\n",
+        "    targets (Tensor): Ground truth labels.\n",
+        "    topk (tuple, optional): Tuple indicating the top k predictions to consider for accuracy.\n",
+        "\n",
+        "    Returns:\n",
+        "    list: A list of accuracies for each k in topk.\n",
+        "    \"\"\"\n",
+        "    \"\"\"\n",
+        "    Р’С‹С‡РёСЃР»РµРЅРёРµ С‚РѕС‡РЅРѕСЃС‚Рё РґР»СЏ top-k РїСЂРµРґСЃРєР°Р·Р°РЅРёР№.\n",
+        "\n",
+        "    РџР°СЂР°РјРµС‚СЂС‹:\n",
+        "    predictions (Tensor): Р’С‹С…РѕРґРЅС‹Рµ РґР°РЅРЅС‹Рµ РјРѕРґРµР»Рё.\n",
+        "    targets (Tensor): РСЃС‚РёРЅРЅС‹Рµ РјРµС‚РєРё (ground truth).\n",
+        "    topk (tuple, optional): РљРѕСЂС‚РµР¶, СѓРєР°Р·С‹РІР°СЋС‰РёР№ top-k РїСЂРµРґСЃРєР°Р·Р°РЅРёСЏ РґР»СЏ РѕС†РµРЅРєРё С‚РѕС‡РЅРѕСЃС‚Рё.\n",
+        "\n",
+        "    Р’РѕР·РІСЂР°С‰Р°РµС‚:\n",
+        "    list: РЎРїРёСЃРѕРє Р·РЅР°С‡РµРЅРёР№ С‚РѕС‡РЅРѕСЃС‚Рё РґР»СЏ РєР°Р¶РґРѕРіРѕ k РёР· topk.\n",
+        "    \"\"\"\n",
+        "\n",
+        "    # Get the top k predictions and their indices\n",
+        "    # РџРѕР»СѓС‡РµРЅРёРµ top-k РїСЂРµРґСЃРєР°Р·Р°РЅРёР№ Рё РёС… РёРЅРґРµРєСЃРѕРІ\n",
+        "    top_k_preds_indices = predictions.topk(max(topk), dim=1, largest=True, sorted=True)[1].t()\n",
+        "    print(f\"top_k_preds_indices.shape {top_k_preds_indices.shape}\")\n",
+        "    print(f\"top_k_preds_indices {top_k_preds_indices}\")\n",
+        "\n",
+        "    # Compare the top k predictions with the targets to see if they are correct\n",
+        "    # РЎСЂР°РІРЅРµРЅРёРµ top-k РїСЂРµРґСЃРєР°Р·Р°РЅРёР№ СЃ РёСЃС‚РёРЅРЅС‹РјРё РјРµС‚РєР°РјРё, С‡С‚РѕР±С‹ РїСЂРѕРІРµСЂРёС‚СЊ РёС… РїСЂР°РІРёР»СЊРЅРѕСЃС‚СЊ\n",
+        "    correct_comparisons = top_k_preds_indices.eq(targets.view(1, -1).expand_as(top_k_preds_indices))\n",
+        "\n",
+        "    accuracies = []\n",
+        "    for k in topk:\n",
+        "        # Get the number of correct predictions in the top k\n",
+        "        # РџРѕР»СѓС‡РµРЅРёРµ РєРѕР»РёС‡РµСЃС‚РІР° РїСЂР°РІРёР»СЊРЅС‹С… РїСЂРµРґСЃРєР°Р·Р°РЅРёР№ РІ top-k\n",
+        "        correct_in_top_k = correct_comparisons[:k].reshape(-1).float().sum(0, keepdim=True)\n",
+        "\n",
+        "        # Move the correct count to cpu, convert to numpy and then to native Python float\n",
+        "        # РџРµСЂРµРЅРѕСЃ РЅР° CPU, РєРѕРЅРІРµСЂС‚Р°С†РёСЏ РІ numpy, Р·Р°С‚РµРј РІ СЃС‚Р°РЅРґР°СЂС‚РЅС‹Р№ Python float\n",
+        "        accuracies.append(float(correct_in_top_k.cpu().numpy()))\n",
+        "\n",
+        "    return accuracies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def visualize_predictions(images, actual_labels, predicted_labels, class_names, num_samples=1):\n",
+        "    mu, sigma = torch.tensor((0.48145466, 0.4578275, 0.40821073)), torch.tensor((0.26862954, 0.26130258, 0.27577711))\n",
+        "    mu_, sigma_ = -mu / sigma, 1 / sigma\n",
+        "    images = transforms.Normalize(mu_, sigma_)(images)\n",
+        "    for i in range(num_samples):\n",
+        "        plt.figure(figsize=(4, 4))\n",
+        "        plt.imshow(np.transpose(images[i].cpu().numpy(), (1, 2, 0)))\n",
+        "        plt.title(f\"Actual: {class_names[actual_labels[i]]}\\nPredicted: {class_names[predicted_labels[i]]}\")\n",
+        "        plt.axis('off')\n",
+        "        plt.show()"
+      ],
+      "metadata": {
+        "id": "IxwOF0njc_ZX"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The function iterates through batches of data from the DataLoader.\n",
+        "\n",
+        "For each batch we do the following:\n",
+        "\n",
+        "* The image data in the batch is moved to the GPU for processing, and the corresponding true labels are also moved to the GPU.\n",
+        "* Image features are extracted from the model by encoding the image batch and normalizing them.\n",
+        "* Logits are calculated based on the image features and the provided zeroshot_weights.\n",
+        "* The top-1 prediction (the class with the highest probability) is determined from the logits.\n",
+        "* Call visualize_predictions to display the actual image, its true label, and the predicted label.\n",
+        "* Calculate top-1 and top-5 accuracies.\n",
+        "\n",
+        "After processing all batches, the function calculates the overall top-1 and top-5 accuracies by dividing the accumulated top-1 and top-5 correct predictions by the total number of samples.\n",
+        "\n",
+        "Р¤СѓРЅРєС†РёСЏ РїСЂРѕС…РѕРґРёС‚ РїРѕ РїР°РєРµС‚Р°Рј РґР°РЅРЅС‹С… РёР· `DataLoader`.\n",
+        "\n",
+        "Р”Р»СЏ РєР°Р¶РґРѕРіРѕ РїР°РєРµС‚Р° РІС‹РїРѕР»РЅСЏСЋС‚СЃСЏ СЃР»РµРґСѓСЋС‰РёРµ С€Р°РіРё:\n",
+        "\n",
+        "* Р”Р°РЅРЅС‹Рµ РёР·РѕР±СЂР°Р¶РµРЅРёР№ РёР· РїР°РєРµС‚Р° РїРµСЂРµРјРµС‰Р°СЋС‚СЃСЏ РЅР° GPU РґР»СЏ РѕР±СЂР°Р±РѕС‚РєРё, Р° СЃРѕРѕС‚РІРµС‚СЃС‚РІСѓСЋС‰РёРµ РёСЃС‚РёРЅРЅС‹Рµ РјРµС‚РєРё С‚Р°РєР¶Рµ РїРµСЂРµРЅРѕСЃСЏС‚СЃСЏ РЅР° GPU.\n",
+        "* РР·РѕР±СЂР°Р¶РµРЅРёСЏ РєРѕРґРёСЂСѓСЋС‚СЃСЏ РјРѕРґРµР»СЊСЋ РґР»СЏ РёР·РІР»РµС‡РµРЅРёСЏ РїСЂРёР·РЅР°РєРѕРІ, РєРѕС‚РѕСЂС‹Рµ Р·Р°С‚РµРј РЅРѕСЂРјР°Р»РёР·СѓСЋС‚СЃСЏ.\n",
+        "* Р›РѕРіРёС‚С‹ РІС‹С‡РёСЃР»СЏСЋС‚СЃСЏ РЅР° РѕСЃРЅРѕРІРµ РїСЂРёР·РЅР°РєРѕРІ РёР·РѕР±СЂР°Р¶РµРЅРёР№ Рё РїСЂРµРґРѕСЃС‚Р°РІР»РµРЅРЅС‹С… РІРµСЃРѕРІ zero-shot РєР»Р°СЃСЃРёС„РёРєР°С†РёРё.\n",
+        "* РћРїСЂРµРґРµР»СЏРµС‚СЃСЏ РїСЂРµРґСЃРєР°Р·Р°РЅРёРµ top-1 (РєР»Р°СЃСЃ СЃ РЅР°РёРІС‹СЃС€РµР№ РІРµСЂРѕСЏС‚РЅРѕСЃС‚СЊСЋ) РёР· Р»РѕРіРёС‚РѕРІ.\n",
+        "* Р’С‹Р·С‹РІР°РµС‚СЃСЏ С„СѓРЅРєС†РёСЏ `visualize_predictions` РґР»СЏ РѕС‚РѕР±СЂР°Р¶РµРЅРёСЏ С„Р°РєС‚РёС‡РµСЃРєРѕРіРѕ РёР·РѕР±СЂР°Р¶РµРЅРёСЏ, РµРіРѕ РёСЃС‚РёРЅРЅРѕР№ РјРµС‚РєРё Рё РїСЂРµРґСЃРєР°Р·Р°РЅРЅРѕРіРѕ РєР»Р°СЃСЃР°.\n",
+        "* Р Р°СЃСЃС‡РёС‚С‹РІР°СЋС‚СЃСЏ С‚РѕС‡РЅРѕСЃС‚Рё РґР»СЏ top-1 Рё top-5 РїСЂРµРґСЃРєР°Р·Р°РЅРёР№.\n",
+        "\n",
+        "РџРѕСЃР»Рµ РѕР±СЂР°Р±РѕС‚РєРё РІСЃРµС… РїР°РєРµС‚РѕРІ С„СѓРЅРєС†РёСЏ РІС‹С‡РёСЃР»СЏРµС‚ РѕР±С‰СѓСЋ С‚РѕС‡РЅРѕСЃС‚СЊ top-1 Рё top-5, РґРµР»СЏ СЃСѓРјРјР°СЂРЅРѕРµ РєРѕР»РёС‡РµСЃС‚РІРѕ РїСЂР°РІРёР»СЊРЅС‹С… РїСЂРµРґСЃРєР°Р·Р°РЅРёР№ РЅР° РѕР±С‰РµРµ С‡РёСЃР»Рѕ РѕР±СЂР°Р·С†РѕРІ.\n"
+      ],
+      "metadata": {
+        "id": "_x7v36LuTcmg"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "wKJ7YsdlkDXo",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "73becef2-a8e7-4b5c-fa94-e9d1041a6f27"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tensor([3, 8, 8, 0, 6, 6, 1, 6, 3, 1, 0, 9, 5, 7, 9, 8, 5, 7, 8, 6, 7, 0, 4, 9,\n",
+            "        5, 2, 4, 0, 9, 6, 6, 5], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[17.9531, 18.5938, 19.7031, 21.1562, 19.2812, 20.5156, 19.2969, 18.9062,\n",
+            "         19.6094, 17.3125],\n",
+            "        [21.4062, 22.2812, 20.0625, 18.8281, 17.4688, 18.7031, 17.4844, 18.6094,\n",
+            "         27.2656, 20.1719],\n",
+            "        [21.5469, 22.9688, 20.0469, 20.1250, 19.4062, 19.8906, 17.9688, 18.9062,\n",
+            "         24.6719, 21.7500],\n",
+            "        [23.8750, 20.4844, 23.6719, 18.1250, 16.9844, 17.7812, 18.9531, 19.4844,\n",
+            "         20.2344, 18.3281],\n",
+            "        [16.9375, 19.9062, 22.9531, 20.7500, 20.0156, 20.7656, 29.5000, 18.2656,\n",
+            "         18.5312, 17.0156],\n",
+            "        [18.7031, 20.5938, 22.2812, 25.6250, 22.1406, 25.6250, 23.9531, 21.0938,\n",
+            "         19.1875, 19.2969],\n",
+            "        [19.1719, 26.7500, 19.4844, 18.4844, 17.4688, 19.3750, 18.6250, 18.4531,\n",
+            "         20.1094, 21.2969],\n",
+            "        [20.3906, 19.3906, 23.7500, 21.5469, 19.3281, 22.1406, 26.3281, 20.7500,\n",
+            "         19.9219, 19.6406],\n",
+            "        [17.8906, 18.8125, 19.8125, 25.7969, 18.6562, 22.0781, 18.8594, 19.7969,\n",
+            "         18.2188, 17.4531],\n",
+            "        [18.1875, 25.1250, 19.5469, 19.2812, 17.8281, 20.0938, 19.9062, 18.8125,\n",
+            "         17.9062, 24.5625],\n",
+            "        [26.4844, 19.9844, 24.5000, 20.0781, 19.6094, 20.7656, 21.1094, 20.0938,\n",
+            "         21.7344, 18.7812],\n",
+            "        [17.1094, 22.2656, 18.2031, 17.8125, 18.2969, 19.3906, 16.4688, 19.6719,\n",
+            "         19.0469, 24.6719],\n",
+            "        [17.9688, 19.5625, 20.1094, 23.7969, 20.1250, 25.5469, 20.3438, 21.3281,\n",
+            "         19.2812, 19.1406],\n",
+            "        [18.9375, 20.5938, 20.9688, 21.2031, 20.8281, 21.5625, 17.1250, 28.7188,\n",
+            "         18.9688, 18.6875],\n",
+            "        [18.5938, 22.7812, 19.9688, 19.2812, 18.3906, 19.3438, 17.3750, 19.6094,\n",
+            "         20.6719, 26.7969],\n",
+            "        [22.5000, 20.8750, 20.4844, 20.0469, 19.5781, 20.5000, 21.2812, 20.6250,\n",
+            "         24.9219, 18.7812],\n",
+            "        [19.8750, 19.3906, 20.6875, 21.5625, 19.5000, 26.5000, 21.0000, 21.3125,\n",
+            "         19.4375, 19.2656],\n",
+            "        [20.2031, 20.4688, 21.0469, 19.7344, 21.4531, 21.3281, 19.4688, 26.4688,\n",
+            "         19.5625, 19.7656],\n",
+            "        [18.0000, 19.6875, 19.2344, 18.0781, 18.0312, 18.6094, 16.6094, 18.7500,\n",
+            "         25.1719, 17.6875],\n",
+            "        [20.4375, 22.1250, 25.7500, 23.1875, 22.5938, 23.0469, 28.1562, 22.8594,\n",
+            "         21.4375, 20.3906],\n",
+            "        [22.2344, 23.3125, 22.6875, 23.0469, 22.7812, 23.7969, 20.4062, 28.4062,\n",
+            "         21.5000, 22.9844],\n",
+            "        [24.2969, 20.6562, 23.9375, 20.3281, 20.2031, 20.1094, 21.7344, 21.2031,\n",
+            "         21.7031, 19.5000],\n",
+            "        [20.8750, 19.2031, 22.1094, 20.1094, 29.3438, 21.5625, 20.0312, 22.7969,\n",
+            "         20.1094, 19.0938],\n",
+            "        [18.4688, 22.7031, 18.8750, 17.9688, 17.3438, 18.5000, 18.5938, 19.2188,\n",
+            "         20.4219, 23.0469],\n",
+            "        [18.2969, 19.1250, 20.7344, 21.2500, 22.4375, 27.2031, 18.2969, 22.6875,\n",
+            "         18.4062, 19.4844],\n",
+            "        [21.0156, 20.7812, 27.5469, 21.6875, 22.8438, 23.0156, 21.3281, 23.4375,\n",
+            "         20.5781, 21.4531],\n",
+            "        [20.8438, 20.5312, 21.8281, 20.4062, 25.0938, 21.2656, 20.6094, 23.5469,\n",
+            "         21.6406, 21.0000],\n",
+            "        [26.4375, 21.5625, 24.4688, 21.0625, 21.9375, 22.6250, 24.6875, 22.2969,\n",
+            "         23.8438, 21.6094],\n",
+            "        [17.9062, 24.0625, 19.2031, 18.5469, 17.4062, 19.0156, 18.4062, 18.2500,\n",
+            "         18.8281, 26.3125],\n",
+            "        [19.2969, 19.6562, 22.2500, 20.8281, 20.5625, 21.4375, 26.9375, 20.6406,\n",
+            "         19.6562, 17.9688],\n",
+            "        [20.6250, 22.1250, 22.3594, 22.0312, 20.3281, 21.8750, 22.8750, 21.5469,\n",
+            "         21.6719, 20.3281],\n",
+            "        [21.8594, 22.3125, 24.6250, 24.5312, 23.7969, 27.9531, 23.9219, 23.9531,\n",
+            "         21.8125, 21.6562]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[3],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [0],\n",
+            "        [6],\n",
+            "        [3],\n",
+            "        [1],\n",
+            "        [6],\n",
+            "        [3],\n",
+            "        [1],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [5],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [6],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [4],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [6],\n",
+            "        [6],\n",
+            "        [5]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[3, 8, 8, 0, 6, 3, 1, 6, 3, 1, 0, 9, 5, 7, 9, 8, 5, 7, 8, 6, 7, 0, 4, 9,\n",
+            "         5, 2, 4, 0, 9, 6, 6, 5],\n",
+            "        [5, 1, 1, 2, 2, 5, 9, 2, 5, 9, 2, 1, 3, 5, 1, 0, 3, 4, 1, 2, 5, 2, 7, 1,\n",
+            "         7, 7, 7, 6, 1, 2, 2, 2],\n",
+            "        [2, 0, 9, 1, 5, 6, 8, 5, 2, 5, 8, 7, 7, 3, 8, 6, 7, 5, 2, 3, 1, 6, 2, 8,\n",
+            "         4, 5, 2, 2, 2, 5, 1, 3],\n",
+            "        [8, 9, 0, 8, 3, 2, 2, 3, 7, 6, 6, 5, 6, 2, 2, 1, 6, 2, 7, 5, 3, 8, 5, 7,\n",
+            "         3, 4, 8, 8, 5, 3, 3, 7],\n",
+            "        [6, 2, 3, 7, 4, 4, 5, 7, 6, 2, 5, 8, 4, 4, 7, 7, 2, 1, 5, 7, 9, 7, 0, 2,\n",
+            "         2, 3, 5, 5, 8, 7, 5, 6]], device='cuda:0')\n",
+            "tensor([4, 5, 9, 2, 4, 1, 9, 5, 4, 6, 5, 6, 0, 9, 3, 9, 7, 6, 9, 8, 0, 3, 8, 8,\n",
+            "        7, 7, 4, 6, 7, 3, 6, 3], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[19.1562, 21.8281, 25.3438, 25.0781, 29.8594, 24.3594, 23.5000, 23.4844,\n",
+            "         20.0625, 20.8438],\n",
+            "        [19.2500, 19.3594, 20.3281, 22.7500, 24.5469, 26.7969, 19.2656, 23.1875,\n",
+            "         19.2031, 19.1875],\n",
+            "        [19.7500, 22.5469, 18.7500, 18.0625, 16.9531, 18.4688, 17.0781, 19.1719,\n",
+            "         21.3125, 26.8438],\n",
+            "        [19.4375, 19.0469, 24.5938, 22.0312, 23.6406, 22.3906, 22.6562, 21.6406,\n",
+            "         19.1719, 19.2031],\n",
+            "        [18.1875, 18.5000, 22.3125, 20.2500, 29.5156, 22.3906, 17.6094, 26.2500,\n",
+            "         17.5469, 17.8750],\n",
+            "        [21.5000, 28.5938, 20.4688, 21.2031, 18.9531, 21.5625, 20.3438, 20.7656,\n",
+            "         22.1562, 26.7344],\n",
+            "        [18.2656, 22.0625, 18.0781, 17.5938, 17.3125, 18.0781, 18.1875, 18.8594,\n",
+            "         20.4062, 22.6094],\n",
+            "        [21.0469, 19.7188, 21.0469, 21.1562, 16.8125, 26.5781, 20.3438, 19.7656,\n",
+            "         19.3594, 18.3438],\n",
+            "        [22.9531, 21.4531, 22.5625, 20.9375, 23.4219, 21.5312, 22.5312, 23.4062,\n",
+            "         21.1875, 20.6562],\n",
+            "        [20.6094, 19.8750, 23.4688, 21.7188, 22.2188, 21.8281, 26.4688, 21.5469,\n",
+            "         20.7188, 19.2188],\n",
+            "        [19.4531, 21.3594, 23.1562, 22.8125, 18.2969, 22.3906, 20.7031, 21.6406,\n",
+            "         20.3906, 18.5156],\n",
+            "        [19.8906, 21.6406, 23.3125, 23.2969, 21.5625, 23.1094, 26.0156, 21.5625,\n",
+            "         20.0156, 20.3125],\n",
+            "        [27.2031, 21.2656, 23.4062, 20.0312, 19.8281, 19.7031, 19.0312, 20.3438,\n",
+            "         21.2969, 20.7812],\n",
+            "        [19.3125, 23.6875, 20.1406, 18.9219, 19.2500, 19.4844, 18.0312, 20.1250,\n",
+            "         20.1562, 27.9219],\n",
+            "        [18.7031, 19.8125, 23.9062, 27.6094, 24.1562, 25.5938, 23.0469, 22.7344,\n",
+            "         19.6250, 19.2656],\n",
+            "        [19.4844, 20.9219, 19.0781, 18.5469, 17.2969, 18.9531, 18.8125, 17.9062,\n",
+            "         22.7812, 21.5000],\n",
+            "        [18.1719, 19.1719, 21.1406, 20.2969, 21.7188, 21.9375, 16.9062, 28.8906,\n",
+            "         17.6406, 18.4844],\n",
+            "        [21.9375, 22.2188, 24.5938, 21.5938, 21.2969, 22.9375, 26.9531, 22.0625,\n",
+            "         21.2656, 20.6094],\n",
+            "        [20.0000, 21.2969, 20.3438, 18.4688, 17.5312, 18.3438, 17.3281, 18.6094,\n",
+            "         20.8125, 25.6562],\n",
+            "        [19.9688, 20.3125, 20.2031, 19.0469, 18.9688, 19.1875, 18.3125, 20.0312,\n",
+            "         27.0000, 20.6094],\n",
+            "        [24.7344, 21.8906, 23.7812, 21.2188, 21.3750, 21.7344, 21.9844, 22.4375,\n",
+            "         22.3750, 20.5469],\n",
+            "        [18.0156, 17.8750, 20.0156, 24.0312, 20.8438, 23.1250, 19.6875, 20.9844,\n",
+            "         17.7344, 17.5781],\n",
+            "        [19.4531, 18.3438, 19.3594, 16.5938, 16.5312, 17.4062, 16.7969, 17.6094,\n",
+            "         25.5469, 17.2031],\n",
+            "        [21.7969, 19.7500, 22.9219, 20.1562, 19.2344, 19.9844, 19.2500, 20.3281,\n",
+            "         24.9219, 17.2656],\n",
+            "        [17.7812, 20.0000, 19.6406, 20.0938, 20.5938, 21.5469, 16.1719, 27.8906,\n",
+            "         17.8750, 18.5000],\n",
+            "        [21.5469, 20.2500, 21.4062, 21.4531, 22.5000, 22.8750, 19.5781, 27.0781,\n",
+            "         20.4688, 19.8438],\n",
+            "        [20.4531, 20.6250, 22.6250, 23.3750, 23.6250, 25.0312, 23.4688, 22.4219,\n",
+            "         21.2812, 19.5781],\n",
+            "        [20.9219, 20.2188, 21.6562, 22.8750, 23.8438, 23.5469, 21.6562, 22.4688,\n",
+            "         21.0781, 19.5781],\n",
+            "        [19.8438, 20.3750, 22.3125, 21.2969, 23.5781, 22.5625, 19.0312, 29.4844,\n",
+            "         19.8906, 19.7500],\n",
+            "        [20.8438, 20.7969, 23.1875, 28.0625, 22.0312, 26.6562, 21.6406, 23.6875,\n",
+            "         21.0312, 20.2188],\n",
+            "        [22.0156, 22.0000, 24.7031, 24.6250, 23.0938, 23.5625, 25.9219, 22.4531,\n",
+            "         21.8125, 21.5156],\n",
+            "        [21.3281, 21.9531, 23.4844, 26.2656, 22.1562, 24.3906, 23.3906, 21.9375,\n",
+            "         22.2031, 20.1719]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[4],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [1],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [4],\n",
+            "        [6],\n",
+            "        [2],\n",
+            "        [6],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [0],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [5],\n",
+            "        [4],\n",
+            "        [7],\n",
+            "        [3],\n",
+            "        [6],\n",
+            "        [3]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-9-b53ed4668e3a>:43: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  accuracies.append(float(correct_in_top_k.cpu().numpy()))\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[4, 5, 9, 2, 4, 1, 9, 5, 4, 6, 2, 6, 0, 9, 3, 8, 7, 6, 9, 8, 0, 3, 8, 8,\n",
+            "         7, 7, 5, 4, 7, 3, 6, 3],\n",
+            "        [2, 4, 1, 4, 7, 9, 1, 3, 7, 2, 3, 2, 2, 1, 5, 9, 5, 2, 1, 9, 2, 5, 0, 2,\n",
+            "         5, 5, 4, 5, 4, 5, 2, 5],\n",
+            "        [3, 7, 8, 6, 5, 8, 8, 2, 0, 4, 5, 3, 8, 8, 4, 1, 4, 5, 8, 1, 7, 7, 2, 0,\n",
+            "         4, 4, 6, 3, 5, 7, 3, 2],\n",
+            "        [5, 3, 0, 5, 2, 5, 7, 0, 2, 5, 7, 5, 1, 2, 2, 0, 2, 1, 2, 2, 8, 4, 1, 7,\n",
+            "         3, 0, 3, 7, 2, 2, 5, 6],\n",
+            "        [6, 2, 7, 3, 3, 0, 0, 6, 6, 3, 1, 1, 9, 7, 6, 2, 3, 7, 0, 7, 6, 2, 7, 3,\n",
+            "         1, 3, 2, 2, 3, 4, 4, 8]], device='cuda:0')\n",
+            "tensor([6, 2, 1, 2, 3, 7, 2, 6, 8, 8, 0, 2, 9, 3, 3, 8, 8, 1, 1, 7, 2, 5, 2, 7,\n",
+            "        8, 9, 0, 3, 8, 6, 4, 6], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[20.1406, 20.5000, 23.9062, 22.9688, 21.1250, 23.2812, 26.3438, 21.5312,\n",
+            "         20.0312, 19.4219],\n",
+            "        [17.7031, 17.7031, 27.8438, 19.7500, 18.5625, 19.1250, 19.9688, 17.9219,\n",
+            "         17.3438, 16.2188],\n",
+            "        [19.5781, 26.7031, 19.9375, 19.5312, 20.6719, 20.1406, 19.4062, 19.8125,\n",
+            "         20.7188, 23.5156],\n",
+            "        [22.5625, 19.7812, 29.4062, 20.6875, 19.9844, 21.1875, 22.2812, 20.0938,\n",
+            "         20.8438, 19.5781],\n",
+            "        [19.2969, 19.6562, 20.4844, 21.2344, 20.0625, 21.3438, 20.7969, 19.9688,\n",
+            "         20.7031, 18.5781],\n",
+            "        [19.3750, 20.3125, 21.0781, 20.0781, 25.4219, 21.2344, 15.5312, 28.0781,\n",
+            "         18.6406, 19.5312],\n",
+            "        [22.7969, 19.9219, 27.2344, 21.9062, 21.5938, 22.6094, 21.9688, 21.0781,\n",
+            "         20.5781, 19.7188],\n",
+            "        [18.9219, 21.2969, 23.2500, 23.2500, 21.6562, 23.8750, 28.1406, 20.3906,\n",
+            "         20.4219, 20.0000],\n",
+            "        [21.3750, 20.8906, 22.5000, 20.1406, 21.5938, 20.7031, 20.3281, 21.5312,\n",
+            "         28.0625, 22.0469],\n",
+            "        [20.5000, 19.6875, 20.1250, 17.6094, 17.3906, 18.0000, 16.4531, 18.8281,\n",
+            "         26.1094, 17.8594],\n",
+            "        [22.7500, 22.0312, 20.5312, 18.9062, 17.6250, 19.5625, 19.5156, 20.2031,\n",
+            "         22.4688, 24.1250],\n",
+            "        [20.3750, 19.0938, 27.7500, 20.5312, 20.0469, 19.8281, 20.5781, 20.1250,\n",
+            "         18.1875, 18.4219],\n",
+            "        [21.5469, 23.3750, 20.0000, 18.9844, 19.6406, 19.1250, 19.7812, 20.7969,\n",
+            "         23.0469, 25.6250],\n",
+            "        [18.9531, 20.4375, 21.4375, 26.6719, 18.7031, 22.3906, 20.9844, 20.8125,\n",
+            "         20.3438, 19.2500],\n",
+            "        [20.9375, 20.6562, 23.3750, 26.6250, 22.4844, 25.8906, 20.0781, 25.5625,\n",
+            "         21.5156, 20.3594],\n",
+            "        [22.2188, 22.6719, 20.5469, 20.8281, 19.0312, 19.9375, 17.9844, 20.2969,\n",
+            "         28.8594, 20.0000],\n",
+            "        [21.7188, 22.0156, 21.5938, 20.7969, 20.5938, 19.7500, 18.0312, 21.3125,\n",
+            "         29.9219, 20.7344],\n",
+            "        [22.3594, 25.7656, 19.4219, 18.6406, 18.4062, 19.3594, 19.6719, 19.5312,\n",
+            "         23.9688, 23.1250],\n",
+            "        [17.9844, 27.0938, 20.8906, 19.7031, 18.8438, 20.8750, 19.9688, 18.2031,\n",
+            "         18.6719, 20.9844],\n",
+            "        [22.5156, 22.4219, 22.8906, 21.6719, 23.4219, 21.6875, 20.8750, 26.3750,\n",
+            "         22.7656, 20.4219],\n",
+            "        [19.6094, 19.8281, 23.6406, 23.0312, 21.9375, 24.0781, 23.1406, 24.2344,\n",
+            "         20.1562, 19.9531],\n",
+            "        [19.9844, 20.2188, 21.8750, 22.1094, 22.0469, 23.7344, 20.6406, 24.5312,\n",
+            "         19.1562, 20.4844],\n",
+            "        [20.9844, 20.2812, 22.7031, 21.4688, 21.2031, 21.3281, 22.2812, 22.5469,\n",
+            "         20.9688, 19.7188],\n",
+            "        [21.3125, 21.3906, 22.3125, 23.2656, 23.7656, 24.3750, 20.6250, 28.6094,\n",
+            "         20.5938, 20.7500],\n",
+            "        [20.0312, 19.3906, 19.9688, 18.1719, 18.2500, 19.4062, 17.0156, 19.9219,\n",
+            "         26.0312, 19.1875],\n",
+            "        [20.1406, 22.2188, 19.5156, 18.1719, 18.7812, 19.4531, 18.5938, 20.4688,\n",
+            "         20.0938, 24.6562],\n",
+            "        [27.6562, 21.1406, 23.7969, 20.9062, 20.4688, 20.4219, 17.9062, 20.3125,\n",
+            "         24.0156, 21.1406],\n",
+            "        [19.6406, 20.5312, 23.0781, 27.4844, 21.4062, 23.1719, 22.6875, 22.3750,\n",
+            "         20.2344, 19.2188],\n",
+            "        [18.2344, 18.3906, 18.5312, 17.3281, 16.8281, 17.1875, 14.8203, 17.5938,\n",
+            "         25.2344, 18.3125],\n",
+            "        [18.2031, 19.2656, 21.5312, 20.1875, 18.1562, 21.0625, 28.1250, 19.3281,\n",
+            "         18.7656, 18.6094],\n",
+            "        [19.2031, 19.0781, 22.5938, 20.7031, 30.5781, 22.1562, 19.1406, 22.9531,\n",
+            "         17.9688, 18.0000],\n",
+            "        [19.1250, 19.7969, 21.8281, 20.9062, 23.2031, 21.3281, 26.0781, 21.7188,\n",
+            "         20.5312, 18.9688]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[6],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [5],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [6],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [1],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [6],\n",
+            "        [4],\n",
+            "        [6]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[6, 2, 1, 2, 5, 7, 2, 6, 8, 8, 9, 2, 9, 3, 3, 8, 8, 1, 1, 7, 7, 7, 2, 7,\n",
+            "         8, 9, 0, 3, 8, 6, 4, 6],\n",
+            "        [2, 6, 9, 0, 3, 4, 0, 5, 2, 0, 0, 6, 1, 5, 5, 1, 1, 8, 9, 4, 5, 5, 7, 5,\n",
+            "         0, 1, 8, 5, 2, 2, 7, 4],\n",
+            "        [5, 3, 8, 6, 6, 5, 5, 3, 9, 2, 8, 3, 8, 2, 7, 0, 0, 9, 2, 2, 2, 3, 6, 4,\n",
+            "         2, 7, 2, 2, 1, 5, 2, 2],\n",
+            "        [3, 5, 4, 5, 8, 2, 6, 2, 4, 1, 1, 0, 0, 6, 2, 3, 2, 0, 5, 8, 6, 4, 3, 3,\n",
+            "         7, 0, 9, 6, 9, 3, 5, 7],\n",
+            "        [7, 4, 5, 8, 2, 1, 3, 4, 7, 7, 2, 7, 7, 7, 4, 2, 7, 6, 6, 0, 3, 2, 5, 2,\n",
+            "         5, 8, 1, 7, 0, 7, 3, 5]], device='cuda:0')\n",
+            "tensor([6, 0, 0, 7, 4, 5, 6, 3, 1, 1, 3, 6, 8, 7, 4, 0, 6, 2, 1, 3, 0, 4, 2, 7,\n",
+            "        8, 3, 1, 2, 8, 0, 8, 3], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[18.9844, 20.2031, 24.8594, 21.1719, 21.4375, 21.8438, 29.8594, 19.5312,\n",
+            "         19.1250, 18.0156],\n",
+            "        [27.5625, 21.8750, 25.3438, 20.3125, 21.9844, 21.0312, 22.6875, 21.6250,\n",
+            "         23.3125, 20.8594],\n",
+            "        [26.4219, 20.0312, 21.9062, 17.2656, 18.7969, 18.4531, 19.0312, 18.0938,\n",
+            "         21.5625, 18.5781],\n",
+            "        [19.6875, 19.7344, 21.2812, 20.3125, 22.4531, 21.6094, 18.0156, 28.3125,\n",
+            "         18.9219, 19.8125],\n",
+            "        [19.7812, 19.9062, 23.4062, 20.4844, 29.2188, 21.9375, 20.2969, 23.7656,\n",
+            "         19.0781, 18.9531],\n",
+            "        [19.1094, 20.1562, 22.5312, 22.0469, 20.9844, 26.4219, 23.6406, 23.2656,\n",
+            "         19.6094, 19.8906],\n",
+            "        [17.4844, 18.7969, 20.8750, 19.8438, 18.2500, 20.0781, 28.8594, 18.5312,\n",
+            "         18.4062, 18.1406],\n",
+            "        [19.3125, 19.5000, 22.7188, 25.4531, 22.8281, 23.4531, 22.1719, 22.3438,\n",
+            "         20.4688, 19.0625],\n",
+            "        [20.1406, 27.7188, 21.7656, 20.6562, 20.4531, 21.2188, 20.3125, 20.4375,\n",
+            "         21.4375, 24.1406],\n",
+            "        [19.3281, 25.7812, 20.5781, 18.5312, 18.5625, 18.9688, 19.0156, 19.0781,\n",
+            "         19.8438, 22.1250],\n",
+            "        [18.9688, 19.0781, 22.0312, 26.3438, 21.6562, 23.5312, 20.3594, 20.0781,\n",
+            "         19.5469, 16.6406],\n",
+            "        [18.5625, 18.8125, 20.8750, 19.9688, 19.4688, 20.0312, 21.0938, 19.6719,\n",
+            "         18.6406, 18.1250],\n",
+            "        [20.4062, 20.2188, 20.3750, 19.1719, 17.1719, 19.0312, 17.9375, 19.5312,\n",
+            "         25.8594, 17.7656],\n",
+            "        [19.7656, 20.3906, 21.0469, 21.6406, 21.2188, 21.7656, 17.8125, 28.2031,\n",
+            "         19.8750, 18.4531],\n",
+            "        [19.2812, 19.5469, 22.2812, 22.8438, 27.2344, 24.1406, 18.5938, 26.2344,\n",
+            "         19.2188, 19.0625],\n",
+            "        [25.8281, 20.8281, 24.2500, 20.9688, 20.0781, 20.5938, 20.9844, 20.7812,\n",
+            "         23.5781, 19.3750],\n",
+            "        [20.9844, 21.7031, 22.9688, 21.0312, 21.1250, 21.4531, 25.1719, 21.0781,\n",
+            "         22.2344, 20.9844],\n",
+            "        [21.5938, 19.4531, 26.3125, 21.1875, 20.6250, 20.9219, 23.3906, 21.3906,\n",
+            "         21.5938, 20.6406],\n",
+            "        [21.0938, 26.1875, 20.7188, 19.4688, 18.8594, 20.4844, 20.5625, 19.2344,\n",
+            "         21.0000, 23.0312],\n",
+            "        [18.9375, 20.7969, 21.5938, 27.2969, 18.6719, 22.9375, 20.8125, 20.7344,\n",
+            "         20.4219, 19.0625],\n",
+            "        [26.5000, 19.9062, 23.2031, 18.5312, 19.5938, 18.8594, 18.9219, 19.5156,\n",
+            "         21.0469, 19.2969],\n",
+            "        [20.0000, 20.2656, 21.6875, 19.4375, 27.9062, 21.2656, 19.2344, 24.7500,\n",
+            "         20.5781, 21.1719],\n",
+            "        [22.7188, 22.4375, 27.8594, 23.2812, 22.5625, 23.7188, 24.7188, 23.6875,\n",
+            "         23.4531, 22.3906],\n",
+            "        [18.8594, 18.7969, 21.4531, 20.0625, 20.5156, 20.2500, 19.6875, 22.6875,\n",
+            "         18.7344, 19.2500],\n",
+            "        [23.6875, 22.7500, 21.8594, 20.6875, 20.0000, 21.3125, 19.9219, 20.8125,\n",
+            "         28.1562, 20.3281],\n",
+            "        [22.2656, 22.2812, 23.5938, 28.7500, 21.8594, 25.0312, 23.2344, 23.5312,\n",
+            "         22.2500, 21.0156],\n",
+            "        [20.6875, 27.8750, 21.9844, 20.7188, 20.4375, 21.3594, 20.7500, 20.6562,\n",
+            "         21.4844, 23.7344],\n",
+            "        [19.7031, 19.5156, 28.1719, 19.2500, 22.0625, 20.5000, 20.5938, 21.2344,\n",
+            "         19.3594, 18.7344],\n",
+            "        [21.0938, 20.6094, 20.6094, 18.8906, 18.0156, 19.5312, 18.3125, 19.5625,\n",
+            "         28.0781, 20.7500],\n",
+            "        [24.2188, 19.4531, 21.2344, 18.0312, 17.6875, 17.6562, 20.0000, 18.3906,\n",
+            "         24.6250, 18.4531],\n",
+            "        [20.1094, 20.0156, 21.0000, 19.2188, 19.2344, 19.2812, 16.9375, 19.6250,\n",
+            "         27.3750, 21.3281],\n",
+            "        [19.7969, 20.3438, 24.7500, 26.2188, 26.1094, 25.0625, 23.0312, 23.7656,\n",
+            "         19.8125, 19.3125]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[6],\n",
+            "        [0],\n",
+            "        [0],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [5],\n",
+            "        [6],\n",
+            "        [3],\n",
+            "        [1],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [6],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [6],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [0],\n",
+            "        [4],\n",
+            "        [2],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [3],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [3]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[6, 0, 0, 7, 4, 5, 6, 3, 1, 1, 3, 6, 8, 7, 4, 0, 6, 2, 1, 3, 0, 4, 2, 7,\n",
+            "         8, 3, 1, 2, 8, 8, 8, 3],\n",
+            "        [2, 2, 2, 4, 7, 6, 2, 5, 9, 9, 5, 2, 0, 5, 7, 2, 2, 6, 9, 5, 2, 7, 6, 2,\n",
+            "         0, 5, 9, 4, 0, 0, 9, 4],\n",
+            "        [5, 8, 8, 5, 2, 7, 5, 4, 2, 2, 2, 5, 2, 3, 5, 8, 8, 8, 0, 2, 8, 2, 5, 4,\n",
+            "         1, 2, 2, 7, 9, 2, 2, 5],\n",
+            "        [4, 6, 1, 2, 5, 2, 3, 2, 8, 8, 4, 3, 1, 4, 3, 6, 1, 0, 8, 6, 1, 5, 7, 5,\n",
+            "         2, 7, 8, 6, 2, 6, 0, 2],\n",
+            "        [3, 4, 6, 3, 3, 3, 1, 7, 5, 0, 6, 7, 7, 2, 2, 3, 5, 7, 2, 1, 4, 9, 8, 3,\n",
+            "         5, 6, 5, 5, 1, 1, 1, 7]], device='cuda:0')\n",
+            "tensor([5, 2, 4, 1, 8, 9, 1, 2, 9, 7, 2, 9, 6, 5, 6, 3, 8, 7, 6, 2, 5, 2, 8, 9,\n",
+            "        6, 0, 0, 5, 2, 9, 5, 4], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[20.0781, 19.8594, 22.4375, 26.5625, 21.3750, 25.9531, 23.1562, 21.6250,\n",
+            "         20.3125, 19.2812],\n",
+            "        [22.7500, 20.7031, 28.7031, 21.9531, 21.1094, 21.4062, 24.7656, 20.6250,\n",
+            "         21.1562, 20.8906],\n",
+            "        [18.9531, 19.4375, 22.8906, 22.3594, 28.7969, 23.9219, 20.1719, 24.7500,\n",
+            "         18.3906, 19.1875],\n",
+            "        [16.1250, 24.3594, 18.2188, 18.3594, 16.9375, 18.3750, 18.0781, 16.7656,\n",
+            "         18.4219, 18.6250],\n",
+            "        [18.6094, 18.4844, 17.9688, 16.2969, 16.5625, 17.6875, 15.3594, 16.5469,\n",
+            "         23.6562, 17.7188],\n",
+            "        [18.5000, 22.1719, 18.9688, 18.6094, 19.0938, 20.0938, 16.9688, 20.2500,\n",
+            "         19.5625, 25.7031],\n",
+            "        [21.1875, 25.8281, 20.3438, 19.5938, 18.3906, 19.5625, 21.3594, 19.5312,\n",
+            "         21.2500, 21.9688],\n",
+            "        [20.1406, 20.2188, 27.8281, 20.9375, 20.7500, 21.5938, 20.9375, 21.4219,\n",
+            "         19.4844, 19.2500],\n",
+            "        [19.0156, 23.3906, 19.2812, 18.6719, 18.4219, 18.7656, 18.8125, 19.7812,\n",
+            "         20.5625, 23.6094],\n",
+            "        [17.9531, 19.3438, 20.0625, 19.6562, 21.1562, 20.7344, 15.1797, 28.5000,\n",
+            "         18.4375, 17.8125],\n",
+            "        [20.5312, 20.0625, 28.2031, 21.9844, 21.6562, 21.7812, 23.4531, 20.3750,\n",
+            "         19.8281, 19.8125],\n",
+            "        [23.3906, 24.3750, 22.7031, 19.5000, 20.1094, 21.0625, 20.3125, 21.5312,\n",
+            "         23.3594, 26.9062],\n",
+            "        [20.1562, 21.5938, 23.9062, 26.5469, 24.0156, 24.5000, 25.1875, 22.0781,\n",
+            "         20.8438, 19.6719],\n",
+            "        [20.1562, 21.3281, 26.3281, 28.0469, 22.2969, 26.5156, 23.7812, 24.7344,\n",
+            "         20.8750, 19.6250],\n",
+            "        [18.1875, 20.6406, 23.8438, 21.9688, 18.4688, 23.3438, 30.4375, 18.3906,\n",
+            "         20.1250, 19.3438],\n",
+            "        [19.2031, 20.5625, 21.9844, 27.8594, 21.0625, 25.5469, 20.7188, 22.7656,\n",
+            "         19.9531, 17.8594],\n",
+            "        [21.0625, 20.9062, 22.1875, 20.7188, 19.3750, 20.9219, 20.6406, 20.0156,\n",
+            "         26.0938, 18.3281],\n",
+            "        [20.7344, 21.3750, 23.1875, 21.5156, 23.1562, 23.3594, 21.6094, 27.7812,\n",
+            "         21.3750, 20.9844],\n",
+            "        [16.9062, 18.3750, 21.7812, 21.7188, 20.4688, 21.0469, 24.6250, 20.1406,\n",
+            "         18.0469, 17.7656],\n",
+            "        [20.2500, 19.7656, 26.1250, 22.1562, 20.7500, 23.3750, 22.4844, 21.2500,\n",
+            "         19.6094, 19.4844],\n",
+            "        [20.5781, 20.6719, 21.0938, 23.5625, 21.8438, 25.1250, 23.8281, 23.5469,\n",
+            "         20.5000, 19.2500],\n",
+            "        [21.2188, 20.0781, 29.5469, 22.9375, 19.5156, 22.9688, 22.7500, 20.8906,\n",
+            "         19.7344, 19.6094],\n",
+            "        [25.6250, 24.2500, 22.6875, 22.1562, 20.5625, 22.2812, 22.9062, 22.0469,\n",
+            "         26.3906, 20.5156],\n",
+            "        [19.2031, 25.1094, 19.8594, 19.1719, 19.0938, 20.8125, 18.9062, 20.0312,\n",
+            "         20.0156, 26.8438],\n",
+            "        [20.5000, 19.0781, 20.6875, 20.6875, 19.9844, 20.8438, 21.6719, 20.6406,\n",
+            "         20.6406, 18.2656],\n",
+            "        [26.6875, 21.6406, 22.9375, 20.9688, 18.9219, 20.3438, 18.5938, 20.6875,\n",
+            "         22.8906, 18.8594],\n",
+            "        [27.2656, 21.5000, 24.5938, 20.2344, 20.7344, 21.2656, 22.1719, 21.1562,\n",
+            "         23.3750, 20.9062],\n",
+            "        [20.5469, 22.1250, 23.5625, 23.5625, 22.8438, 26.5938, 23.9375, 22.6875,\n",
+            "         21.4062, 21.8281],\n",
+            "        [21.5938, 19.0938, 27.7344, 22.0938, 20.8906, 21.3594, 21.6562, 20.3594,\n",
+            "         19.9688, 19.6875],\n",
+            "        [20.2500, 24.5469, 21.2812, 21.0312, 20.3594, 22.3281, 19.0781, 21.7656,\n",
+            "         20.3906, 27.2812],\n",
+            "        [20.3438, 20.1250, 22.5156, 22.7656, 20.7188, 24.5312, 20.7188, 22.5938,\n",
+            "         20.5938, 20.0625],\n",
+            "        [21.1094, 20.5000, 21.5625, 18.8750, 28.5312, 21.1562, 18.4219, 22.2969,\n",
+            "         19.1406, 20.3438]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[3],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [3],\n",
+            "        [6],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [2],\n",
+            "        [5],\n",
+            "        [2],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [6],\n",
+            "        [0],\n",
+            "        [0],\n",
+            "        [5],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [4]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[3, 2, 4, 1, 8, 9, 1, 2, 9, 7, 2, 9, 3, 3, 6, 3, 8, 7, 6, 2, 5, 2, 8, 9,\n",
+            "         6, 0, 0, 5, 2, 9, 5, 4],\n",
+            "        [5, 6, 7, 9, 0, 1, 9, 5, 1, 4, 6, 1, 6, 5, 2, 5, 2, 5, 2, 5, 6, 5, 0, 1,\n",
+            "         5, 2, 2, 6, 3, 1, 3, 7],\n",
+            "        [6, 0, 5, 8, 1, 7, 6, 7, 8, 5, 3, 0, 5, 2, 5, 7, 0, 2, 3, 6, 3, 3, 1, 5,\n",
+            "         3, 8, 8, 3, 6, 5, 7, 2],\n",
+            "        [2, 3, 2, 5, 2, 5, 8, 6, 7, 2, 5, 8, 4, 7, 3, 2, 5, 4, 5, 3, 7, 6, 6, 7,\n",
+            "         2, 1, 6, 2, 0, 7, 2, 5],\n",
+            "        [7, 5, 3, 3, 9, 8, 0, 3, 2, 3, 4, 2, 2, 6, 1, 4, 1, 6, 4, 7, 4, 0, 2, 8,\n",
+            "         7, 3, 1, 4, 5, 2, 4, 0]], device='cuda:0')\n",
+            "tensor([2, 1, 6, 6, 8, 4, 8, 4, 5, 0, 9, 9, 9, 8, 9, 9, 3, 7, 5, 0, 0, 5, 2, 2,\n",
+            "        3, 8, 6, 3, 4, 0, 5, 8], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[23.3594, 20.5156, 25.5938, 22.1406, 21.9844, 22.4844, 25.2344, 22.6250,\n",
+            "         22.0312, 20.9688],\n",
+            "        [20.2031, 26.9688, 21.4531, 20.1250, 21.4219, 20.4844, 21.2812, 21.0000,\n",
+            "         20.6719, 22.9688],\n",
+            "        [20.4219, 21.5625, 21.4375, 20.6406, 21.1562, 23.0625, 22.6250, 22.7969,\n",
+            "         20.8750, 20.6875],\n",
+            "        [18.3750, 19.3125, 23.1094, 20.9688, 20.7188, 21.2344, 29.0469, 20.0469,\n",
+            "         19.0000, 18.0469],\n",
+            "        [24.0469, 23.9844, 22.1094, 21.5312, 20.3125, 21.9688, 22.2031, 21.1094,\n",
+            "         25.1094, 23.6562],\n",
+            "        [19.7031, 20.8594, 20.2656, 18.8281, 21.2656, 20.7500, 16.7031, 24.5625,\n",
+            "         19.5938, 21.4375],\n",
+            "        [21.5312, 19.2656, 21.5938, 20.2188, 17.5000, 19.7812, 18.1875, 20.0938,\n",
+            "         27.5938, 19.0469],\n",
+            "        [21.5156, 20.9688, 23.8594, 24.6562, 28.0312, 25.2031, 22.2656, 24.7188,\n",
+            "         20.4531, 20.2656],\n",
+            "        [19.0469, 19.1875, 21.1562, 20.7812, 21.1562, 25.6094, 21.9375, 21.3281,\n",
+            "         18.6719, 19.8906],\n",
+            "        [22.3438, 19.5000, 20.2812, 19.7031, 20.9375, 20.2656, 20.4688, 20.2344,\n",
+            "         21.8281, 19.4219],\n",
+            "        [19.4219, 23.6406, 19.8750, 19.5625, 17.8750, 20.2188, 18.3906, 19.3438,\n",
+            "         20.5781, 26.5469],\n",
+            "        [17.4375, 19.8906, 18.2344, 18.3594, 17.7031, 17.7656, 17.6719, 18.8594,\n",
+            "         19.2188, 21.6562],\n",
+            "        [20.2500, 23.7500, 21.0625, 19.9844, 20.4219, 19.9062, 19.1719, 20.1562,\n",
+            "         21.2969, 25.6094],\n",
+            "        [21.0938, 20.8281, 20.8281, 18.9062, 18.6719, 19.8594, 19.3906, 18.9062,\n",
+            "         24.4062, 20.1562],\n",
+            "        [19.3750, 23.3750, 19.1562, 18.6719, 17.8125, 18.9062, 18.3906, 20.0312,\n",
+            "         21.7031, 24.5000],\n",
+            "        [18.6406, 23.0781, 20.3438, 18.5000, 18.7031, 18.8906, 18.2188, 19.7344,\n",
+            "         19.4531, 27.5312],\n",
+            "        [17.8438, 19.3281, 21.6875, 25.6094, 20.0156, 22.3438, 21.5156, 20.0312,\n",
+            "         20.4844, 18.3750],\n",
+            "        [20.4062, 20.7969, 21.4219, 21.1875, 22.2031, 22.7188, 18.1406, 28.8281,\n",
+            "         19.3125, 20.4688],\n",
+            "        [18.4062, 18.2656, 19.5781, 20.3438, 17.7812, 23.0938, 19.8281, 19.6875,\n",
+            "         17.9531, 17.2812],\n",
+            "        [25.2031, 21.5000, 21.3125, 18.8281, 17.9844, 18.8438, 18.7500, 19.7812,\n",
+            "         23.0156, 19.8750],\n",
+            "        [25.0000, 21.0312, 24.6562, 19.8125, 20.4062, 19.8906, 21.5312, 20.6094,\n",
+            "         22.9688, 20.1094],\n",
+            "        [20.0156, 19.8438, 21.8281, 22.3125, 18.6406, 26.3125, 22.3125, 20.7812,\n",
+            "         19.8594, 18.5469],\n",
+            "        [20.3125, 20.0312, 28.7031, 20.5938, 19.0156, 20.7031, 21.0312, 20.0312,\n",
+            "         19.0000, 18.1875],\n",
+            "        [21.8281, 20.5000, 27.7344, 23.5000, 22.8125, 22.2500, 23.8906, 21.8906,\n",
+            "         20.9219, 19.3750],\n",
+            "        [19.0625, 19.1250, 20.9375, 26.8125, 20.3125, 24.7031, 20.6094, 20.9375,\n",
+            "         18.7969, 17.4219],\n",
+            "        [18.5625, 19.4688, 19.2500, 18.5312, 18.1250, 18.4219, 15.6406, 18.1250,\n",
+            "         26.4375, 18.7344],\n",
+            "        [19.6406, 19.6094, 23.3906, 22.4219, 22.1250, 22.0781, 25.7969, 21.4688,\n",
+            "         19.8438, 18.7500],\n",
+            "        [20.4531, 21.1875, 22.5156, 27.7656, 21.5000, 24.0938, 21.9062, 22.2656,\n",
+            "         21.2969, 20.2188],\n",
+            "        [19.0000, 20.4844, 22.7031, 21.6406, 27.0781, 22.7969, 21.7500, 25.3125,\n",
+            "         20.4844, 18.7500],\n",
+            "        [23.2656, 16.1562, 18.5156, 15.1016, 14.2969, 15.6953, 15.1094, 15.3125,\n",
+            "         20.3125, 13.8047],\n",
+            "        [18.2188, 18.3750, 21.9062, 21.5469, 20.4688, 25.8125, 21.7812, 20.7969,\n",
+            "         17.7344, 18.0156],\n",
+            "        [22.7188, 23.1719, 22.5938, 21.6875, 21.1094, 21.7656, 19.9219, 21.6562,\n",
+            "         27.3438, 22.3281]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[2],\n",
+            "        [1],\n",
+            "        [5],\n",
+            "        [6],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [4],\n",
+            "        [5],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [9],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [5],\n",
+            "        [0],\n",
+            "        [0],\n",
+            "        [5],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [6],\n",
+            "        [3],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [5],\n",
+            "        [8]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[2, 1, 5, 6, 8, 7, 8, 4, 5, 0, 9, 9, 9, 8, 9, 9, 3, 7, 5, 0, 0, 5, 2, 2,\n",
+            "         3, 8, 6, 3, 4, 0, 5, 8],\n",
+            "        [6, 9, 7, 2, 0, 9, 2, 5, 6, 8, 1, 1, 1, 0, 1, 1, 5, 5, 3, 8, 2, 6, 6, 6,\n",
+            "         5, 1, 2, 5, 7, 8, 2, 1],\n",
+            "        [0, 2, 6, 5, 1, 4, 0, 7, 7, 4, 8, 8, 8, 2, 8, 2, 2, 4, 6, 1, 8, 3, 5, 3,\n",
+            "         7, 2, 3, 2, 5, 2, 6, 0],\n",
+            "        [7, 4, 1, 3, 9, 1, 3, 3, 4, 6, 5, 7, 2, 1, 7, 7, 6, 2, 7, 2, 6, 2, 3, 4,\n",
+            "         2, 9, 4, 7, 2, 1, 3, 2],\n",
+            "        [5, 6, 2, 4, 6, 5, 7, 2, 2, 2, 2, 3, 4, 9, 0, 8, 8, 3, 2, 9, 1, 7, 0, 5,\n",
+            "         6, 0, 5, 6, 6, 5, 7, 9]], device='cuda:0')\n",
+            "tensor([0, 1, 7, 2, 8, 8, 7, 8, 5, 1, 8, 7, 1, 3, 0, 5, 7, 9, 7, 4, 5, 9, 8, 0,\n",
+            "        7, 9, 8, 2, 7, 6, 9, 4], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[24.2500, 19.1406, 21.7188, 18.0781, 20.4688, 19.7500, 20.2969, 20.0000,\n",
+            "         22.9062, 18.6562],\n",
+            "        [20.0469, 26.8906, 21.0938, 20.6094, 20.3125, 21.2812, 20.2812, 19.7500,\n",
+            "         20.3906, 23.2812],\n",
+            "        [18.1250, 20.1406, 21.2031, 20.2969, 24.7969, 21.5781, 18.8750, 27.3438,\n",
+            "         18.3438, 18.6406],\n",
+            "        [21.4062, 19.0312, 26.1094, 19.5625, 20.6406, 19.7500, 21.0312, 20.7500,\n",
+            "         19.6562, 19.0312],\n",
+            "        [19.2500, 20.2031, 20.0156, 19.5156, 18.3906, 19.8281, 16.1875, 19.9219,\n",
+            "         27.0469, 20.1562],\n",
+            "        [20.8906, 20.5469, 20.8750, 19.2969, 18.2656, 19.7344, 17.7656, 20.2500,\n",
+            "         25.9844, 19.0156],\n",
+            "        [19.6562, 20.5312, 21.8750, 21.8125, 23.8750, 22.9219, 18.2188, 29.5312,\n",
+            "         18.6719, 19.1719],\n",
+            "        [21.5312, 21.1562, 21.6875, 20.5156, 18.9531, 20.3438, 19.2812, 20.6094,\n",
+            "         27.3281, 20.0781],\n",
+            "        [18.7500, 19.5312, 21.3594, 25.3750, 20.1719, 25.7500, 21.2812, 22.0781,\n",
+            "         19.2812, 18.0625],\n",
+            "        [20.7812, 26.5469, 20.8125, 21.0000, 18.6562, 21.7969, 18.5312, 21.1406,\n",
+            "         20.2656, 23.4375],\n",
+            "        [19.7500, 20.0000, 20.7656, 18.6562, 18.8594, 18.9688, 17.4375, 19.4375,\n",
+            "         27.3594, 19.9375],\n",
+            "        [20.2969, 20.9062, 21.5781, 20.3906, 21.3906, 22.1406, 18.3438, 27.8438,\n",
+            "         20.7031, 20.9219],\n",
+            "        [19.1875, 25.7500, 20.7188, 19.3125, 18.4531, 19.4375, 18.1719, 18.2344,\n",
+            "         19.4844, 23.5156],\n",
+            "        [18.4375, 19.9375, 21.7812, 26.1875, 19.6719, 23.2969, 19.7969, 21.3750,\n",
+            "         19.6875, 18.5000],\n",
+            "        [28.1719, 21.5000, 24.0781, 20.2656, 21.9688, 20.7500, 20.8906, 20.9375,\n",
+            "         24.1719, 20.8594],\n",
+            "        [19.6719, 20.4844, 22.4844, 23.1094, 20.9688, 26.8906, 21.9375, 22.8750,\n",
+            "         19.2969, 19.9531],\n",
+            "        [17.9531, 19.3906, 20.4219, 20.0469, 21.3438, 21.1094, 15.8594, 28.7656,\n",
+            "         18.5781, 18.2969],\n",
+            "        [19.5469, 24.1250, 20.9219, 19.5938, 19.1875, 19.9844, 19.4844, 19.8750,\n",
+            "         21.7188, 28.3750],\n",
+            "        [18.6719, 20.0781, 20.8438, 21.4375, 21.6719, 22.5000, 18.4688, 27.4688,\n",
+            "         19.0625, 18.2969],\n",
+            "        [22.1406, 20.4375, 23.6250, 21.0156, 29.9219, 22.1094, 20.0156, 24.3750,\n",
+            "         21.4062, 20.4375],\n",
+            "        [20.4844, 19.5156, 20.2188, 20.6875, 19.1719, 25.9688, 21.0156, 22.0312,\n",
+            "         19.7344, 19.0156],\n",
+            "        [21.3438, 23.7656, 21.1094, 19.3906, 19.8906, 20.9062, 21.4219, 21.3125,\n",
+            "         21.7344, 24.6094],\n",
+            "        [20.7031, 19.8281, 19.8125, 18.7188, 16.9062, 20.0938, 18.7188, 18.9219,\n",
+            "         23.7188, 18.2188],\n",
+            "        [26.0781, 20.7188, 25.8906, 20.2500, 21.8906, 21.0312, 21.6719, 21.5625,\n",
+            "         23.5469, 20.6719],\n",
+            "        [18.8594, 19.8125, 21.0625, 21.0312, 20.1406, 21.6406, 15.8750, 28.8438,\n",
+            "         19.4844, 19.1719],\n",
+            "        [16.9531, 22.3125, 18.0469, 16.5469, 16.6094, 17.5312, 16.7656, 18.5156,\n",
+            "         18.4375, 25.5000],\n",
+            "        [21.8281, 21.1094, 19.9062, 19.9688, 17.4531, 19.9375, 19.8438, 18.7344,\n",
+            "         25.0312, 18.3125],\n",
+            "        [19.7969, 20.1094, 27.8438, 20.0781, 23.9844, 21.5625, 19.2969, 21.9375,\n",
+            "         18.8125, 19.1250],\n",
+            "        [18.7188, 20.5000, 21.4062, 22.9219, 25.3750, 26.1406, 19.9844, 27.6719,\n",
+            "         18.7656, 20.3125],\n",
+            "        [20.6719, 21.0000, 22.5156, 24.2031, 22.9062, 24.0312, 25.3906, 22.9219,\n",
+            "         21.8125, 20.0938],\n",
+            "        [18.0938, 22.9219, 19.0625, 17.9375, 17.8750, 18.6094, 17.0625, 18.5625,\n",
+            "         19.0469, 27.2344],\n",
+            "        [21.4688, 21.7031, 23.1250, 25.7656, 28.0469, 27.7656, 20.6094, 27.5000,\n",
+            "         20.8438, 21.1094]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[0],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [5],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [0],\n",
+            "        [5],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [0],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [2],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [9],\n",
+            "        [4]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[0, 1, 7, 2, 8, 8, 7, 8, 5, 1, 8, 7, 1, 3, 0, 5, 7, 9, 7, 4, 5, 9, 8, 0,\n",
+            "         7, 9, 8, 2, 7, 6, 9, 4],\n",
+            "        [8, 9, 4, 0, 1, 0, 4, 2, 3, 9, 2, 5, 9, 5, 8, 3, 4, 1, 5, 7, 7, 1, 0, 2,\n",
+            "         5, 1, 0, 4, 5, 3, 1, 5],\n",
+            "        [2, 5, 5, 6, 9, 2, 5, 0, 7, 5, 1, 2, 2, 2, 2, 7, 5, 8, 4, 2, 6, 8, 5, 8,\n",
+            "         2, 7, 1, 7, 4, 5, 2, 7],\n",
+            "        [4, 2, 2, 7, 2, 1, 2, 1, 2, 7, 9, 4, 8, 7, 4, 2, 2, 2, 3, 0, 3, 6, 1, 4,\n",
+            "         3, 8, 3, 5, 3, 7, 8, 3],\n",
+            "        [6, 3, 3, 4, 7, 7, 3, 7, 6, 3, 0, 9, 5, 1, 1, 6, 3, 5, 2, 5, 0, 0, 2, 6,\n",
+            "         4, 2, 5, 1, 2, 4, 5, 2]], device='cuda:0')\n",
+            "tensor([3, 9, 6, 4, 7, 6, 5, 1, 5, 8, 8, 0, 4, 0, 5, 5, 1, 1, 8, 9, 0, 3, 1, 9,\n",
+            "        2, 2, 5, 3, 9, 9, 4, 0], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[20.5625, 19.8125, 23.1719, 24.7812, 23.4219, 24.4219, 20.4531, 23.3281,\n",
+            "         20.8906, 20.5000],\n",
+            "        [16.8438, 22.0000, 18.3125, 17.3125, 17.4688, 17.7344, 16.3906, 18.9531,\n",
+            "         18.2656, 25.5625],\n",
+            "        [24.3594, 22.6719, 25.3281, 23.8906, 22.0625, 23.8594, 23.7344, 22.8438,\n",
+            "         23.2969, 21.9844],\n",
+            "        [19.8750, 19.4688, 22.7344, 21.1406, 29.3750, 22.8594, 20.4844, 22.3438,\n",
+            "         18.2031, 18.8125],\n",
+            "        [19.5469, 19.3906, 21.2500, 21.8281, 20.8906, 24.2188, 18.1562, 26.6719,\n",
+            "         19.1406, 18.0625],\n",
+            "        [21.8125, 23.3906, 25.2969, 23.0938, 22.3906, 23.3750, 26.1094, 23.5469,\n",
+            "         22.4375, 22.5938],\n",
+            "        [19.7500, 19.5781, 20.2500, 21.5312, 18.8750, 26.6250, 19.9688, 21.6719,\n",
+            "         19.2500, 18.7656],\n",
+            "        [16.7500, 23.6094, 17.7188, 17.8438, 16.4062, 18.2969, 17.2969, 15.9844,\n",
+            "         18.0312, 22.5938],\n",
+            "        [22.0156, 22.4531, 23.2969, 25.4219, 22.8594, 26.2188, 22.7344, 24.6094,\n",
+            "         21.9844, 21.0938],\n",
+            "        [21.0000, 23.5000, 20.7812, 19.3438, 18.2969, 18.7031, 18.3438, 18.6719,\n",
+            "         26.3125, 20.6719],\n",
+            "        [21.2969, 21.8438, 21.3125, 20.5312, 19.6250, 21.0625, 18.7656, 21.1250,\n",
+            "         28.4375, 21.5938],\n",
+            "        [28.3594, 19.7969, 23.1719, 17.0156, 18.7344, 18.7500, 16.6562, 18.7031,\n",
+            "         21.7812, 19.8750],\n",
+            "        [20.5625, 20.5469, 23.5000, 22.2188, 28.8906, 22.8281, 20.6094, 23.9219,\n",
+            "         19.9844, 19.7188],\n",
+            "        [22.3594, 21.5625, 21.8906, 22.1094, 21.1875, 22.1406, 21.0625, 21.3906,\n",
+            "         24.1875, 21.5312],\n",
+            "        [19.3438, 19.3594, 20.8438, 23.3750, 20.9531, 26.1719, 19.3125, 21.4375,\n",
+            "         19.2656, 19.2188],\n",
+            "        [17.9531, 18.5781, 19.4844, 20.2344, 19.5938, 26.0938, 18.7500, 21.0625,\n",
+            "         18.1562, 17.8594],\n",
+            "        [20.1094, 25.7188, 20.1094, 19.0312, 15.8984, 20.1406, 19.0938, 17.6875,\n",
+            "         19.6875, 22.2969],\n",
+            "        [16.7188, 24.3594, 17.9844, 16.6562, 15.9844, 18.1875, 16.2812, 16.5469,\n",
+            "         17.9688, 21.4062],\n",
+            "        [23.5156, 22.8906, 22.0156, 20.2188, 19.5625, 21.3438, 19.9688, 20.2969,\n",
+            "         25.3125, 21.2188],\n",
+            "        [20.6406, 24.0781, 20.6562, 19.3438, 19.2031, 19.9688, 17.8438, 20.2969,\n",
+            "         21.6562, 27.6875],\n",
+            "        [26.0938, 20.2344, 23.8125, 19.4531, 22.0469, 20.7500, 20.6406, 20.9688,\n",
+            "         23.0469, 19.2656],\n",
+            "        [21.0000, 21.4844, 22.6094, 28.2812, 21.7344, 25.2500, 23.0938, 22.9688,\n",
+            "         20.7812, 19.7969],\n",
+            "        [19.7031, 24.9844, 20.5312, 19.2812, 18.9688, 19.5781, 19.1406, 19.9688,\n",
+            "         19.8906, 22.2031],\n",
+            "        [23.0781, 25.0938, 22.2812, 21.3125, 22.1875, 21.7188, 20.6562, 22.5000,\n",
+            "         23.6562, 28.6094],\n",
+            "        [20.1562, 19.8125, 22.0156, 20.0000, 18.5156, 20.4375, 21.2812, 19.6250,\n",
+            "         19.4219, 17.9375],\n",
+            "        [21.5938, 19.7812, 26.3750, 21.4844, 21.7969, 20.7344, 23.3594, 20.9375,\n",
+            "         20.7969, 18.5781],\n",
+            "        [18.8750, 18.7969, 20.9062, 22.2031, 19.2812, 25.9375, 21.0312, 20.6250,\n",
+            "         18.9219, 18.1875],\n",
+            "        [20.2031, 21.1562, 23.1875, 25.9531, 23.2031, 23.5312, 22.8594, 23.7344,\n",
+            "         23.0938, 20.0312],\n",
+            "        [20.2344, 23.6719, 20.5156, 19.2500, 19.0938, 20.0469, 18.0156, 20.7500,\n",
+            "         21.0781, 25.8125],\n",
+            "        [17.8594, 21.3906, 18.2344, 17.0000, 17.1094, 17.5156, 16.8906, 18.1250,\n",
+            "         19.2812, 24.1250],\n",
+            "        [20.7344, 21.2344, 23.7969, 24.2812, 27.4531, 25.8594, 20.2188, 28.2344,\n",
+            "         19.8438, 20.4375],\n",
+            "        [26.0000, 21.3750, 24.0938, 21.7344, 22.0469, 22.0625, 20.7344, 23.5312,\n",
+            "         25.1406, 19.6250]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[3],\n",
+            "        [9],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [5],\n",
+            "        [1],\n",
+            "        [5],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [0],\n",
+            "        [4],\n",
+            "        [8],\n",
+            "        [5],\n",
+            "        [5],\n",
+            "        [1],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [3],\n",
+            "        [1],\n",
+            "        [9],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [5],\n",
+            "        [3],\n",
+            "        [9],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [0]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[3, 9, 2, 4, 7, 6, 5, 1, 5, 8, 8, 0, 4, 8, 5, 5, 1, 1, 8, 9, 0, 3, 1, 9,\n",
+            "         2, 2, 5, 3, 9, 9, 7, 0],\n",
+            "        [5, 1, 0, 5, 5, 2, 7, 9, 3, 1, 1, 2, 7, 0, 3, 7, 9, 9, 0, 1, 2, 5, 9, 1,\n",
+            "         6, 6, 3, 7, 1, 1, 4, 8],\n",
+            "        [4, 7, 3, 2, 3, 7, 3, 5, 7, 0, 9, 8, 2, 5, 7, 3, 5, 5, 1, 8, 8, 6, 2, 8,\n",
+            "         5, 4, 6, 5, 8, 8, 5, 2],\n",
+            "        [7, 2, 5, 7, 2, 1, 2, 8, 2, 2, 2, 9, 5, 3, 4, 4, 2, 2, 2, 2, 4, 7, 7, 0,\n",
+            "         0, 0, 2, 4, 7, 2, 3, 7],\n",
+            "        [2, 8, 6, 3, 4, 5, 6, 3, 4, 9, 0, 1, 3, 2, 2, 2, 0, 8, 5, 0, 7, 2, 8, 7,\n",
+            "         3, 3, 7, 2, 2, 7, 2, 5]], device='cuda:0')\n",
+            "tensor([3, 0, 0, 9, 8, 1, 5, 7, 0, 8, 2, 4, 7, 0, 2, 3, 6, 3, 8, 5, 0, 3, 4, 3,\n",
+            "        9, 0, 6, 1, 0, 9, 1, 0], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[18.8125, 19.2031, 21.1094, 27.0156, 20.3750, 23.0781, 19.7969, 21.4531,\n",
+            "         19.0625, 18.1094],\n",
+            "        [28.2969, 21.2031, 24.1719, 19.8750, 20.0156, 19.8125, 20.2969, 21.2969,\n",
+            "         22.8906, 20.6875],\n",
+            "        [26.9531, 22.0156, 22.3125, 20.5000, 18.3594, 19.6406, 18.6094, 19.6875,\n",
+            "         21.4375, 20.0156],\n",
+            "        [20.2812, 22.5156, 18.3594, 17.9219, 17.2500, 18.0312, 16.9531, 18.5000,\n",
+            "         20.8438, 24.0156],\n",
+            "        [19.3438, 20.3906, 18.7656, 18.7969, 17.0469, 18.5938, 16.7031, 18.2500,\n",
+            "         25.3125, 19.4844],\n",
+            "        [19.2812, 25.2344, 19.4844, 17.4375, 17.6875, 19.0625, 17.8438, 17.8438,\n",
+            "         18.6719, 24.1094],\n",
+            "        [20.4688, 22.2344, 23.2188, 23.8594, 20.9062, 28.6094, 22.4062, 22.5781,\n",
+            "         20.1250, 22.2969],\n",
+            "        [18.1406, 19.0156, 19.4375, 20.8281, 19.0625, 21.0625, 16.9219, 27.4062,\n",
+            "         18.6875, 17.7656],\n",
+            "        [25.9062, 22.9375, 22.1250, 19.4062, 17.9219, 19.4531, 20.7500, 20.3125,\n",
+            "         24.4375, 20.5156],\n",
+            "        [20.9844, 20.4375, 20.2969, 19.6719, 17.9375, 19.2812, 18.8438, 19.2500,\n",
+            "         26.4219, 19.3594],\n",
+            "        [20.6875, 19.7812, 27.1406, 21.4375, 23.3438, 21.8750, 21.4375, 22.9062,\n",
+            "         19.9062, 19.5156],\n",
+            "        [19.7656, 19.7656, 23.0000, 19.9062, 28.6562, 21.1094, 17.6250, 24.4688,\n",
+            "         18.7656, 19.2812],\n",
+            "        [20.2031, 20.7188, 20.7969, 20.6406, 20.9844, 21.5469, 15.6016, 30.3750,\n",
+            "         18.8438, 19.3906],\n",
+            "        [23.4219, 20.5625, 22.9688, 20.5312, 20.7344, 21.5312, 22.2188, 21.6719,\n",
+            "         22.3125, 19.8438],\n",
+            "        [21.0938, 20.7344, 27.1875, 21.7656, 24.9062, 22.6406, 22.1250, 23.3594,\n",
+            "         19.8750, 19.8750],\n",
+            "        [17.2500, 19.2031, 19.4688, 24.2812, 21.4844, 25.3125, 20.4219, 21.6719,\n",
+            "         18.8750, 17.7656],\n",
+            "        [18.7656, 19.2500, 21.8281, 20.2656, 20.6250, 20.4844, 25.3125, 20.2969,\n",
+            "         19.5000, 18.3281],\n",
+            "        [20.1875, 20.2344, 21.8438, 26.0469, 19.4062, 23.8281, 19.5312, 21.6875,\n",
+            "         21.0625, 19.2031],\n",
+            "        [23.6250, 25.0469, 22.7812, 21.7969, 21.3281, 22.4531, 20.8438, 22.6562,\n",
+            "         28.0312, 23.7656],\n",
+            "        [21.5312, 21.9688, 23.5156, 27.1875, 22.2500, 25.9375, 23.0625, 24.6719,\n",
+            "         22.0000, 20.7031],\n",
+            "        [25.7656, 21.7656, 22.3594, 18.6562, 17.5312, 18.9219, 20.3281, 20.0469,\n",
+            "         21.9219, 19.5000],\n",
+            "        [19.9531, 20.2500, 24.7500, 25.9688, 25.2188, 25.4688, 23.0938, 23.7344,\n",
+            "         19.8438, 20.1875],\n",
+            "        [18.4375, 19.6250, 22.4375, 22.1719, 26.5312, 23.9844, 20.4219, 26.8281,\n",
+            "         18.1875, 18.3594],\n",
+            "        [18.4531, 19.3750, 20.1719, 26.8438, 20.2031, 22.9844, 20.3125, 21.5000,\n",
+            "         18.7500, 18.2656],\n",
+            "        [18.6719, 21.6719, 18.8906, 16.8594, 17.0469, 17.7500, 17.9062, 18.2031,\n",
+            "         19.8906, 24.4219],\n",
+            "        [26.8906, 21.4844, 21.6562, 18.3906, 19.2656, 19.5156, 19.8438, 18.7500,\n",
+            "         23.9844, 19.8281],\n",
+            "        [20.4219, 21.1562, 24.5938, 24.0625, 21.8125, 22.9688, 26.6094, 20.6094,\n",
+            "         20.5781, 19.3125],\n",
+            "        [18.4219, 25.3438, 19.0469, 17.3281, 18.0625, 18.7500, 16.4688, 17.4844,\n",
+            "         18.5000, 22.8281],\n",
+            "        [26.2500, 21.6875, 20.8281, 19.4375, 18.2656, 19.0625, 17.9219, 19.1250,\n",
+            "         22.3281, 21.4688],\n",
+            "        [19.2188, 23.3281, 19.4531, 18.1250, 17.8750, 18.6406, 18.6562, 19.7812,\n",
+            "         20.3750, 26.1719],\n",
+            "        [20.5312, 26.5625, 20.4531, 19.7969, 20.1094, 20.0000, 21.0781, 19.4219,\n",
+            "         20.8906, 24.2188],\n",
+            "        [28.7812, 20.7969, 26.0469, 20.2188, 22.2031, 21.8906, 19.4844, 21.7500,\n",
+            "         25.2031, 20.7031]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[3],\n",
+            "        [0],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [1],\n",
+            "        [5],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [8],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [2],\n",
+            "        [5],\n",
+            "        [6],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [3],\n",
+            "        [0],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [3],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [6],\n",
+            "        [1],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [1],\n",
+            "        [0]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[3, 0, 0, 9, 8, 1, 5, 7, 0, 8, 2, 4, 7, 0, 2, 5, 6, 3, 8, 3, 0, 3, 7, 3,\n",
+            "         9, 0, 6, 1, 0, 9, 1, 0],\n",
+            "        [5, 2, 2, 1, 1, 9, 3, 5, 8, 0, 4, 7, 5, 2, 4, 3, 2, 5, 1, 5, 2, 5, 4, 5,\n",
+            "         1, 8, 2, 9, 8, 1, 9, 2],\n",
+            "        [7, 8, 1, 8, 9, 2, 2, 3, 1, 1, 7, 2, 4, 8, 7, 7, 4, 2, 9, 7, 8, 4, 5, 7,\n",
+            "         8, 2, 3, 2, 1, 8, 6, 8],\n",
+            "        [2, 7, 8, 0, 0, 0, 7, 2, 2, 2, 5, 5, 2, 6, 5, 4, 5, 7, 0, 2, 1, 2, 2, 6,\n",
+            "         2, 1, 5, 5, 9, 7, 8, 4],\n",
+            "        [4, 1, 3, 7, 3, 5, 6, 4, 6, 3, 3, 3, 1, 7, 6, 6, 7, 8, 2, 6, 6, 7, 3, 4,\n",
+            "         0, 6, 4, 8, 2, 2, 0, 5]], device='cuda:0')\n",
+            "tensor([7, 9, 1, 2, 6, 9, 3, 4, 6, 0, 0, 6, 6, 6, 3, 2, 6, 1, 8, 2, 1, 6, 8, 6,\n",
+            "        8, 0, 4, 0, 7, 7, 5, 5], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[19.0938, 21.7031, 19.0781, 18.5781, 20.4219, 20.0312, 16.1406, 26.9844,\n",
+            "         21.0312, 20.4844],\n",
+            "        [18.5781, 22.7500, 19.3906, 18.3281, 18.2031, 18.0938, 18.9531, 19.4688,\n",
+            "         20.3125, 24.7969],\n",
+            "        [17.1562, 24.7969, 18.6250, 17.7656, 18.7500, 17.7969, 17.3906, 17.0469,\n",
+            "         17.8125, 19.5625],\n",
+            "        [21.8438, 20.5625, 28.6719, 21.5625, 21.5781, 21.6875, 21.3750, 21.8125,\n",
+            "         19.3594, 19.3750],\n",
+            "        [20.8281, 20.8594, 23.9062, 22.7500, 24.9219, 24.3281, 25.6719, 23.7031,\n",
+            "         21.3438, 19.7344],\n",
+            "        [19.1562, 25.6719, 21.0156, 19.4531, 18.4375, 20.2344, 19.6406, 19.8281,\n",
+            "         20.1250, 28.4375],\n",
+            "        [21.0312, 20.6875, 22.3125, 27.1875, 21.7656, 23.3906, 21.9688, 22.0156,\n",
+            "         21.1875, 19.7812],\n",
+            "        [21.3281, 20.9688, 23.2031, 28.3438, 28.1250, 24.8594, 22.2344, 24.1562,\n",
+            "         20.8594, 19.5312],\n",
+            "        [18.9844, 20.5625, 22.3594, 20.9688, 21.3594, 20.5781, 24.4219, 20.0156,\n",
+            "         20.4219, 19.2656],\n",
+            "        [29.4062, 21.2969, 25.8906, 19.5781, 19.4219, 20.7656, 20.1562, 19.6250,\n",
+            "         23.0156, 19.6562],\n",
+            "        [20.4688, 15.7422, 17.6094, 13.1484, 12.8984, 14.0625, 15.2188, 14.2188,\n",
+            "         17.2188, 13.1016],\n",
+            "        [18.6719, 19.9375, 23.9219, 23.0000, 22.0469, 22.9844, 28.6406, 20.6094,\n",
+            "         19.0000, 18.7188],\n",
+            "        [17.6875, 20.2344, 21.6875, 22.2969, 20.8750, 22.5000, 26.4375, 20.2969,\n",
+            "         18.5469, 19.4688],\n",
+            "        [18.0000, 19.8750, 21.6406, 21.6562, 19.8594, 21.7344, 25.2188, 19.6406,\n",
+            "         19.5938, 18.1094],\n",
+            "        [20.0312, 21.1250, 23.0781, 26.2031, 22.3281, 25.7500, 21.2188, 26.6875,\n",
+            "         20.8125, 20.4375],\n",
+            "        [22.0312, 20.9375, 27.0938, 20.8906, 23.0156, 22.0938, 24.5312, 22.4062,\n",
+            "         21.3906, 20.8281],\n",
+            "        [20.7656, 21.3125, 24.8750, 24.6250, 23.1094, 24.4844, 27.7188, 22.0156,\n",
+            "         20.4375, 20.1094],\n",
+            "        [22.3594, 24.5156, 21.2969, 20.1250, 19.7969, 21.4062, 19.3750, 20.8750,\n",
+            "         22.4375, 22.3125],\n",
+            "        [20.3281, 21.8906, 21.4219, 19.3594, 18.9531, 20.6562, 18.1719, 19.5000,\n",
+            "         25.3438, 20.8906],\n",
+            "        [21.9375, 20.9219, 27.9375, 22.8750, 21.3125, 21.9531, 25.5625, 20.9688,\n",
+            "         20.5312, 20.2344],\n",
+            "        [19.3281, 26.4844, 20.1250, 19.0938, 18.3906, 19.8906, 18.2188, 19.5000,\n",
+            "         20.6094, 22.6875],\n",
+            "        [23.1406, 21.1406, 24.5625, 22.4062, 24.1250, 22.8750, 26.5938, 22.5000,\n",
+            "         22.7031, 21.3594],\n",
+            "        [22.8594, 22.0938, 21.6562, 19.4531, 18.9531, 20.4062, 19.4688, 20.7344,\n",
+            "         26.7969, 21.8125],\n",
+            "        [15.6172, 18.2188, 19.4688, 18.8125, 17.0312, 19.7969, 26.9688, 16.3906,\n",
+            "         16.4375, 16.6719],\n",
+            "        [25.0469, 22.1719, 22.1562, 18.8594, 21.1562, 20.5312, 18.6250, 21.6562,\n",
+            "         28.9844, 23.0938],\n",
+            "        [25.5469, 23.1406, 25.7344, 22.2500, 22.9219, 22.7812, 24.0000, 22.8750,\n",
+            "         24.2969, 22.3438],\n",
+            "        [20.8750, 22.4219, 23.2500, 24.1094, 26.7344, 25.1250, 21.6406, 25.9688,\n",
+            "         21.2656, 21.9375],\n",
+            "        [27.4062, 22.3438, 24.0781, 20.2031, 22.0156, 21.3906, 20.7500, 22.3281,\n",
+            "         24.0469, 21.9375],\n",
+            "        [19.4531, 20.9531, 22.2812, 22.2500, 22.6875, 22.5156, 18.2031, 29.1250,\n",
+            "         19.0469, 19.2969],\n",
+            "        [18.3438, 19.3281, 20.7812, 20.4219, 21.2656, 21.3281, 15.4766, 28.3125,\n",
+            "         19.0781, 18.1250],\n",
+            "        [19.2188, 19.7031, 21.6250, 23.3125, 22.7500, 26.2188, 22.1562, 22.7812,\n",
+            "         19.0312, 19.4219],\n",
+            "        [18.9688, 18.7344, 19.6094, 20.5625, 19.3750, 25.7500, 21.0938, 20.2812,\n",
+            "         18.9531, 18.8438]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[7],\n",
+            "        [9],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [6],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [3],\n",
+            "        [6],\n",
+            "        [0],\n",
+            "        [0],\n",
+            "        [6],\n",
+            "        [6],\n",
+            "        [6],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [6],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [6],\n",
+            "        [8],\n",
+            "        [6],\n",
+            "        [8],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [5],\n",
+            "        [5]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[7, 9, 1, 2, 6, 9, 3, 3, 6, 0, 0, 6, 6, 6, 7, 2, 6, 1, 8, 2, 1, 6, 8, 6,\n",
+            "         8, 2, 4, 0, 7, 7, 5, 5],\n",
+            "        [1, 1, 9, 0, 4, 1, 5, 4, 2, 2, 2, 2, 5, 5, 3, 6, 2, 8, 1, 6, 9, 2, 0, 5,\n",
+            "         0, 0, 7, 2, 4, 5, 3, 6],\n",
+            "        [8, 8, 4, 7, 5, 2, 2, 5, 4, 8, 8, 3, 3, 3, 5, 4, 3, 0, 2, 3, 8, 4, 1, 2,\n",
+            "         9, 8, 5, 8, 5, 4, 7, 3],\n",
+            "        [9, 7, 2, 5, 2, 5, 7, 7, 3, 1, 1, 5, 2, 2, 2, 7, 5, 9, 9, 5, 2, 0, 9, 3,\n",
+            "         1, 6, 3, 1, 2, 2, 4, 7],\n",
+            "        [4, 2, 8, 4, 7, 8, 6, 2, 5, 5, 6, 4, 4, 1, 4, 5, 4, 5, 5, 0, 5, 5, 2, 1,\n",
+            "         2, 1, 2, 7, 3, 3, 6, 2]], device='cuda:0')\n",
+            "tensor([3, 5, 2, 3, 4, 1, 7, 5, 4, 6, 1, 9, 3, 6, 6, 9, 3, 8, 0, 7, 2, 6, 2, 5,\n",
+            "        8, 5, 4, 6, 8, 9, 9, 1], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[19.5625, 20.4844, 22.4844, 27.7969, 23.9531, 23.8750, 21.0625, 24.4844,\n",
+            "         19.5938, 18.9688],\n",
+            "        [20.0625, 19.9531, 21.6875, 23.5938, 23.1562, 26.9219, 21.9844, 20.9062,\n",
+            "         19.5781, 19.2969],\n",
+            "        [19.8125, 19.2031, 27.6094, 21.3906, 20.0938, 20.7812, 22.3594, 19.0469,\n",
+            "         18.3594, 18.4062],\n",
+            "        [21.0156, 20.6250, 23.8438, 27.6562, 22.5781, 25.9844, 24.0781, 22.6875,\n",
+            "         21.4844, 19.2969],\n",
+            "        [19.6406, 20.5312, 21.3438, 21.9844, 22.8281, 23.0781, 18.8594, 25.6094,\n",
+            "         19.9375, 19.2500],\n",
+            "        [18.4062, 25.0625, 19.5781, 19.3594, 19.7969, 20.0625, 19.3438, 18.6875,\n",
+            "         18.6406, 22.9219],\n",
+            "        [19.3125, 19.8281, 21.3750, 20.8750, 21.4219, 21.6875, 16.6719, 28.8594,\n",
+            "         19.4219, 19.9688],\n",
+            "        [19.7656, 19.4062, 19.6719, 22.3281, 20.5156, 27.8906, 19.5625, 21.7031,\n",
+            "         19.4688, 20.4375],\n",
+            "        [19.4062, 19.9062, 21.7812, 20.5625, 23.5000, 21.0469, 20.0000, 21.2344,\n",
+            "         19.2031, 19.4844],\n",
+            "        [20.4219, 20.6094, 23.8281, 22.6562, 21.2500, 21.4219, 24.3281, 21.1250,\n",
+            "         20.4062, 19.3750],\n",
+            "        [19.3125, 27.1094, 21.7969, 19.6562, 21.7344, 20.3125, 20.4531, 20.3594,\n",
+            "         19.9062, 23.5469],\n",
+            "        [18.9531, 24.5312, 19.1562, 19.0156, 18.0781, 20.1250, 18.2812, 19.3594,\n",
+            "         19.5469, 24.7188],\n",
+            "        [18.8438, 19.5312, 21.3281, 27.0625, 21.7188, 25.1562, 20.9375, 22.0781,\n",
+            "         18.9062, 18.7969],\n",
+            "        [18.2656, 19.2812, 22.5156, 22.1875, 20.9375, 22.7812, 27.6719, 20.0625,\n",
+            "         18.5781, 18.4688],\n",
+            "        [19.1875, 19.5625, 22.7031, 22.5469, 22.1250, 22.8438, 26.7188, 20.5938,\n",
+            "         19.1875, 18.8594],\n",
+            "        [20.1719, 25.8594, 21.1562, 19.3438, 19.0312, 20.4062, 20.0000, 19.9375,\n",
+            "         20.2812, 26.3750],\n",
+            "        [19.7344, 20.4531, 21.5156, 27.2188, 19.9062, 23.9062, 21.2812, 21.9219,\n",
+            "         19.8594, 18.7969],\n",
+            "        [19.9219, 20.6719, 20.8594, 19.3125, 19.4531, 20.4688, 18.9375, 19.8594,\n",
+            "         26.1719, 20.9688],\n",
+            "        [28.0625, 21.8438, 25.3438, 20.6719, 21.9375, 21.9688, 23.0312, 21.8906,\n",
+            "         22.8125, 21.0312],\n",
+            "        [17.0000, 18.6875, 18.2812, 18.2812, 18.0156, 19.2344, 14.7891, 25.5469,\n",
+            "         17.5000, 17.4062],\n",
+            "        [17.9688, 19.0781, 27.5781, 20.8750, 21.7344, 21.4531, 21.8906, 20.5938,\n",
+            "         18.0312, 18.0625],\n",
+            "        [19.1719, 19.6719, 23.0938, 20.6406, 21.4219, 21.1406, 23.9219, 20.8906,\n",
+            "         19.1875, 18.8281],\n",
+            "        [26.1406, 19.8438, 25.3594, 19.5000, 20.4219, 20.0781, 19.8281, 20.4219,\n",
+            "         23.4219, 19.3281],\n",
+            "        [18.1250, 19.3750, 21.1094, 23.7344, 18.7188, 25.0000, 21.9688, 19.2344,\n",
+            "         19.6406, 18.4531],\n",
+            "        [19.1250, 18.2812, 17.4688, 16.5938, 16.3594, 16.7500, 17.2188, 16.6875,\n",
+            "         24.3594, 17.0938],\n",
+            "        [20.0781, 19.8750, 20.6562, 21.9531, 20.8906, 27.0781, 20.7031, 23.3438,\n",
+            "         19.0469, 19.3438],\n",
+            "        [19.8906, 20.3281, 21.2031, 20.0625, 25.7188, 22.5312, 18.4062, 26.3594,\n",
+            "         19.4531, 20.5469],\n",
+            "        [18.6094, 20.6562, 22.5312, 22.7969, 21.1250, 21.7656, 25.3125, 20.2500,\n",
+            "         19.7344, 18.5312],\n",
+            "        [23.0625, 21.2031, 21.7344, 19.2188, 19.2500, 20.0781, 20.4844, 19.8906,\n",
+            "         26.3594, 19.4219],\n",
+            "        [17.2031, 22.6719, 19.1094, 17.0469, 17.5781, 19.3594, 17.0781, 17.9062,\n",
+            "         19.2812, 25.3438],\n",
+            "        [19.5938, 23.9375, 19.4062, 19.5156, 18.5938, 19.9375, 18.3125, 20.4531,\n",
+            "         20.3438, 24.2969],\n",
+            "        [19.2500, 25.8438, 20.2500, 19.9688, 19.2656, 20.5312, 19.7656, 18.9688,\n",
+            "         19.3594, 21.9531]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[3],\n",
+            "        [5],\n",
+            "        [2],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [5],\n",
+            "        [4],\n",
+            "        [6],\n",
+            "        [1],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [6],\n",
+            "        [6],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [0],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [6],\n",
+            "        [0],\n",
+            "        [5],\n",
+            "        [8],\n",
+            "        [5],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [9],\n",
+            "        [1]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[3, 5, 2, 3, 7, 1, 7, 5, 4, 6, 1, 9, 3, 6, 6, 9, 3, 8, 0, 7, 2, 6, 0, 5,\n",
+            "         8, 5, 7, 6, 8, 9, 9, 1],\n",
+            "        [7, 3, 6, 5, 5, 9, 5, 3, 2, 2, 9, 1, 5, 5, 5, 1, 5, 9, 2, 5, 6, 2, 2, 3,\n",
+            "         0, 7, 4, 3, 0, 1, 1, 9],\n",
+            "        [4, 4, 3, 6, 4, 5, 4, 7, 7, 3, 2, 5, 7, 2, 2, 2, 7, 2, 6, 1, 4, 4, 8, 6,\n",
+            "         1, 3, 5, 2, 2, 5, 7, 5],\n",
+            "        [5, 6, 5, 2, 3, 4, 2, 4, 5, 5, 4, 8, 4, 3, 3, 5, 2, 1, 8, 3, 5, 5, 7, 2,\n",
+            "         2, 4, 2, 5, 1, 8, 8, 2],\n",
+            "        [2, 2, 4, 7, 2, 2, 3, 9, 3, 4, 6, 7, 2, 4, 4, 8, 6, 5, 5, 2, 3, 7, 4, 8,\n",
+            "         6, 6, 9, 4, 6, 2, 5, 3]], device='cuda:0')\n",
+            "tensor([0, 2, 2, 7, 3, 2, 8, 0, 9, 5, 8, 1, 9, 4, 1, 3, 8, 1, 4, 7, 9, 4, 2, 7,\n",
+            "        0, 7, 0, 6, 6, 9, 0, 9], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[26.4531, 21.1250, 22.5000, 18.7344, 19.0938, 19.0156, 20.7188, 18.8125,\n",
+            "         22.1875, 19.1094],\n",
+            "        [21.6875, 19.9844, 28.4688, 21.9062, 19.6406, 21.2969, 23.3906, 20.7812,\n",
+            "         20.1406, 19.3125],\n",
+            "        [19.5000, 19.5000, 20.6719, 20.9531, 19.4219, 20.2500, 20.7812, 19.7656,\n",
+            "         19.4688, 17.8438],\n",
+            "        [21.0469, 22.2656, 24.1406, 22.7656, 25.7031, 23.7188, 19.8750, 30.0469,\n",
+            "         21.3125, 21.4688],\n",
+            "        [19.5469, 19.8438, 22.8438, 26.1250, 21.0625, 24.1875, 21.3750, 21.2656,\n",
+            "         20.6250, 19.4062],\n",
+            "        [21.0000, 21.8438, 25.3594, 23.4688, 23.4844, 23.2656, 25.1562, 23.1094,\n",
+            "         21.5312, 21.3281],\n",
+            "        [20.8125, 23.5000, 20.6406, 20.2812, 18.9375, 20.4844, 19.6875, 19.7969,\n",
+            "         24.7344, 22.7969],\n",
+            "        [28.0938, 21.0000, 24.5156, 19.8750, 21.4688, 20.1406, 18.0781, 21.0312,\n",
+            "         21.8281, 20.3594],\n",
+            "        [17.4219, 22.1562, 18.4219, 17.8125, 17.9219, 17.5625, 16.8281, 18.3750,\n",
+            "         19.6406, 25.1562],\n",
+            "        [19.4219, 20.0000, 21.2188, 22.8594, 21.0312, 27.0000, 19.7500, 22.1250,\n",
+            "         19.7188, 20.1250],\n",
+            "        [19.2500, 18.6719, 19.7812, 18.5781, 18.1250, 18.8750, 17.6562, 18.3125,\n",
+            "         24.7344, 18.4062],\n",
+            "        [17.1719, 24.3594, 18.8906, 17.6094, 17.8125, 18.3438, 18.1719, 16.5312,\n",
+            "         18.1250, 20.6562],\n",
+            "        [19.5938, 21.9531, 18.0156, 18.1562, 18.2031, 18.6250, 16.2500, 19.1094,\n",
+            "         20.0156, 24.9375],\n",
+            "        [18.7656, 19.2500, 20.2031, 19.2188, 27.4219, 20.6094, 18.2656, 21.9844,\n",
+            "         18.7188, 19.4688],\n",
+            "        [18.8594, 24.8906, 19.7812, 19.8594, 19.2812, 19.2344, 19.5938, 20.0156,\n",
+            "         18.4688, 20.1250],\n",
+            "        [19.0781, 20.1875, 22.1250, 23.7812, 21.9375, 23.6875, 22.0156, 22.4375,\n",
+            "         19.3594, 19.4531],\n",
+            "        [21.5312, 21.1406, 22.6250, 19.8594, 19.5781, 20.9688, 20.1875, 20.9062,\n",
+            "         25.1094, 20.1562],\n",
+            "        [19.2188, 25.7656, 19.7656, 19.4844, 19.7812, 20.0938, 19.9375, 18.4375,\n",
+            "         19.5625, 21.4531],\n",
+            "        [25.0312, 22.1094, 24.8750, 21.9844, 23.0156, 22.3750, 23.2500, 22.9062,\n",
+            "         22.4688, 20.9219],\n",
+            "        [19.8594, 19.7500, 19.7812, 18.7656, 19.5625, 20.3438, 15.4844, 27.1562,\n",
+            "         18.2188, 18.6719],\n",
+            "        [18.5156, 23.1406, 18.5469, 17.9688, 17.4375, 18.4531, 16.6875, 18.7500,\n",
+            "         20.3594, 26.6719],\n",
+            "        [21.6094, 20.1094, 23.2031, 21.5625, 24.5625, 21.4062, 22.8438, 23.1250,\n",
+            "         22.6094, 19.8125],\n",
+            "        [19.2344, 18.2344, 26.9375, 19.0781, 17.4844, 18.6406, 19.9688, 18.9219,\n",
+            "         17.7344, 17.3281],\n",
+            "        [16.6562, 17.5156, 18.2812, 18.8906, 18.6875, 19.2500, 13.7812, 26.1406,\n",
+            "         16.3906, 15.9922],\n",
+            "        [29.3281, 22.6406, 24.8438, 20.9375, 21.6250, 21.5312, 19.0469, 21.1094,\n",
+            "         23.8750, 22.4062],\n",
+            "        [19.5156, 20.8594, 20.7344, 19.5312, 20.5625, 21.0156, 17.2812, 27.5625,\n",
+            "         20.3438, 19.3125],\n",
+            "        [29.3438, 21.8594, 25.9688, 20.5156, 22.5469, 20.9531, 22.6094, 21.9844,\n",
+            "         24.6875, 22.5156],\n",
+            "        [17.9375, 18.7500, 21.3438, 21.7656, 22.2031, 22.1875, 24.3750, 19.6562,\n",
+            "         18.1719, 17.9531],\n",
+            "        [20.3906, 20.6719, 24.0781, 22.2812, 21.8906, 21.6875, 25.9375, 21.1406,\n",
+            "         20.0781, 19.3125],\n",
+            "        [20.0000, 24.4375, 20.6094, 19.3594, 19.7500, 20.3281, 19.6250, 21.0781,\n",
+            "         20.5000, 25.7031],\n",
+            "        [25.6562, 19.9062, 22.6875, 18.6406, 19.0469, 18.4375, 19.7656, 18.2812,\n",
+            "         20.5156, 17.8750],\n",
+            "        [19.6719, 24.1719, 19.6562, 18.3438, 18.7656, 19.8438, 18.1406, 21.0156,\n",
+            "         19.5625, 25.7188]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[0],\n",
+            "        [2],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [8],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [8],\n",
+            "        [1],\n",
+            "        [9],\n",
+            "        [4],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [1],\n",
+            "        [0],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [4],\n",
+            "        [2],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [6],\n",
+            "        [6],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [9]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[0, 2, 3, 7, 3, 2, 8, 0, 9, 5, 8, 1, 9, 4, 1, 3, 8, 1, 0, 7, 9, 4, 2, 7,\n",
+            "         0, 7, 0, 6, 6, 9, 0, 9],\n",
+            "        [2, 6, 6, 4, 5, 6, 1, 2, 1, 3, 2, 9, 1, 7, 9, 5, 2, 9, 2, 5, 1, 2, 6, 5,\n",
+            "         2, 5, 2, 4, 2, 1, 2, 1],\n",
+            "        [8, 3, 2, 2, 2, 4, 9, 8, 8, 7, 0, 2, 8, 5, 7, 7, 0, 5, 6, 0, 8, 7, 0, 3,\n",
+            "         8, 1, 8, 5, 3, 7, 8, 7],\n",
+            "        [1, 0, 5, 5, 6, 3, 0, 4, 2, 2, 5, 5, 0, 2, 3, 2, 1, 6, 4, 2, 7, 6, 3, 4,\n",
+            "         1, 2, 6, 3, 4, 2, 1, 5],\n",
+            "        [6, 5, 7, 3, 7, 5, 2, 7, 7, 4, 1, 6, 7, 9, 2, 6, 5, 4, 7, 1, 2, 8, 7, 2,\n",
+            "         9, 4, 4, 2, 5, 8, 6, 0]], device='cuda:0')\n",
+            "tensor([2, 8, 7, 2, 2, 5, 1, 2, 6, 2, 9, 6, 2, 3, 0, 3, 9, 8, 7, 8, 8, 4, 0, 1,\n",
+            "        8, 2, 7, 9, 3, 6, 1, 9], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[20.7500, 19.6406, 28.3750, 21.9062, 20.6406, 22.2188, 21.4531, 21.0156,\n",
+            "         18.8750, 18.6875],\n",
+            "        [24.7969, 21.2656, 20.7031, 18.7969, 17.4062, 18.2969, 19.0938, 17.8750,\n",
+            "         23.4844, 18.7500],\n",
+            "        [19.5000, 19.6250, 19.5156, 19.2188, 20.8594, 21.3281, 16.3125, 26.1250,\n",
+            "         18.5938, 19.1094],\n",
+            "        [22.4375, 21.5938, 28.5312, 22.1875, 24.6562, 22.8125, 22.2500, 23.9219,\n",
+            "         21.1562, 20.7344],\n",
+            "        [20.8438, 20.1719, 23.4219, 20.6875, 20.1875, 22.3906, 21.2500, 21.8750,\n",
+            "         21.2656, 19.6719],\n",
+            "        [18.3906, 21.0000, 22.0156, 23.2969, 20.9219, 26.9219, 20.5625, 25.8438,\n",
+            "         19.4219, 19.6406],\n",
+            "        [18.9375, 25.4062, 20.4219, 19.1719, 20.1094, 20.4062, 19.2656, 18.9062,\n",
+            "         19.6875, 21.0156],\n",
+            "        [22.1406, 18.8906, 29.4688, 20.3906, 19.8438, 20.9062, 20.9531, 20.3750,\n",
+            "         20.1719, 19.0000],\n",
+            "        [19.3281, 20.2812, 22.4844, 21.6719, 19.8594, 22.0000, 29.3125, 20.0000,\n",
+            "         20.2969, 18.7031],\n",
+            "        [21.1875, 20.8594, 29.0781, 22.5625, 20.7344, 22.5938, 24.0312, 21.4844,\n",
+            "         20.2188, 19.5312],\n",
+            "        [18.1719, 24.0469, 19.3438, 17.7031, 17.9531, 19.1875, 18.0000, 20.1094,\n",
+            "         18.4688, 25.8906],\n",
+            "        [19.7812, 20.7344, 23.4531, 22.0781, 22.1250, 22.9688, 26.2344, 21.0781,\n",
+            "         20.1719, 19.3906],\n",
+            "        [20.1719, 20.9688, 24.0938, 23.9531, 27.2656, 24.9688, 21.3125, 25.5938,\n",
+            "         19.4844, 20.1406],\n",
+            "        [20.3281, 21.2344, 22.3906, 27.5469, 21.5000, 23.8594, 22.0625, 22.1406,\n",
+            "         21.3281, 19.6250],\n",
+            "        [26.9844, 22.6875, 22.3438, 18.8594, 17.6406, 19.6406, 17.7188, 20.3594,\n",
+            "         24.6250, 24.4844],\n",
+            "        [18.1250, 20.1250, 19.0312, 26.0781, 16.9531, 21.5781, 18.9531, 19.1406,\n",
+            "         19.7812, 17.9531],\n",
+            "        [18.6562, 22.9219, 20.1406, 18.8438, 17.7500, 18.4219, 19.2188, 19.5781,\n",
+            "         19.5625, 24.6094],\n",
+            "        [20.9531, 24.9531, 20.6094, 20.3594, 18.5781, 21.1562, 20.1562, 19.8906,\n",
+            "         26.3281, 23.0625],\n",
+            "        [18.0781, 20.2031, 18.5156, 18.7344, 18.8906, 20.3125, 16.2188, 26.1406,\n",
+            "         18.0156, 19.0625],\n",
+            "        [20.7812, 19.5781, 20.6875, 18.1875, 18.4531, 19.2031, 16.4062, 19.8750,\n",
+            "         27.2188, 20.2969],\n",
+            "        [20.5625, 20.7656, 21.8438, 19.2500, 19.9375, 20.8125, 17.5469, 20.3125,\n",
+            "         27.7656, 21.8125],\n",
+            "        [18.8438, 18.8438, 22.2344, 22.0625, 29.9531, 23.5000, 19.8594, 22.3125,\n",
+            "         18.2344, 18.6719],\n",
+            "        [27.9844, 19.6562, 23.5469, 18.8125, 19.5625, 19.0938, 18.6250, 18.6406,\n",
+            "         22.7656, 19.7656],\n",
+            "        [19.9844, 25.4219, 20.1719, 19.0625, 20.4219, 19.8750, 19.0000, 19.6406,\n",
+            "         20.5000, 24.0625],\n",
+            "        [22.0469, 20.6562, 20.6250, 18.2031, 16.0781, 18.6094, 17.3594, 18.1406,\n",
+            "         23.1406, 19.0469],\n",
+            "        [21.7188, 20.1562, 29.0000, 21.4219, 21.8438, 22.0000, 23.4062, 21.3281,\n",
+            "         20.7656, 19.8125],\n",
+            "        [16.9219, 18.8750, 19.4219, 19.0312, 18.9688, 20.4844, 14.8750, 27.4531,\n",
+            "         18.2188, 18.2812],\n",
+            "        [20.5938, 20.1719, 17.8750, 17.7344, 16.2500, 18.6250, 16.4531, 17.0312,\n",
+            "         20.1875, 20.6719],\n",
+            "        [20.7344, 20.9219, 22.5625, 27.7500, 22.2500, 26.8125, 22.4375, 23.0625,\n",
+            "         20.8438, 19.7031],\n",
+            "        [20.2344, 19.7031, 21.8281, 19.9688, 19.7969, 21.5781, 24.3906, 20.4844,\n",
+            "         19.7656, 18.9844],\n",
+            "        [19.2812, 24.9375, 20.2500, 19.6875, 19.8750, 19.9844, 19.1562, 19.2031,\n",
+            "         19.0312, 22.8438],\n",
+            "        [18.9219, 22.4219, 18.8125, 16.9375, 16.8594, 18.3750, 15.8125, 19.0156,\n",
+            "         19.9844, 26.0312]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[2],\n",
+            "        [0],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [5],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [6],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [6],\n",
+            "        [4],\n",
+            "        [3],\n",
+            "        [0],\n",
+            "        [3],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [2],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [6],\n",
+            "        [1],\n",
+            "        [9]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[2, 0, 7, 2, 2, 5, 1, 2, 6, 2, 9, 6, 4, 3, 0, 3, 9, 8, 7, 8, 8, 4, 0, 1,\n",
+            "         8, 2, 7, 9, 3, 6, 1, 9],\n",
+            "        [5, 8, 5, 4, 5, 7, 9, 0, 2, 6, 1, 2, 7, 5, 8, 5, 1, 1, 5, 0, 2, 5, 2, 9,\n",
+            "         0, 6, 5, 0, 5, 2, 9, 1],\n",
+            "        [3, 1, 4, 7, 7, 3, 2, 6, 5, 5, 7, 5, 5, 2, 9, 1, 2, 9, 1, 2, 9, 7, 8, 8,\n",
+            "         1, 5, 2, 8, 7, 5, 2, 8],\n",
+            "        [6, 2, 1, 5, 8, 2, 5, 5, 3, 3, 2, 4, 2, 7, 1, 8, 7, 5, 9, 9, 5, 2, 9, 4,\n",
+            "         2, 4, 3, 1, 2, 7, 5, 7],\n",
+            "        [7, 6, 2, 0, 6, 1, 4, 3, 8, 7, 5, 3, 3, 6, 2, 7, 8, 0, 4, 7, 1, 3, 1, 2,\n",
+            "         9, 0, 4, 5, 6, 0, 4, 0]], device='cuda:0')\n",
+            "tensor([0, 7, 3, 7, 4, 5, 0, 0, 2, 9, 3, 4, 0, 6, 2, 5, 3, 7, 3, 7, 2, 5, 3, 1,\n",
+            "        1, 4, 9, 9, 5, 7, 5, 0], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[27.0312, 20.8906, 26.0938, 20.8594, 22.1875, 21.6406, 22.2031, 21.4844,\n",
+            "         22.6719, 20.6875],\n",
+            "        [18.9219, 20.2656, 20.0781, 21.5938, 21.4531, 22.9062, 17.4844, 28.3906,\n",
+            "         19.0625, 19.2031],\n",
+            "        [20.1875, 22.1562, 23.4219, 24.0781, 23.5000, 23.8438, 22.1406, 25.6406,\n",
+            "         20.2188, 21.0469],\n",
+            "        [19.3438, 19.8281, 21.4531, 21.7500, 22.5625, 22.5938, 17.7031, 28.6094,\n",
+            "         19.0781, 19.0312],\n",
+            "        [20.6875, 20.9219, 24.1719, 22.2969, 27.7812, 24.1250, 23.7969, 25.2969,\n",
+            "         20.1250, 20.9531],\n",
+            "        [18.9062, 18.2656, 20.4844, 21.2656, 20.0938, 26.0000, 19.3594, 20.4844,\n",
+            "         18.2969, 18.3750],\n",
+            "        [28.5938, 22.5469, 23.2812, 21.1719, 18.8594, 20.9062, 18.8906, 20.1094,\n",
+            "         25.1875, 20.3438],\n",
+            "        [28.1094, 20.6562, 22.1719, 17.6719, 18.5156, 18.9531, 17.9375, 18.5781,\n",
+            "         22.1719, 19.6250],\n",
+            "        [23.0938, 20.6875, 28.3594, 22.3906, 21.9531, 22.5625, 20.8594, 21.9219,\n",
+            "         19.6250, 19.6406],\n",
+            "        [20.6406, 25.2188, 21.2500, 20.1094, 19.9844, 21.1406, 20.2969, 21.5625,\n",
+            "         22.7344, 28.0000],\n",
+            "        [20.1094, 18.7969, 21.6094, 22.0156, 18.8594, 21.6719, 19.8594, 19.9531,\n",
+            "         20.4688, 17.6094],\n",
+            "        [18.9688, 20.1250, 24.2656, 24.7344, 28.3750, 22.9844, 21.1562, 23.3750,\n",
+            "         18.8594, 20.0938],\n",
+            "        [22.6719, 21.0625, 21.3750, 20.3438, 20.7031, 21.8750, 19.7500, 21.2969,\n",
+            "         21.9844, 20.2969],\n",
+            "        [18.7656, 20.5781, 24.0312, 22.3906, 20.8906, 22.3125, 23.3125, 21.1875,\n",
+            "         19.4375, 18.3750],\n",
+            "        [20.4219, 21.3594, 25.7344, 23.7656, 25.7344, 24.8906, 22.2812, 26.2656,\n",
+            "         20.1406, 20.3750],\n",
+            "        [20.2500, 20.4219, 22.0469, 23.6406, 22.2656, 26.9688, 21.8594, 22.5625,\n",
+            "         19.9688, 20.2500],\n",
+            "        [19.0781, 21.1875, 23.1250, 25.8906, 24.4375, 25.3281, 23.8438, 23.9688,\n",
+            "         21.0781, 20.3750],\n",
+            "        [22.9688, 22.6250, 25.3438, 23.3438, 26.3125, 24.4688, 23.6250, 27.1406,\n",
+            "         22.1406, 22.1719],\n",
+            "        [18.9219, 19.5000, 21.0781, 26.8281, 20.4844, 23.0625, 19.0938, 20.9062,\n",
+            "         19.7812, 18.8438],\n",
+            "        [18.2344, 20.2969, 20.3594, 19.7812, 21.0781, 21.3750, 16.4219, 27.3438,\n",
+            "         18.7969, 19.5781],\n",
+            "        [21.7656, 21.3438, 26.0625, 22.7969, 22.7656, 22.6875, 24.3438, 22.4219,\n",
+            "         20.8594, 20.6250],\n",
+            "        [19.3125, 20.2500, 21.7188, 23.1719, 22.2500, 27.3438, 22.2812, 21.7188,\n",
+            "         19.3438, 19.5938],\n",
+            "        [20.5625, 22.0781, 23.5000, 28.8906, 21.5938, 25.8438, 22.4531, 23.5312,\n",
+            "         21.6094, 20.2812],\n",
+            "        [19.7812, 23.0000, 18.9219, 18.8125, 18.4375, 19.4219, 18.0938, 18.7188,\n",
+            "         20.2188, 20.5000],\n",
+            "        [18.5781, 25.8594, 19.9531, 18.3594, 18.8750, 19.6562, 21.8125, 19.0781,\n",
+            "         18.9844, 21.0625],\n",
+            "        [19.9219, 19.6406, 22.6094, 22.1250, 26.6562, 23.0156, 19.0625, 26.1719,\n",
+            "         19.3750, 19.5781],\n",
+            "        [20.4844, 23.3594, 19.9062, 18.6406, 18.7656, 19.4844, 17.6250, 20.6875,\n",
+            "         21.9531, 27.2188],\n",
+            "        [20.1719, 23.2656, 20.2656, 18.9375, 19.5625, 19.6250, 17.4688, 20.3125,\n",
+            "         23.0156, 27.9062],\n",
+            "        [18.0625, 18.4688, 20.0156, 20.9688, 19.4531, 26.2500, 19.1094, 20.9688,\n",
+            "         17.7031, 17.8594],\n",
+            "        [18.9531, 19.7812, 20.8125, 20.5156, 21.7344, 21.0312, 17.9219, 28.3438,\n",
+            "         18.7188, 18.7812],\n",
+            "        [18.7969, 17.8594, 19.5312, 21.5625, 19.9688, 25.5938, 20.3281, 21.0156,\n",
+            "         17.8750, 17.8125],\n",
+            "        [30.2031, 22.0469, 26.2188, 20.9219, 21.4219, 21.6250, 20.7969, 21.3125,\n",
+            "         23.1094, 21.4688]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[0],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [5],\n",
+            "        [0],\n",
+            "        [0],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [2],\n",
+            "        [7],\n",
+            "        [5],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [5],\n",
+            "        [3],\n",
+            "        [1],\n",
+            "        [1],\n",
+            "        [4],\n",
+            "        [9],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [7],\n",
+            "        [5],\n",
+            "        [0]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[0, 7, 7, 7, 4, 5, 0, 0, 2, 9, 3, 4, 0, 2, 7, 5, 3, 7, 3, 7, 2, 5, 3, 1,\n",
+            "         1, 4, 9, 9, 5, 7, 5, 0],\n",
+            "        [2, 5, 3, 5, 7, 3, 8, 8, 0, 1, 5, 3, 8, 6, 4, 3, 5, 4, 5, 5, 6, 3, 5, 9,\n",
+            "         6, 7, 1, 1, 7, 4, 3, 2],\n",
+            "        [8, 3, 5, 4, 2, 7, 2, 2, 5, 8, 2, 2, 5, 3, 2, 7, 4, 2, 2, 4, 3, 6, 7, 8,\n",
+            "         9, 5, 8, 8, 3, 5, 7, 8],\n",
+            "        [6, 4, 4, 3, 5, 2, 1, 1, 3, 7, 8, 7, 2, 5, 5, 4, 7, 5, 7, 2, 4, 4, 2, 0,\n",
+            "         2, 2, 7, 7, 2, 2, 6, 1],\n",
+            "        [4, 1, 2, 2, 6, 4, 3, 9, 4, 2, 0, 5, 7, 7, 3, 2, 6, 6, 4, 1, 5, 2, 6, 5,\n",
+            "         5, 3, 0, 2, 4, 3, 4, 5]], device='cuda:0')\n",
+            "tensor([2, 2, 2, 9, 7, 3, 9, 4, 3, 5, 4, 6, 5, 6, 1, 4, 3, 4, 4, 3, 7, 8, 3, 7,\n",
+            "        8, 0, 5, 7, 6, 0, 5, 4], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[19.7500, 18.7344, 28.2031, 20.7656, 19.9688, 20.0469, 24.1562, 18.7344,\n",
+            "         18.4375, 17.9219],\n",
+            "        [23.8906, 19.2812, 27.6562, 19.0469, 19.5156, 19.2812, 21.2656, 19.1406,\n",
+            "         20.6562, 19.5469],\n",
+            "        [19.2500, 19.9219, 22.3125, 19.1875, 18.9531, 20.6875, 19.7031, 20.8750,\n",
+            "         18.8750, 19.8594],\n",
+            "        [18.5469, 23.0000, 18.7188, 17.7969, 17.2344, 18.1406, 18.1719, 18.5781,\n",
+            "         19.1406, 26.5781],\n",
+            "        [17.9688, 19.8594, 19.6250, 19.9688, 20.7812, 20.8438, 15.5625, 27.7344,\n",
+            "         18.1562, 18.3438],\n",
+            "        [19.7188, 19.4219, 22.2188, 27.0938, 21.4688, 23.2656, 20.6250, 21.9219,\n",
+            "         20.2969, 18.7812],\n",
+            "        [20.0625, 22.8750, 19.1875, 18.0469, 17.9062, 18.3438, 17.0000, 19.2031,\n",
+            "         22.0312, 27.1875],\n",
+            "        [18.4375, 18.9375, 22.6719, 21.3750, 28.0469, 22.6094, 20.9375, 22.0625,\n",
+            "         18.2812, 17.9062],\n",
+            "        [20.2812, 19.8281, 24.4531, 27.9531, 24.5000, 24.2969, 23.2812, 23.4219,\n",
+            "         20.0000, 18.5781],\n",
+            "        [19.0938, 20.1562, 19.7969, 20.7969, 20.6719, 26.4219, 19.3906, 24.5469,\n",
+            "         19.4062, 19.6250],\n",
+            "        [19.2969, 18.9219, 21.3750, 24.2812, 24.7969, 23.5625, 20.6250, 21.2500,\n",
+            "         20.0156, 18.7500],\n",
+            "        [20.5156, 21.4688, 24.2969, 22.0781, 19.9844, 23.2812, 31.7969, 20.0000,\n",
+            "         21.0781, 20.4688],\n",
+            "        [18.4531, 19.1406, 22.2500, 21.6406, 19.5625, 26.5312, 20.3125, 21.9531,\n",
+            "         18.8438, 18.6875],\n",
+            "        [19.0781, 20.9219, 23.7500, 26.1719, 21.7969, 23.7031, 25.7031, 21.3594,\n",
+            "         20.2500, 19.6094],\n",
+            "        [18.1094, 25.9688, 19.8438, 18.4844, 19.1562, 19.4375, 18.8594, 18.5781,\n",
+            "         18.3125, 25.0156],\n",
+            "        [20.0625, 19.9375, 23.2500, 22.0938, 29.6406, 24.8125, 22.1094, 24.4688,\n",
+            "         18.5625, 18.8594],\n",
+            "        [20.2344, 21.3594, 22.8906, 25.6875, 25.1094, 24.8594, 23.4219, 24.0000,\n",
+            "         21.6406, 20.7812],\n",
+            "        [16.6406, 17.8281, 20.9531, 21.2344, 22.2812, 21.9375, 18.8594, 20.6406,\n",
+            "         17.1406, 17.4531],\n",
+            "        [18.5938, 19.6562, 22.0938, 20.5938, 28.2500, 22.7500, 18.6406, 24.2812,\n",
+            "         18.1875, 19.0156],\n",
+            "        [17.3438, 19.3750, 21.2031, 26.7344, 19.0469, 22.2969, 19.5938, 20.7500,\n",
+            "         19.5312, 17.8438],\n",
+            "        [18.9688, 19.2500, 21.7344, 21.7344, 23.3125, 22.6250, 20.4062, 28.2812,\n",
+            "         18.5469, 19.0781],\n",
+            "        [21.1094, 22.0781, 21.3438, 19.5781, 18.4844, 19.8906, 18.2031, 19.3438,\n",
+            "         26.0781, 20.7031],\n",
+            "        [21.3906, 21.2031, 25.2969, 26.2969, 29.2500, 25.8125, 23.8438, 24.8281,\n",
+            "         21.4688, 19.8281],\n",
+            "        [18.3750, 20.1250, 19.8750, 20.0938, 20.3125, 20.8594, 16.9375, 27.7500,\n",
+            "         18.8750, 19.1875],\n",
+            "        [20.9219, 20.6875, 19.9531, 18.3906, 18.2344, 19.0781, 17.5469, 18.3125,\n",
+            "         25.5312, 17.9531],\n",
+            "        [27.2812, 21.1406, 23.3125, 18.5156, 19.7812, 20.0000, 19.3750, 20.3281,\n",
+            "         22.8594, 19.7969],\n",
+            "        [20.0156, 20.0000, 23.7500, 25.9219, 21.3750, 25.2812, 20.7969, 22.3438,\n",
+            "         21.1406, 18.9531],\n",
+            "        [20.0625, 21.5625, 21.5625, 21.2188, 20.9062, 22.2344, 17.7031, 28.8125,\n",
+            "         20.2188, 19.7812],\n",
+            "        [19.4844, 20.6094, 23.8750, 22.6562, 23.4688, 24.9219, 27.4375, 23.4375,\n",
+            "         18.8906, 19.4062],\n",
+            "        [27.7031, 21.8750, 22.4844, 20.1094, 19.7969, 20.6875, 19.8438, 21.1250,\n",
+            "         23.1562, 21.6719],\n",
+            "        [19.9844, 20.3750, 22.3750, 23.3750, 21.5781, 27.7812, 20.9688, 22.9531,\n",
+            "         19.7969, 19.8281],\n",
+            "        [18.7812, 20.0156, 21.5469, 23.7344, 30.3125, 23.9375, 20.0469, 22.2969,\n",
+            "         18.7031, 19.8125]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[2],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [3],\n",
+            "        [9],\n",
+            "        [4],\n",
+            "        [3],\n",
+            "        [5],\n",
+            "        [4],\n",
+            "        [6],\n",
+            "        [5],\n",
+            "        [3],\n",
+            "        [1],\n",
+            "        [4],\n",
+            "        [3],\n",
+            "        [4],\n",
+            "        [4],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [4],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [0],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [0],\n",
+            "        [5],\n",
+            "        [4]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[2, 2, 2, 9, 7, 3, 9, 4, 3, 5, 4, 6, 5, 3, 1, 4, 3, 4, 4, 3, 7, 8, 4, 7,\n",
+            "         8, 0, 3, 7, 6, 0, 5, 4],\n",
+            "        [6, 0, 7, 1, 5, 5, 1, 2, 4, 7, 3, 2, 2, 6, 9, 5, 4, 5, 7, 5, 4, 1, 3, 5,\n",
+            "         0, 2, 5, 5, 5, 8, 3, 5],\n",
+            "        [3, 6, 5, 8, 4, 2, 8, 5, 2, 3, 5, 5, 7, 2, 2, 7, 5, 3, 5, 2, 5, 2, 5, 4,\n",
+            "         1, 8, 2, 2, 2, 2, 7, 3],\n",
+            "        [5, 8, 1, 2, 3, 7, 0, 7, 5, 4, 2, 3, 3, 5, 5, 2, 7, 2, 2, 7, 3, 0, 2, 1,\n",
+            "         2, 1, 7, 1, 4, 1, 2, 7],\n",
+            "        [4, 9, 9, 7, 1, 4, 7, 3, 7, 1, 7, 1, 6, 4, 4, 6, 6, 7, 3, 6, 2, 9, 7, 3,\n",
+            "         5, 7, 4, 3, 7, 9, 4, 2]], device='cuda:0')\n",
+            "tensor([8, 6, 8, 5, 5, 9, 9, 9, 5, 0, 1, 0, 8, 1, 1, 8, 0, 2, 2, 0, 4, 6, 5, 4,\n",
+            "        9, 4, 7, 9, 9, 4, 5, 6], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[22.3125, 22.7344, 19.7500, 19.9062, 18.2500, 20.3125, 19.0312, 19.0938,\n",
+            "         23.4219, 20.2500],\n",
+            "        [17.9844, 19.4844, 23.9688, 22.7812, 23.3750, 22.4688, 26.0938, 20.3281,\n",
+            "         18.7188, 18.2500],\n",
+            "        [23.0000, 22.5781, 22.4531, 19.3438, 21.0312, 20.5000, 19.2188, 21.4844,\n",
+            "         29.6406, 23.3906],\n",
+            "        [20.5000, 20.7812, 21.7812, 24.3438, 23.2188, 27.3750, 21.5625, 24.0938,\n",
+            "         19.7031, 19.9219],\n",
+            "        [18.2500, 17.7500, 19.5625, 20.3750, 18.9375, 25.9062, 18.7500, 20.5312,\n",
+            "         18.1719, 18.5156],\n",
+            "        [20.7344, 23.6562, 20.5781, 19.2344, 20.8125, 19.8125, 18.7031, 21.5156,\n",
+            "         20.6094, 27.2188],\n",
+            "        [19.0781, 23.3906, 19.3281, 18.4375, 18.5781, 19.4375, 17.1875, 20.7812,\n",
+            "         20.7656, 26.4844],\n",
+            "        [18.3594, 22.9062, 18.9688, 17.9219, 18.1719, 18.6406, 18.1719, 19.8906,\n",
+            "         19.9844, 23.9219],\n",
+            "        [18.9688, 19.0625, 20.0156, 21.6094, 23.2500, 23.6875, 19.9062, 22.0469,\n",
+            "         19.0312, 17.8281],\n",
+            "        [29.2344, 22.9844, 25.2031, 21.3438, 20.4844, 21.6406, 21.8281, 21.8906,\n",
+            "         23.1875, 20.8750],\n",
+            "        [18.8750, 25.8594, 20.7656, 19.2344, 18.1875, 19.8438, 20.2500, 19.1875,\n",
+            "         18.0312, 21.5000],\n",
+            "        [23.5781, 21.8594, 23.3594, 22.5312, 21.6250, 22.4688, 21.6719, 22.7031,\n",
+            "         22.3281, 20.4844],\n",
+            "        [21.5156, 21.0469, 21.4531, 19.9688, 20.4062, 20.1094, 18.5625, 20.3438,\n",
+            "         26.9844, 21.6562],\n",
+            "        [18.5938, 25.5625, 19.9062, 19.2500, 19.3906, 20.9062, 19.0625, 18.6250,\n",
+            "         18.9219, 21.8906],\n",
+            "        [18.5156, 24.7500, 19.5312, 16.9531, 17.7969, 18.9688, 16.5156, 16.4688,\n",
+            "         18.0781, 20.7500],\n",
+            "        [18.8438, 18.7344, 19.4219, 17.7188, 19.1875, 17.5156, 17.5938, 17.9531,\n",
+            "         27.0312, 18.0625],\n",
+            "        [28.0469, 22.7812, 22.6250, 19.9062, 19.4688, 20.5938, 20.5938, 20.5469,\n",
+            "         25.4844, 21.8125],\n",
+            "        [23.0469, 21.0625, 28.2031, 22.2188, 23.4844, 23.4375, 23.0625, 24.0781,\n",
+            "         21.9375, 21.0469],\n",
+            "        [21.4844, 20.5312, 28.4219, 20.6875, 23.9375, 21.9531, 20.9062, 22.6250,\n",
+            "         19.4531, 19.7500],\n",
+            "        [28.4531, 22.5000, 28.0625, 21.7344, 21.7812, 21.4375, 23.5938, 22.3281,\n",
+            "         24.3750, 21.2969],\n",
+            "        [19.5312, 20.3906, 21.5156, 20.7812, 25.8750, 21.5625, 20.5938, 22.9062,\n",
+            "         19.5312, 18.9531],\n",
+            "        [20.1406, 20.4531, 20.3125, 19.6562, 19.5000, 20.3125, 19.9062, 19.9375,\n",
+            "         20.5000, 19.7500],\n",
+            "        [20.7812, 19.7812, 22.4531, 22.0000, 21.9219, 27.1875, 21.3281, 23.6250,\n",
+            "         19.5312, 19.9844],\n",
+            "        [18.5469, 18.6250, 21.2812, 20.6250, 27.5625, 22.8906, 21.4062, 20.0781,\n",
+            "         17.8750, 17.7656],\n",
+            "        [16.9531, 22.4688, 18.8125, 17.6562, 17.5625, 17.8594, 16.3906, 18.3281,\n",
+            "         18.9844, 26.9062],\n",
+            "        [20.4062, 20.6094, 22.3906, 20.2969, 28.9688, 22.0469, 18.2969, 23.3438,\n",
+            "         20.3125, 20.0938],\n",
+            "        [19.3438, 19.3906, 20.4062, 20.2344, 21.4531, 21.4219, 18.5312, 27.2344,\n",
+            "         18.3594, 19.1094],\n",
+            "        [18.8906, 22.0781, 18.4375, 18.2812, 18.0625, 18.5938, 18.2344, 18.4219,\n",
+            "         20.6562, 25.4688],\n",
+            "        [18.9531, 20.6562, 19.2969, 18.3906, 17.1094, 18.5938, 18.0938, 18.3750,\n",
+            "         19.8750, 21.1094],\n",
+            "        [19.7188, 20.6406, 22.2031, 20.4844, 29.9219, 22.6406, 19.5781, 23.0781,\n",
+            "         18.8125, 19.6719],\n",
+            "        [19.5625, 19.8750, 21.6250, 22.4219, 19.8438, 27.7812, 21.1094, 21.0000,\n",
+            "         19.4531, 19.7188],\n",
+            "        [20.1719, 20.2656, 22.9531, 21.1562, 21.1094, 21.5312, 23.6875, 21.3594,\n",
+            "         20.7656, 18.8906]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[8],\n",
+            "        [6],\n",
+            "        [8],\n",
+            "        [5],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [9],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [0],\n",
+            "        [1],\n",
+            "        [0],\n",
+            "        [8],\n",
+            "        [1],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [0],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [0],\n",
+            "        [4],\n",
+            "        [8],\n",
+            "        [5],\n",
+            "        [4],\n",
+            "        [9],\n",
+            "        [4],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [9],\n",
+            "        [4],\n",
+            "        [5],\n",
+            "        [6]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[8, 6, 8, 5, 5, 9, 9, 9, 5, 0, 1, 0, 8, 1, 1, 8, 0, 2, 2, 0, 4, 8, 5, 4,\n",
+            "         9, 4, 7, 9, 9, 4, 5, 6],\n",
+            "        [1, 2, 9, 3, 7, 1, 1, 1, 4, 2, 9, 2, 9, 9, 9, 2, 8, 7, 4, 2, 7, 1, 7, 5,\n",
+            "         1, 7, 4, 1, 1, 7, 3, 2],\n",
+            "        [0, 4, 0, 7, 3, 7, 7, 8, 7, 8, 2, 7, 0, 5, 2, 4, 1, 4, 7, 8, 5, 5, 2, 6,\n",
+            "         8, 2, 5, 8, 8, 5, 2, 5],\n",
+            "        [5, 3, 1, 4, 2, 4, 8, 7, 3, 1, 6, 3, 2, 2, 5, 0, 2, 5, 5, 6, 2, 2, 3, 2,\n",
+            "         2, 5, 2, 0, 2, 2, 6, 7],\n",
+            "        [9, 5, 2, 2, 4, 0, 5, 2, 2, 7, 5, 5, 1, 4, 0, 1, 9, 6, 0, 1, 3, 0, 4, 3,\n",
+            "         7, 1, 3, 5, 0, 1, 7, 3]], device='cuda:0')\n",
+            "tensor([6, 1, 5, 3, 8, 9, 5, 8, 5, 7, 0, 7, 0, 5, 0, 0, 4, 6, 9, 0, 9, 5, 6, 6,\n",
+            "        6, 2, 9, 0, 1, 7, 6, 7], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[19.3125, 20.2500, 20.3906, 20.9531, 20.9844, 21.1719, 23.7500, 20.6250,\n",
+            "         20.7656, 19.0781],\n",
+            "        [16.0000, 24.0156, 17.7031, 15.3438, 17.1406, 17.5625, 15.6016, 15.5078,\n",
+            "         16.3125, 21.9062],\n",
+            "        [20.5469, 20.2500, 21.7188, 22.7812, 20.4688, 25.4844, 20.6250, 25.2500,\n",
+            "         20.1719, 19.5000],\n",
+            "        [17.5625, 18.9219, 22.6094, 23.0156, 21.0469, 21.5156, 21.4219, 20.2812,\n",
+            "         19.3438, 17.6562],\n",
+            "        [20.2188, 19.3594, 19.5938, 18.0781, 18.1250, 18.4531, 17.5625, 18.7344,\n",
+            "         25.4219, 17.8281],\n",
+            "        [18.4062, 21.8281, 19.6406, 19.0000, 17.1094, 18.5781, 18.1875, 19.2969,\n",
+            "         20.2500, 24.1406],\n",
+            "        [20.0625, 20.4688, 21.1719, 21.0469, 21.9375, 22.8125, 17.4688, 28.1094,\n",
+            "         20.3906, 20.7500],\n",
+            "        [22.6250, 22.7500, 21.3281, 19.2812, 19.3281, 19.8750, 19.1250, 19.5938,\n",
+            "         27.1562, 21.7031],\n",
+            "        [20.2188, 20.9688, 24.6406, 24.3594, 22.4219, 26.4219, 20.7500, 25.4219,\n",
+            "         19.5000, 20.6250],\n",
+            "        [17.7344, 19.6250, 19.0312, 18.1406, 18.3438, 19.5625, 15.7422, 25.4219,\n",
+            "         17.8906, 18.9375],\n",
+            "        [25.0469, 20.5156, 21.8750, 19.1875, 18.6250, 19.5625, 20.7188, 18.8594,\n",
+            "         21.3750, 18.4688],\n",
+            "        [18.8906, 20.0781, 20.5781, 20.4688, 20.7344, 21.0625, 17.2656, 27.7500,\n",
+            "         18.4375, 19.9688],\n",
+            "        [27.2188, 21.4375, 23.2500, 18.7969, 18.9844, 18.8438, 18.0781, 19.5312,\n",
+            "         21.0312, 20.0469],\n",
+            "        [19.5000, 19.8438, 21.1250, 21.8438, 21.5625, 26.3281, 20.0625, 21.9844,\n",
+            "         19.2188, 19.9844],\n",
+            "        [27.7031, 23.5000, 24.8750, 21.1875, 22.4062, 22.0312, 19.6094, 23.3125,\n",
+            "         24.7500, 23.3438],\n",
+            "        [27.5625, 20.1250, 24.1875, 20.2969, 19.3125, 19.7656, 20.5312, 19.2500,\n",
+            "         22.0469, 17.4219],\n",
+            "        [18.8594, 19.5625, 20.3281, 18.4688, 27.0469, 20.6875, 17.6562, 21.6875,\n",
+            "         19.9062, 19.7188],\n",
+            "        [17.6719, 18.9219, 22.3281, 23.6562, 21.5625, 23.4375, 26.8594, 19.2344,\n",
+            "         18.1406, 17.9688],\n",
+            "        [20.6250, 23.8125, 20.6250, 19.8438, 19.5156, 19.9688, 19.4844, 21.0469,\n",
+            "         21.9844, 28.8438],\n",
+            "        [24.9688, 19.9062, 24.0312, 20.8125, 22.3438, 20.6250, 20.4531, 23.5469,\n",
+            "         22.0469, 20.7500],\n",
+            "        [18.9844, 25.0625, 20.1094, 19.2812, 19.2500, 20.5625, 19.4844, 19.9531,\n",
+            "         19.3281, 26.4375],\n",
+            "        [20.0000, 20.8594, 21.9531, 23.0469, 19.4531, 27.9844, 21.7656, 21.6250,\n",
+            "         20.0156, 19.3594],\n",
+            "        [20.5000, 20.8906, 20.5938, 20.0625, 19.7188, 20.0625, 21.7344, 19.8438,\n",
+            "         20.5469, 20.0312],\n",
+            "        [16.9219, 17.8281, 21.7344, 21.5625, 20.5000, 20.5469, 24.4688, 19.0312,\n",
+            "         17.5156, 17.4062],\n",
+            "        [20.0625, 21.2031, 25.0781, 22.6875, 21.8438, 23.1875, 29.3438, 21.7500,\n",
+            "         21.1719, 20.5000],\n",
+            "        [24.6719, 22.8906, 27.7500, 24.4688, 24.9219, 23.9375, 25.0781, 24.5469,\n",
+            "         22.4375, 22.3281],\n",
+            "        [22.0469, 24.8438, 21.3906, 20.7500, 21.6875, 21.1875, 19.9531, 23.4688,\n",
+            "         23.0000, 28.2500],\n",
+            "        [26.7969, 21.6250, 23.3125, 20.4688, 20.6562, 20.7500, 20.4219, 20.8281,\n",
+            "         21.6875, 20.9688],\n",
+            "        [20.0625, 27.9062, 21.5938, 20.2031, 18.1875, 21.8750, 20.1406, 20.4375,\n",
+            "         21.0312, 25.1250],\n",
+            "        [18.6094, 19.3281, 19.2031, 17.8594, 19.1406, 19.4531, 15.2109, 26.0156,\n",
+            "         17.7500, 19.1719],\n",
+            "        [17.5312, 18.6562, 22.6250, 20.8906, 20.1094, 21.1250, 24.0938, 20.2031,\n",
+            "         18.5469, 17.4844],\n",
+            "        [18.7812, 19.3906, 20.9688, 21.0156, 19.8906, 21.9844, 15.4922, 29.4688,\n",
+            "         18.8906, 18.8594]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[6],\n",
+            "        [1],\n",
+            "        [5],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [5],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [5],\n",
+            "        [0],\n",
+            "        [0],\n",
+            "        [4],\n",
+            "        [6],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [6],\n",
+            "        [6],\n",
+            "        [6],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [7]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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Ri160md+L9IGXLlTsD5L0BV/wBbjPfe6DBz7wgfjKr/xK3P3ud8dP/dRP4f73v39337gp1U/8xE/gwz/8w/GFX/iFeM973uOfRzziEbj73e/u+1K/4Q1vwOnpKb7hG76hU32f+9znXjdvv/3bv413vvOdeO5zn7uy451lncS7Ko9/8Ad/cE32eNb0dV/3dd35a1/7WgDAP/tn/6y7/rznPQ8A8HM/93MAgF/8xV/EPM8XG319EKcLFfuDJP3QD/0QPv7jPx7TNOF+97sfPuETPmG1v8rWplRve9vb8Fd/9Ve4733vu/ncP/uzPwMQm1Pl7QQA4D73uQ/uda97XTNvpu7f6OZf74s83pE0btRlG3iNG3Z91Ed9FO55z3t2G30B6429PuIjPuIuze9FuvPSBUB+kKS/+3f/7nVj87Y2pWqt4b73vS9uvfXWzd/c5z73udPyeKPpAz2PN9988+b1i1XEP/TTBUB+iKeHPvSheMMb3oDP/uzPPtjRgdic6m1ve1u32da73/3ulSd56x0A8Ja3vAVf8AVfcPC+Q4DyvsjjnZlsA6+3ve1tvj0sAPzpn/4p3vve93YbfQGysVdmoX/+53/+Ps3vRbrxdGGD/BBPX/7lX45lWfDt3/7tq+/mefaNuL7gC74Au90OP/ADP9BtrPWyl73suu/41E/9VDzkIQ/By172stXGXvlZhzb/uqvyeJYwnxtJt9xyy+Z7v/d7vxcA8LjHPQ6AeNGnacLLX/7y7r5xg7CL9IGbLhjkh3h65CMfiWc+85l46Utfije/+c14zGMeg91uh7e97W34iZ/4CXz/938/nvzkJ+M+97kPvumbvgkvfelL8fjHPx633HILfvu3fxuve93r8JEf+ZHXfEcpBS9/+cvxhCc8AZ/8yZ+Mr/7qr8ZHf/RH4/d///fxu7/7u/iFX/gFALLxFQA8+9nPxmMf+1jUWvGVX/mVd1kezxLmcyPp4Q9/OJ72tKfhh3/4h/He974Xj3zkI/Hrv/7r+NEf/VE86UlPwqMf/WgAwP3udz885znPwfd8z/fgiU98Ir7oi74Iv/M7v+P5vVDRPwjS+9mLfpGukyzM501vetM177vWplTMzD/8wz/Mj3jEI/jmm2/me9zjHvx3/s7f4X/+z/85v+td7/J7lmXhF7/4xfzRH/3RfPPNN/OjHvUofstb3sIPetCDrhnmY+lXfuVX+Au/8Av5Hve4B9/tbnfjhz3sYfwDP/AD/v08z/wN3/ANfJ/73IeJaBXyc2fmkfnOC/N597vfvfrNfr/nF7/4xfyQhzyEd7sdP/CBD+Rv/uZv5uPj4+6+eZ75X/7Lf8kf9VEfxTfffDN//ud/Pr/1rW/le9/73vx1X/d1183bRXr/posFcy/SRXofp/e+9724173uhZe85CV4wQte8P7OzkW6RrqwQV6ki3QXpkMbfQHAox71qPdtZi7SudOFDfIiXaS7MP2H//Af8MpXvtKnMP7Kr/wK/v2///d4zGMeg8/+7M9+f2fvIl0nXQDkRbpId2F62MMehmma8J3f+Z3467/+a3fcvOQlL3l/Z+0inSFd2CAv0kW6SBfpQLqwQV6ki3SRLtKBdAGQF+kiXaSLdCBdAORFukgX6SIdSGd20nzjN3wNmBkM1r/A0hoaM+Y2Y78smNuCuc04bXLc0MAAQECpBVQKap2w202otaBO8reUAiLy57fW0Fg+c1tkWhnrcwqh1IJpmrDb7TBNkxwfTdhNO9Ra5V22YjURoBMWGhDPgv41Eyxz+gDUOH0HkH4AoDQ5tme1pYEbY15m7Pcz5nnB6bzH8X6P0/0e+3nG6bzHyTJjXhYsbcEpL9i3BbOWceYGq7EZDQuAVgAmKTcXAgoBpUh5UrkAoDD7KTGDGChs5/oTJi8PkL5jAE0vNgblumg2XTD/CECRPDERuJKfo+g7hlkilica6jTywH6OZq+Je6OgpHXipQXnuiCSQ72H/ET/Hdrcy7ZYe/cmebLnFPJXkj3Xi8hggvQNu5JN+/mRJn/WMeSH6w8O/PWss73ZH4v8/vxae422jb066gzohAmprCC/VZqdUPTewvJt39QceUw10lhzZ/nOMpbrxVKByBIBXIoc1wmo0gdE1pIM2r1EXqUAOtnglM8/esX/wFnSmQGSXZjifOu6ftuVmSjqwb7n/IxUM5yq1eTVGl8S6TWOD+I5zAxuDKravRTM4xUpvyHNXQYpPRPace1rGtpxrAf2MvQCkt/3geIV6/o4wQchADeeyfyMO/tnqSfe6XXo5baKuEiH0nmb+M6rzY03b2XmBmVwK50TIOXtDkSKYAFSes8AWgbdNn458DmABII6TiXAsesEw7J4Jw/XGjcQU8c6AyB7YIZdX41k3F8fh+X0kXf2dcL+HPtZ1MZKXHwkWKcbwaprygWlPzxepoT+/ZvzM3m8cCidNdPr8aPPEwAQbzxuIxN3uFNsgeP2Q7eKd7YiH8hkfvWYja3v7shA5oxx48fnqcOkkGxh1JmfcahsB5N1LIrfMAcTW436N57ODJCtJabkINmCNTELjU6MjZnBZJ0sAUgC0MaMkoEQAjgNI+gE+LpssOShcRO1vDUQERo38IKkhmaGmplr+ipR/sJQnYD9feSgLip2ZrdN66FZXtX0YPnPYL9mn4fBMcvNVjrUlc8m4zTct9XrSNpt6yXXSmcR8sNFz+Nl/1Lt2NcU+TsKkqO6ufG8u4xf5uofR6Xxuzuaic5MoReG02v8sBvXV5itF67VxNs/vNZ7sXHTMIoYSG7ee2Pp7ACpICCvVRBTxmbgxCw2NPuOtZY6oICCaWugJrYdAzZ7jz9Tn8+pJRgNjQuW1kD+WbC0Ampi8yzMKEUrJ+mRgYU8XugYJDs4yrmBowFl4wSYTe63Olhaw5LqxYDT32sDxwHWeCNpSwQyWK7MTfnvIXka/97R7ObecsOsB4NdYOP8It1Y2gTHsWJpfXqNQW6TBY9/P8DTmQFyWRZXHY3/NQXEpTW0pmCmoOAOF7A7YKDA2EpR4GlAIxRzpgDBCPXejnGqfid/KAByIVBZACLUpGonS7P90zHIdKEDSNoAyMwii3V2ZZoC+IxlWdDagrYsUh8tAX1rYQpwZjwypT5dD5u2MIMOfLr7UrUQ54uJsxJcXbkhWR5/kMHxAFhel6leE+kP/Obc6YOk595ZaRSI8ZiGm7Ong+N0rDW+Fgsezw+RwlUakTX9iKH9fQuVbzydHSBbzyBZ1eCGFl7nxgkMWqjYHKq0sUIwQE0KxWmPZAHd5iBnDFIqnMFMKAxQIyxtAS1aSerFalxQuAg7TcDrLLJrRcTFhFYOkObZVcboDLIhNHcHSGWPS9MBI8Cxt5kGi+yQImXMtZ8todoQJgdCPkyuerCUevHz1TM5ZULaeyVq1B/az/r8DsKZbbsp/ytx3wTlrA3QdkG1TOPz7MqYvVVaMSeO9/lTqL81FS2X5Wydc0CXLQAZMCH7kDo7v+eBjUccftfW6NnVKXl9js+Q7ER9XAvX2H9zALQyqK0b7UBKN7qdPz2jE6KhUm8AK88MkPt539kOBRsExBZumFNYzgIFCbu7MQq06QhYGhwsChhERcHMVHkFFwCLhflYtTBAxjBJALcqqC6topCEDQkGrEEy91nqOmuicxbqMgKknpZ0zBoGw62hLQ3zsmBuDfM8Y2kLlmUR4OxAs3V1G+XLgAlvdwMxHqUxyXQGRwfzuA0EGs712E5cWPVdjZMsG2IpUFL/204uD6HQBjheC7BydjzTY+ZzJWx05rOnKN/md/6HrvnY9TByDZAk6tr4uj/NbZ+Oc3+Ms+thQU8aNgefQ+BI+ncrzxmsVFDMCStEyb7LoHWofvJJuof1OW5vzMiaOsmh+jtnOjNAnpyeAqkxMotcWOxui4LlAjl3FbuQspAiZVuAwg0FBYUrUBbpvgRlXGZ/VLVdKJcyCwIxYWmMiobSGmpbMLciMZBEDrgGjhEyR2vJ0U4rIMTxaYh4QCSANPBhrwhniG1JLHLROMe2YFmamx/cPqu2zkP+2Z5M5Gi0ddokUzycQwcMBAOggUUyszBxlncyAyhQ1qymEiQn2cbo3GH42HlME0DUY3cL5WsD45EM93+1DLnwDPLOuyIl18A/pHrpv0wdeixu7rcZbJCgivtnjtjefd83ujdi/64MhakyE1j6pVxvKyEZ65ck9pBKykv8TrITD3EFyB7j74vKZ20IZgNJxCC5EmUDuJynoQUzOG4BdGZApMI7Eo5zpjMD5DzP3WgVnmxg4UUCm80micWDshsxiAnUeiFrXCQW1MqSVGwD30XB0t5jrJmIUEpDWypKa1haQW0FdWka0KsfJIAcA5dzy2trO6Nsie1s2CBHgDQvdWvsTFFU7VnAMTHHlSffQZhz5lb55PzODoV6UDQAzMBYUtUbKAJi++36dcsPJaHKFrTt4Gg24I382a/Z+AaSQyzVqd4Tv+k7MHegl74bbMox3sX16KiraurIWr4/TnAgyS8PcB04IKwoywFEPsRuxk5sAu/XI0Ik/hhU5rP8++E93TklsCT/nrzOx4qkeL9lLQF3tEXUeILw7nrncU6DQZc8PxtGHu8LCRTHvkHD9cMNeDCdGSBP96epsH1YzmLea0BUXT1mVYEl2p5czAoYBGGQxAWUIvxZZ9+wMlJnj/pbazxqhKrebyqEushMnUIJILUBVqp2qscMhACENS7cfdfP+AggAof8whikOaKazDBamgD94uyxuc0ye/cVJkRmTXZsUBgbI0mpY4d9OI5Lvo6EL1ofxeonsx6NBKBCOihJfqkApjJ1fTi9f5Rzd6yNIJl+yFqx1rk4FYgjw2nmSlzLQt/1tZw/hs/e2eyH19wXRhuC4aVOeDAIETZqZs1JvXD5sKtE9q+lmRNIpt901Zh8A31e8oPjx0T62AyQRDJLCXIufVLLPI40eswu/+Ib6NDTb2Mvoz1bSAhJu5ig6rNy2foaS4NDBkYDawDcMEye9ilZ8ZtzpjMD5PHpiR8bWLG+3MCRwWgENGp6bpkjrQQDRWV6hUC6jzP5FKGIrZSQodaBMbychFICAEsJ1ijtXRJAyrkfO8eyBzM6hqMMUkCwbQKk5IA61mVOKgFAYdZhKtCBxJgx4OWMT2rwNOq5vTleZRnwP4Q0tRAb4Kg3F+8LcpDlifVLZoAXVuopH268sqd3jGTsi55fPWjo8z4ia/otF6CfMjgMFB2r3KiTgTjccLICZ8C6w2kjR10dUAeS/uKtUak7YawqeAsQDBA7tQLu5JSprFa/dPi9jcOk2LVl1FlnWrfnGXMsKkgtlX+UhWEAyjcQN3HuKshwbnRHzFRxtKq4M6WzM8h5jzEPDHRzUJkgjhNK91hvbdr5IB2PWD+teYNIE7cEGMnRA6QjLXNzhW9QqzGAJWmHowSQXmsJIBUYHSwhAKmF7tkjOWPz8btpm7McNyyurRtAso64bltdNRp38uIVfqCn0/CVQxoPAKn14Azb6yxhGisT0JGYWRl7kRpjPpCRPIo7W0yoogMP2wC0KoS3ov/EGWTqqNQdp+cnUJa6s07kT0NG9vyuAN0YRHKxegDm9G8+GFEaDk7b2B2V7v12rFa7RlZv8YRxrDTzUzd45ZdSeqXWq5udCpycpNFTm2+cZz02n7zQ2SMj0LIDxaFMZpMEFBsymA+DA+XDQFF7VZcde9/m6IuNyrl+OpcNcnwPU3zsvI3X7AujQtZaBaBWomLsdwE3MZvG3xuA4tYw7QSkOlgGA2MhwpZKMrlcm0FS8l47aAI9e2QF2gSQlp2YUcTm70FMSdQBpbM7pmMbgNNg26UNQLDTDIx+PnygtSRgmYAm14V1KDLvox53gLOR8lfc1yGnurRE+WdpUHXAtMYEgASWpiLGc1LX0fw78CABZld9AypRrrVcINK8ifxa+63TVkNtPK0rNBCZteu8cVOcr4fTKNjmNPIObLRPpIHGRx5Xs5HUb+rquRuAaCzZoZcOAuvfJ4MEUf8dbQjJ1vO8rrKsRX2sXnsD6RxhPgNAaidy04OpyMWup/NG3uFakx8TU68TdgNNqNj53I6B5A0Fd8OaASD0r7MlXTFotLt5SlMLMzCah3sVRpPnlw9Duv0cWob8OAP9bFftOUHqWApK3TuGTrACwwSa/h311wZzLCyQP9tpxd6bxrT8spy6AZr7S7kiMiu3r5MgD18pe/QRLViOZoSovz+nFbPI32FdlTTekPPRlW/V1AfTIeujty6tmjJ+23XuAe0tjxYpZvduFSYDIW18N9og0zG7Cp4APAtychCvK3Eo8PaYEyXSSAlu/b2UD8b8+w0bSLiF1zcIkucKFLeXdR3AwBBJmEoIQmYdPvI0ACQcENo5M0jaY67JIBlAnoa4AZIGlIXEIEwAGondc2SQnIFxY6mz7tmeXeq+ywLDEPaViu92Wr4WOOYRcAsU0/eJfHu+8nn3XTo/dJ0P3XCGEdm0737cOgyOuSzdV5TAEYgOm/tpKjcIPoiMTt9cfThwvgmSG+k84Jiffe57DmU2owqzh2MBJPJq51bnqlFtA2B67qH2xnCMvt5ze1OuxPz6azREh/v2HAfd/KBU9jG/mynV6soGef50rrnY8tKs/liWJCMGlJ5V7vPoUqbg2INnPCkDZPws3WEqmx8HoHm7JIDsVG0A3NpqhO/Wp/POLcfeJhx/1SrXh4l0PYczxjr2OTs2FTzD5EbPpVGavA4FGYZSdMLo3xnb1eeOEwy2ZHlMFn7mZYMMAl2bJYz3cow2R97KoJxbC67i97Rjd/2M4p1mjihRVK+7yAsPzDLs1/aAIWQxFSLyPVgJ+rTRF8d7abhp/X1gRB9nyj1CtZR3teMRepa9Ar0MkiCMYOlrjw6/s55nNl0GwMROOiS/nByLIc+k/X1dVckGCQkFDAbJuXW0KFpOzbcNiFmMOP9jQpL6od91lhFO09mXO/O/3F3ojMPWifRkZFAuyYhKz+K05lM9QPpZZ9PiDsy8MU29JtIBltVh1lV9emaAZJSRuxweGmApxGSzzjKuyWNzSaNso41PhIBSr1SJy/Xp+eF0PGTHJcjAdqh3b5NtyeHhOMrCa3Jo2esakvsfa9aH4kbHRe6kfSdel8nO06DZ1eEwgHKSOgMht2NsDHKpTAGOWzVF3i9tQgMPX4eUbMuLNDVtNGJGfeq6DRX0TrXOOHsYJLPpJDN2b7aUh7Vtc+ypGG5Isj0WZ2MQIhvKSgz6PTjqT5NfwR1NQAeUWQ7HOs59/azpBgByuK6FdkHy0nA3Ao4gHo3NnVxmIN46NnAMUAzhz0NFVtupSJiKhDZohi3KP4l8KL4Hu4D/JZiJAKFyB/6v+q8dZ7W6V7GHu7UOVTLSd1GnK7Dm9fWuXrLek4fV7vkd/EX+aATHdJ6zvXrMGhy7otLwlY/8FKEngyNpxABr6sGE5Q/O44M93Ya+3E6bU3c3sn+t/mWP2MKC/tqAOtYHaPydIeHQydRsxLYwQF7lHulBDopDfQ7gOaxBsVnG3Dugx9YXonzcFW0Fdkn8HOC8HeSiP8fLjhg8R3kYKnnsd44D5wDFnM4BkLkrDwLL8HUfc3X1/WFEjxDTa/ahxAxcPjrd1eSGtTNkMEgPGnrOCN79x9StKIPN9vH2CfyN8/wexnigdcX+d21h7TsMtp5ngDmqrvblpiCM0m+DxFb+1o/2juFrvCXHEx/4XZ5u7jclQHJkj17qRSTqgsRDXrZKLPmyQcqrzGWGvZ3GGsnL6JEFwXunTa2fhHQcEPxhfTff+H4NFtu9m+NerZt4qkZfMOtWGIDZ9J19DqamzkPt4JjwNr07R6WwCvhhXJHVuKIK18wPCNv/WBcdiGZx5nwTQC4fkXevk6H6OB9xarEsFOcEynMxyEPP7r/j1MTRF8XGQ8OvMADVIT61wec2e2a6y9+LzYoMsaN4L8XxJjPWR/lMl+FVq7TOtIO51xlbuVMter4pemTXc9XjnMcbq/WtRsrCYR0JZpMLSe3rfmCPCSvyQKLF2SLxHaivxo3utug1bscmrOIg1w/Oh+xytn6BZHA9ISWFB+X4ve5NPTB2wNml1KkzpCVP2sG+mUEjF5TSwciCigilqdTcDvy8szeS5mMEHCtrlC+H7vUpSwC6v30/UMV5e1zrpoT6CKb6eJ6vJVNcKZVlqJOUB0Jqm6xNjnk9B0jeoIrdi9m6quKfULk3lj3i7ZFqA+6uUaZRcoYfjfWYpciHLhUclobpgt+HVxBsEoI2XO78dl+SlAAsq7m+i212uDSoBEha1kOybNS2fEVcaLror47nyMMoGFTOi9p0RzPA2CV4+A2QgNKwWN+bW6jvLwk8cme1rHaFC83E8B5aH+uwnjgbh+QRqPMUNtqok/jxoTdslWu4usV0xh8kkQSi7OTlTkHbyhRl76QCUBMxacFi5T3DwGKB4Ojr230FKTN9G489kfXpcdfIIHPT+bXsxO1wYQ3ufgulSR0JILvpsRtpNeyfDUxW6cwAeei5Xol5RNBvRnbYcR09HZ95PnC05LWd/m48eENQ7ZNnwlnnbPnFqWeZrSsNes4sxx64lZsseCNrXuXXQdIemF5AGwU68CgvQn5eGiAisH8ExzUwbgJlAkf78kBLHEzs3cw+nutryoNJGnunzWwid+L176KrCdDnwHlO13PRzpauUfID4NgVW8/dyTEifTfgSXwMEXcAHyBBwSCH93BfzZttfChFDw8oovSdnVn+x+5ByfY9ajIBipH/DI9xlJ7Iw/O38n++RrwzVGwTntgoi71GOKnYaeRMk4s3AlVWx2cr02Fw7CrLcAEYvLccwAhlUSOA629zF7aG9DczQEld2/TWnrlMA6itSnSd5Mio7cIDS9IRvIssYKuboaNoZ3XTQ/p+7FRb2ehzqzVH+v7MDvQ91jHY7sltcWBEyR1i7CDDuNVf2xhr/KmHGOVWoo2/gxrrudgCSSt7Zotbz7AG8vt0kGN0JpYMNB2bHDzWa3Dk7hzd3fF3BKAs69bNu/J1p7FWpHec7jt7JsH6V8cmx1JyPu4HibEE50nnctJsvkQBccUkNHfOKkzFHnrLOLZ3ALm6z55rLWqCJi/qOsTGb4mTyp++6zhTYgt5V0UR0ihY95gRAex8xOzcW2+ktW402aClA5YL7wZosyJp5rVdR6HUNQxnRyS6ZtmMzYzHWDGbcYA69IprAaF9N35/R9Oh52Rgy/6Fg6wxnRtbzCE4RLbi/ka16lKC4qFOHfFA/a8AixK56Vq8h6v1AwMLUveLYtHwHVKX3coYrQ5Sng0SKQHkeG/qZDraibgHupyRUqzSubZ9tax011PlCMorW0EapViymdkiWe3BwHcAScIBgKT1kJ/Ouw7S1YgoBLY8U34uI2btWAC3s0h7DsfI5pMYUpY6o7M9OGUmqxObgDpmt0tDTN1W6qR73VadIGtvMzBEagsvdx4oMmjBZj6lkT9pBGtJXPVKOXJPtYFkAGX0c9ayb8leZjTbHTi/9UY7yFbafI4BIhIweszexo9GwEhldzC035V0j6MoQpYLxapSG0i6rp1DNmUDShruPiCBubMdHDFSHqj/yXhfvqfvx3LFVzOHyKG/UkGGiGQbF3UInyV710t3WMU2lcsWYkADuLB3IrZAVit11da1XkDoamQTHDO4mJBwKnZJGRlggX00ATIDRGKHzQARJjzcfb9Vw9tv00cblDL0/dwPo7niTN7H/NvDOl3r7GkTg/Uftl0kS39HMAmOQUOrOgSVPZsGmN3abOnl3Xx1SmsoKSCSrRqTPludue+e21Zb8nuDNYC6L53p3khnuVbH3irblr1v9bDxPNUBKTBmVunAm8TYjeXVRhWO71Nmt6IQ4paw4ubMcfp+le/rjtjU359ln/LBhikr5YQo6iBYqGlwMSKRDupkwebOHgd2PHba66TzA2Tm0tZJuO9I3AzgOC1iqcMgyx4066qIPx4OVMaGTAdpFIXRHF43MEHx2Z5tNgA/74WAkSEigMHU89wwzoJVoh2Du47J+eW9oASUgkYBzYBBaXmqXFFDyjA1/jXA7/pY3ncmhThBZx31fS2xC3uIxTrqINhNo3P8z15IgFDk3NcfpCGc50AHZXtd0k0MEKBdvAvt6GF1XSEHUv7JISSl4dj7KQWI+XcWotJBTT9TpXsuibMlI3wxLIz6laBw+z6tCWDawWBXNlmSNuU4hz2HPcSqH4IOVAJlWafVOECWl/wIGnloaGgZtMmeSEBDTBnmkoExGKJHMZCBI1CYdLtlTvdyyMAZ0w2q2PEiq/CobEaz1acJwiClNyrQUAdQtgeGVWh4U2MEWtF+W+jCsC6hc7apr/sCjxfAvJ7zHeVJHY7I1ehxxsWo3hvgZNrhYRsr8wB69TtlPliDN296bt/Km6C4KuzGL7wd2TtgMEgru/3URqX4nYF8gHm8x8TYc6pMy1aylvIF+PeqfAY77cB+gQf0Tg2vg2Y3dzzbrnwUO0PKYxtyl0RUf/qbiWNGDHc0+ZOwFk6rUuIYMPR5myq6D9JSWN/lOD0zpgGHNMeA1/v4bQAc30EH8mttZnOtx+rw2hoHjL4WYGF1lie70boQyKgVyUIz/mzq+yITYuV4XSchvfaMLb5K546D7AUVHkPV/JiVQXIfv13yZkDW3Xzo9Q6Tz2NkWQum40xHXXOvQNeJVhWko6xd71Vs+CgbPx6BITLTye+WA8kHlCHz1lnzd8PYHWpEfsnZ04jlbGUZiuZOHKtzZxNJmCnACoAzF9cSUn2LT2tQPQ0uSX/rnd/aOw0qhO79vfytpxesBwd07dfPCT5Pl4lWGOsyMyMa8j4CJ+efZOBkrH+X6yD/nroiBSDaQbYhDH2h1wZGcLR+QKlj+VtW5SXNPBkwcvd1f2Pu00OyLgvkiVcxUYDSb4thhINjDh6P/m8b7TEDBRFZE48fzq+TzscgU2240HJalkxHq6bgGG1EIG4AF1GweolPBYUyiQDMLfuEQStzBAnLaiDGLFnrTIcYZxec/tg9a2EB4vskhVuyoh1+NW7mmgsJdxcy/Nlk+WFe/9Y7DPUdJWegf1MHECHm3BUnqiN1IEKocAxwCaC0Z/ugNbpDlZH57B7OfYP8P0MHV5kIoj6WYBG5LP63C8fyi92dXVnHethCt40pHnTgOA+ELhIJ5Ozcy7gB+C7y6dxwzB9O6TzvmWHnW5lVG6Svl6kvcvbYzLYMgNNq/ckJ51XSFXxDwNLVvnjU/8IKthok4plhrmGPQebxN/oOwwHZlCC4YThtDBjtQSbksXgHx4vPlc4OkJsC3Ds2ouKTTbLrteq8SdJqwsV24uBIncBsDfreDtb5S3qf7xVjDwiQZM9LLoOVa6zBNKKyNEoWkt51mV/Zd1k/02fkm+N5fS/OVefKTtcOvVo04sBGSaIeujIP91vbmc2Itt6dcrFxPQ8grlbbFQN8e5cPBOljJbPOjrhGfTFW5UZfvXFhBMkxcc73gXsplddAcZBl/zFtPEO/i26rRwlooy+gq5/cBbpjSupmKkyYqay3BjjaHRvDCcyOB6LsS+yK4BoC9dfjhNLN8eUora6xcJKDdIstvN1SOS3P7oDtfiZXerAeMn8OkDzXTBpLuUNlZ4bsk237rkTBxctW3NO0zqsBYwIbUg84YgSxFwfemt1BW7Ax3ABlW5ZyD3lZQDDkZ0UqhndSV+tDfq1JrPfme1fsJZ0zDgsZouN0PCn34o0ec0iD7FxPvAGIKRdhF0rPtLLa820Q8sVa+0JIrWQA6Y87z2zXOXhVEb1Nspeh9CtnsQ7o16h7wgAqG9U7XszVkAqJTs1O14C+DS1fbpo3b6szdi2DMUJC+DeHOiEdxHxFbqs3exBYVgbPW4hYNdBYo6MwSd6oA3T7lmJpRgbyxIhcOZ1DL1deujnLV64f0uc0/WEhsUGKe8MGWerqgRRr7LcuCBngrzdIDumGAPIsKfcXYFX1hxOl9kUCx/RjZxH2j6na7s2zWgkW4nk6BI7A6AS/8WR6LFmmraESKrokpO9yZu2nq454+LXXyv6Il13bMLrOx+PfFSogOq6FmXjHzA/PA8dwfKhs1ymj3X4mEpCqexMk09/x1T0gbD+2K05XFurL2HV+Yf2iVuriHCqnrk7qAEVlLfu5CCY+3RjMw/s4gU+6zmeqwO26GJstMtTf7DbmA/lfydlYPqR8Ox6Qm4NcDlI99IN16l+5AOco+w0A5MZIk2gWsTS4YYMJRT6/lvyvwXFtn/KcsIyEMdb3DqTxMC81NgLkiiKk625DwziWrn8e77PCpl7oWY38Hvque+aB3svD7V0GRkGgdadYyUnnyUan8nTbIOR3AGmp/PG9QwGsfFvql/Xua1Rvlh0jCOS9A2vhyoPSIZA8kIVDYLk6z1iv4OhM0kKZkIFPXmzlMY99BgI51n6Urlm2c33YxU7L8H6RnjcOCuPDDpUv1Un+riBfo+jrmw84LKvRNw9/5+eOfeROHTogk/Kljg5+jbyuzprODpCJ/YTzP4NTnHYZSJ1C5Odw7roRhNbnm9nK7OfAyNCzJd4evqCG7iQ0o1B0ISsb71gNTp1AjkAYP7RA+M3BjfrsxqAzvETBwD28Dg7RUhklN6tKWR6n+w0YAyx7gaS0MPI2VufBUR5iahsog/CATKQhvt4OqXaIVm+gOO3aLuoIXd2PIupZoD4bcW+QgDIU1McO/RCli+lBzn6sNKnDBzAKu2zJwdOBTy4mJ1nYkit7R5bBzUWYU/6HerQXxVfajzdAc0xnI2qbUp+esRFNsfl8ra/shV9ljK7ztnU6M0D29rec+pZh6xJJWCyUg6g4SI44mQE3V4iPqonZkL9HQSsFQW/m0IZQzhZTdLVuI3AGw3ha5pD5zACFfNDYAsmwDyWvdSeMw0CT6jKDYS8keVQg/T/qr/tuOMzvceD0wlF89LuOxae/OUDAn6vv8XFmOPZ3duCIBIpRqxHKkeRqo2iGR+Ch4+p5dB5kAVLZyefhLC7gtEJ7v8qPnA/vBzobpH+vS4y5VlRKZ981B0T8jfWMfAfMLENpQDeq0oNk3/pe1zAxs5ePKxfl8uU6FgZS8p2c2yn6SkdEhvOt5NNWD0JskkEtRKNgrx5dhhiLPDe09ZxrvOpAOoeK3XddyYRkplkfVTRkyvF0EBVMO53tSU3D8+wdPYStRw/7LuQ9BGozpQBi/7v1UETnBgY4TIDhm4Gdg6dnEXBGxLnUB9IKQOIZq/uA3uxpL7YbOlvnAOMDOPq6fStbYf9iKUt6Vgah7v6MGOgAA4A64xJKqSz18y6MHdszpDyUUNexPYFk5x0Z82dFbLFUoszC0HIRQMy62ZsPJV17keU1a0hEMuWtkIYwWT2SOE1IwYFslRrp+BaL28CYWbhTA6OxrveYQjakKdmBY2toTFU3/E2IqQi6Jc4mp51FJA0kq7pAX9UrMTxXygKifyiwxu5o6e51V6E+g8PzzpLOwSAzCueik9pFNbJJO00wP8uxdb4SnSCGPWlkxsre0mFZrrPUJ3VstXFGb46eIPGSvHpwV3dOxCRzDpFZdxqAfQvg/BV9FUXV5d8x3FZEWgHRDftnZeP9IYBMfKL7gjo7hF2m/FN/j89BSG0GpGXHcjo0+FMuZ+5d+XxkpdYAxmx6hiM/GbojbTzWgNKOOYqCdCzfaR2U7Y5v8lQSupoU5Lrz35hc+0cPSlzkQj4g2NYSxh5tTQDSUDnpEA1MRWMdI74YsD6QGGYSLk5M0juPFcxjbg8AiNadV3PqKxkkvUdYftI9o2is2SVpPoyoqsynMliMsf025iOYzVa0R0qmJY+x3ciFt+CBMm+lGwTIQzfBZtoGi3SJtL/U13aXYhz0j0696jpTRscMenrA4Jijnb82oET/mwDCBI1ZwKMSeoDsVJS+HvKot7KT5keywXu+3jOBTASNcXQv83cw0OUQm201XvIsEa3ay/eHIetnI0hy9LMNHM7z1rfU9ChbgGI2R6QirgZJO6WN81UdWCcfHpTvN3AtG+dbDJKGj20r7IxSWaQDZCk699zqtngfyQC5sK4qZRMeuKERklORV6YNu57L62E/zoA1f4cGtqGcdmoDRfSQwUkzjsmWjdy+KXv96/qMZGBeMRCVcYbO0VZ7oy2MU/w27RMD0Gz0juumczhpNi5tjPDhgQ41GwRRLdyrJ9Xdw4AyJETk/4pBbmSnq09jhoks2tSj4VWr55AKUgbH2E+k2A3pVwdRfpUEJKXFbdTTATTyvvG0XO4YMIaHe32HcLGVJ9+UewX7P3GNEmPW4mWGs1lUstE/8tURlwRyeZtOf/5GOSwLXg6sj7t7B5En6vPgt1slc7rR5EW/LlD1muIvcViYDwKjvZcAUrW6mIpdVXYMJGuJabeloCUGuRiDbLIjF7WGmYHWWmftOIvHd10BKRXqHjKSzWsNOluffJ+AV//9Vt8dZX6Vf1Z84f5GZnu+ahjKIAnA0r13MMnY74fyXi+dj0GOIw8N38MYI3f2lRg1M6MMVF8D4DgkrVMYrNNf/UglKDAmhEnddSiGValcLc4UKfLqYElRjs2njdmWRmTtpeZQskdtFjk/Q981qtbBXrl/F3KUgV1JguJG0OjuAWJRvjEjm6q9/Vol3NSjTuA7cE29fCj6JiseqmIrrQZp+/lInlbMnTrQyU8JcDRZCIdNMCf244LEqnRgLQqUvqxcIaBUBUgCqEgfURulqI8Ni7GitqCBUJoESQum5Zod5nzZVwcQaWRO3bRQR91YBCYcVPFXypuZtEraRhsYEOX2GAdF20kyv2czeRfv38WuXst3ru5bu3Yjclw/Tzo7QOanb0lu/kvj+SiJyQkwJD7DcZcvDrC0drZqp/HHK2YRoOd2pQSI+djzmxkVaJ2vrRYwAcn3JLaX7xnLvPV8H2z8hyaAjCh97jPrBqPh3FjnenEMOdgCyGxDtXPLV1RRPI+7567TFnM8cGeW+XUHPUQ9xwdzekhiksEcE9Ai3UMcNksKBinHpMemZhd31sgNxf8aQ2/Grhmw+NyFDBjNAZSB6UB/yCAZtYMwg2wMJvp97JLZA1YGymt1ca9OHs6BTsNYZZn7wTxyMGYSYXP173L55OU5rGz8vfx62yF1KN1hG2SOw2TFkwyIXu8U328a/NHj2NZxd1M6NWD0xrkOhQ5gTB2MMKjVEQPYnRugJMDwjh8v6N83Ckga6bcuXyt1WH/wnSNKjQ8f3zK0bWaRWk5/71i2zeel1455HHtevlc7wKqfd6/QbxMNp0Hn7GSgexgN12OU6jr9cO5AyeawEZAsEEA0JlkpwLEQoZSSvNkFqHUNkEUAslmVqP2xgFGYHYC7KjhrynWmI2CAYt8FfRk+B8R+4BvB0R5CG/Xfy+a1c53zs/oiXRu/Zm0/77/o42H76NxodCJePeta6U6dakhIo4bWZDATHET1c6eREY5oulEDSWmGNCx8Qr5nOB8Pfx0sDTAovWoAgbF42T64PUzeQKLU9FvPpAPnqRP0GVqnsTq3hTSwZzMP6d5R4xnfc81K6UbCVMmD2nW9x2w+N2fCzzkBKGs5FRzZwDFA04CyKjjWUlTVLgqOanu0v0TgZINcLA+tgUvBws1Z5AjW16wfS6n9RWyp90yn34T5xfSNa4Mj5Zfl+h/Zes7LkPJtq6nckZWNLwIczUETINkD4/q1fOD64XSHAXJNUsiP/YatlrU6pUEuxxfw6iBOU/gO6bGrBAzkJf9zsC9pPqPDqsQQ6QiPPnZN0V7sqTJWHQLIjD9bo/XWOhadWpLK2ffdEFum/q/fa3iuF/IslN4hFD+k9O/BNgA6xuzvUcC9JjntyspR5u7hyS7Lhzy0iDYaGOTWjFEo47N6kaB2ubEBoEaxqEqT7xwT/S/L3dyUJWlLeI8mwTomFAYqSACyFP9bSgVVk6vEIBNANmtPXYnfVPUu5CiXMUVjdCaRrvy0PjYGacCoReoMRQY4nCRlvAepDSiNIxTP3lSpeYRfa4CU9dRmNqCzfuGB7nnepNsg4e2SJwes8s28Gk+vlc4OkAOKR4nOmXJPGoamrl8MJ/26bilcRxsvRjru4t/k8QaGQcO7WDZjhwMwhs1Rn2wMEgbsqfpHAU3lyFMYyf/hDiwPEf+e2MRc6S65vFJ3rbNpUozMbu8CVm9VEfP8RFk3QDg/aKvg4/Ot3P0t3sPYjnmdr9WjO5Duv/OOyqk/DaMwj5/GAZj6j4GqxSU6SOocadNC3FmjwFZBqEVZZJHYXyJljh7qIwBpsslgVJW1hcJR6AAJuOMxz2ByCbQMdc1hMj66IdMtdszpgAcWOTRIns46TpDtcDx9Zz4MXzco9VfOP7TiWDva5W5cpHXDc9gfDVDtPV0i9IPNddLZbZBWoozwVqBrQfIIgqmzrTyXm+9FCH1+XBJ6amk0TOBoLJK80eW4W5vQilFslgOFtA/gGJ546vI/gpC9W6pGemg3oLO9uZeK6IT6nmG0zQDo8kz5IICG0gu5yw+GBhzKoG/KmJJdXnYUj6DhmduA2T/RBDc6Xl78OKIPEoqncnaDRLaXrmRIhM1zlWSEdYDilj7KIht0F1VmMEt0om8KZQ8kSPRXU82ZBRgnUnCkgqkUUKmgqmq2flj/2pzrxQqo0R8Vqq4DWJjcNimhPz05GIs8em49bA0IjYqsj6QO7R62oQ2ThpZfGSGZSXJS34gsUBJAyrd6m1u7BGtPv7WnNemjgEaEcASHA0C3sZ/my2qg65/nAEfgvCp2bpDcD0Y7RPEyd7d6SkI9qtirFg/Z6W1eDJldwIkxclRA531EXlglCQ7Sl7YCAelMBxvlDRQRoJ6BMWPNOGaUNJh0sZlDVXZl3Toeq4TW1+6M1LdD336cxLHLewfQBx66Osysgb3zMwBqjE3B8cqzBjaU4vW7x4ELqVPr43tgBJq9V8FA1jZV9sitC9I24SIoQzSnCsQGOVHBRBVTqaBqAElAmcBVkNWYotkH0RiNCgoBFU1Dh0inPW7Ld5RR6iIHRo+ybkC5dviYdlXAtrGNyRfzWsw6DOq/Z+ujaoZy04kxW0qyn9oiQDKG4vBYp96SmsAmE7Afxzuh78yTJyy/loezpnOE+UQl9BfQvTFZk/R8naMOMAkh4EOvM1C0ET+jf2aMLrP5GCEmxfsRDa/RPKZIX6Y4Fw+j5lkbwEByaK8O9PLIRVoHPo6dawRbo2QXwESrr9cZGi5ZYbbsfK7G5AEB/bX8rqE2fdSPK2t05IRSMZ0Orj5m9fZgNbgqqSaHwQMWeBmA4eXn/sPMDpLjsaiWDRZ649MXipbS7N76bAPI+BQUVa+NQUbsY0l5lfnehS22MsVVIsmz/W25ZEMTZO8oKMl8qOzIY0q2yXX2vah4isMAPehAkpvH5MfkJdkxffvjcZDlTqKBZmtjjvKuMkMxc4YVXVVk5P4k914cvXCurqfpHDZIe6udI3IBE3xgi0kYqPTsa4ity9Q8JVcNhyd2Nsg8sqaPZUfUbOq+93yr7dE6XgeQ1OfRVl0BCP2KzFEdNtXSQOVQGc4ziuXK8MBY43P+z0btjW0F9L9NN2YgdIlz1DcGMIyOQy/N9e4P88IzelS2c473+ccyykOxom4B1myV/p48UnWlTr9Nr3KnUHovN+t0BtrNOzMogbf+3kBSHCvyqaWglgSQZOq1yZUGDBGDmVCI/bf2LAdehoKnhAAZWHbNTkYJ9FfWJqk/dhxTfx9fsz+jl5Lc5no9tyPzqonc5pvQOzfL+DeqNp4U7DTHHduNNlRF3ry9Uh5G8b9rAfJg6kcFv2qAmO7i8Tvqzzv5vwaEOCVvCPsjAihzvxXck4yUDN6dMwaug3veSvJU69+W8rcd+BrxbHkN2UOlOR9IRj331pf+uzs1neWxqROsvsgjg1cGD5/xmrXbCI56nwGn7lFA3JJN0kDZnmIokH6bP43hO0aZiu3bExiK6g0ZzNVISVzclkcGjCjCHCEMsqodUqYXFiUGMXFx0XwzCAvCySOgqLN6hk9s14COkFD6twfHxB6R1rP0wY9hA1zsUT66X9aJR3DUTEn51sJjz8t9owPJ4RdexuGLAMPAnd7sZUDe91MXnXN0lfN7sS2NQk/U3TYCov1GACjP2aYBLNe5X5GBLRD0T4hJ1r58Gar0pav/JeWJIDv66XlLQJ/B0lnXgXzy6mA70YHjfL4lOGdq483GuEYmD/YIK3E/68HHNOtT3b8BcJuCj74N8zPH4zEnIui88ey+AP2zqBtACyfv81Cekj5jWbc+BZ0SInGQVJKKbTZAUbFFE9F1B5h0KyV2MMx/LZ/5mDnyxkMOvQWSah3sUu/qKi333ZiSygqcEbbXC0aI07V6QWSNrHIQbWbyZKuAMcgZsj0mx8+uRdPWbWAfG92/p8/oyO7w9yzpfCr22IGS8bXPdjqm9IGBEB0AywBItucfzM+B6wkUred23XZkjpanEvZFHvK2DZBbwhHv3hj4ujyPYIDx3o3i0uqK/RkaJhtiRnAcP1vJfq4vNPtt7kAjsI+fwynBrHdC+OBFahPrPKxD2sT7TA+CXqU8xbk7PqD+Oc5/1elCBn6U7Iz9lD8rAvy79B+tz03oO0B3lVnKan+dOSKBJAiF+xCssTUkh4ScqwycjlNbba9feB/VzmNkbVRhu6rvs+H9i/w4+ttmKhCNsPSyTgkrCIlsDW9PTX4+BLxOunEGqRc79cZHhAQmDn7JpmfnKe7Q7o97cbCwXunmPc+UZIUk1AuSean1GWaDNKHYYpH20y6PChfWMvbqjh15qw31mG4M8e25QP5lfrZ1j1W1jBdGBDkElGPSl2WVLgzs8XtfIBbaqb1rrmPtunqhVEJtP2H6Yl8tqn+R3ZvrkPOzKFS8nLXUSbLvLw9CeWpgYaCSAhAloDRwIoCalZV93rWKUipTD5IJXuU/i+NV8LcQF6k/DBEZEZkheaGBQcaz+pLJeywMxjUmhh5HXyH4F4ihnroBhr2PmYSbrZo7oOrEiKJ+QBRz0O3cbOjNskUeXgWwDgBZXrwoXuGbsNDZt6+BkucE0BsP8xkudoCof3m43lQybXpVp15ncAT6dh/LbkNYasBVlqCNaiOPtZzdm8J4zObYVDaagmNLW3F6o/nx8O6NSmfA7Pp6wX8pRUksAZAVW4IJhXBkMQ5Q7t/U2STzsssjGFqmGjYTpcp3OTf2xd7XpPsb+yFS69roqEnP9YbQZ6V6K8VYmnSQYlXg3uzUMfURrMCd7WBjtYzAmD8GhAaOtUr5SrPvxKtM3DSUR+4HdH40UQ/2xpLGN3cGd4q1wAAFBgY3RYvGApSLxDuWpvmB2SajkDKOEJiplwlC78BJ9bzVLlstFQ+ScrE+wGjAypPsmSJnfD6J3FY1KlE/DHJDpNSB9CUuhNa4F9lUjQt6wDyMc1rwLbzaxLDD6cZU7A4UtHBESVAzMGqojApTS8zMHSEbLFKfHJXEwzVIB7IwD1J0y2Ab9pN4v8lu3oyqy1PKFwaAxHDsYKajNuUvkjHEOvehOM3uM5gVLGjbmoCZ0UghIw0Edi4AQp2X32V462PP9zbV+mR7jgVNy00WcyZLgplanPMfYS+dJCbZCXVX2qMoMBIYlf0bEPKsEQGP5vUAWVQWQCpql9xTa8+keLYxxqoDU9W2keNQe4vWkwVoexlNhUwdPwoagMhmyAZEK2Gp8YaIrzSQ5MZAawKSiVEWlkB0eM7TtrHejEm+QS5+PjBtpKgvij/emNGvQ3zYAbNZxad+n8GR0qdkgHRTQ/QLeZSEWEUMdQBl0fJathyyV+KVAseHQT2VcjPE7VA6F0COYLXlrcrAyHZPkplO1XY1Wx7cgU96XnQsa5MIQLXO5puIx+36e2v89DeDcgZGy0/J16Ihslcs59NEiMcv8p35S2NGDizBwPx5FM+MepF/zVC/epmdtv5aDkvx7xJAdk/i+E3Xr7wNpOM42LACGUiAg0cWmYYpV8vJTjuwIhAqR4hLSXUnTJGdMTb9WJusN3/qYbpTfIncsSKzVjTMhm1OtbWPrKjDZjdkYZam7nUrzpNDuioK9tfyTxJSidiMS6Y3GkDKAGB/BSQ1vIdDtbZHkj86bI4GlAykcDD0SJKb0weffN36hYGjkp+G7j2cfuFmNkOwASAFJEsCSP2lAopEfjDMyRI5hA/00ZphzpJ+kgvAK4VylIrx/Hrp3DZIXl00NWewPwKhpjr4yNJO7jVODDLbHKWBo//yUCmsjMDsOWCIXVEzlzAc2XqWV+Sxc8tDVqtB8Hi1uJe98RxSukya2rgGLd8Ph42JyLGsBiPgGKu2+IzSGFRSnTYVAjgoAD5tzjuyvU9FKoEeuu+9kkJomKEU1ZmjbRaV6xQOjAY+ygKpB/roO5TKVmSRB/Qe4wxc2YsMBRAuLCoYy9/WEFsROCCQ5zLem/JAFq9o7FEXmEDv2c6Ln7DNPWxNf6m2yDT7SiqRYNERTX9nrFEGJmN+Es/YmNGaztJpOq1nCxyb5NUHIQUVA0ggAaVqM5w6UQ9ovWyi+64b6mE2emYGVwI37eMDs/SkBlpbAxM6xZJ8TjqBNEA+1PPo09xisLb6KQAaN2eRheGAGu9n6y5eMIX2TDnODY7AjdggvUT927wREusKm56xxqxaqzCZo8Z+6385vUM9qMoWslPDDxvgNknPU+IxGYQReQR0Rzk7T/nrGSRFS3pm2RGAUn5te4XViKGxdlvq9Rhqks0Dttsd9L7Fv0lF17NmFzQPZgM1HO/jIIZ2RQZRKV+wDOrKLpYN9vYpWgfEyVlDkWcrU7BF+cjyYBoao98JYOlztO64MtrSwCSg0oixoDmLlBkYPTginWX3ieShKCDbrJcAzqL1RBD2yAvArMbJJrVvWxfHPOtoLwMrBqE1ddCU6PjGhA0cGzc5TiBJBpKNMGmnWrQGs6NOmrt04GgmaGPXlO4dmz16kg3KCezBiKhFli1r0cBckE0/jk7KHuH1QqAqKnbR5d+oe74BuQ3ECJmFEQsZZZqx+RYD8/XYYodX+fwc6cwAuRXSsoXHLiAbKmycGzj2NsiuD9obWP9Jdj4DtgST2p7Zq96zR/dQ5+MNAFwtSpGANI9J3fBr7+QQudxdDJgoSWme+VM4VMqwlbEPGMIY8oiZ/mZaa68bwdEEKQHkwZHUbJD+jnCQZM8KablNFZX+kcEx1G1zMlj/8ZmdkMUdzNFpQGVrKpqzBqaGAmhoaChYGqMUYRcMHdS6yiEHb9I2d9Xdnu8AWZInm9yTbG3nrF0CFl0KbGMu31qBuohIAVfLU+vHJ2PCnALUbeaOaRnG4N1xhTATZAZptZ7xwNTxQTQ62TG5CsKRTGaprZ2dJ8TtATf6LxHpYhwEJFCMrSgSg3QaYHUj7zb7pgTFF3BryaEZ3n+NDEqjf5bjXOBuCDhXOgdAbh/7uYENkqrtoKSAqKAYwEQectPtnJeqTpikCbskCythYzEIkFuPKNR9Hyyyt5F63t12iqA5Vr5cxStgtPP0vf81byw7UI0ssp+D624X9drlsZqQ1V0PxUippXd0oJHY40GAzO/2OlwnsnYB3HkBL0N0W48xVBOCeI4DqJy9EVBLwTQCpNavAwsEsAp09z9V1UxbiQxr3ij+eh2TTAX0WSslANLvU4Bk7ZSCDaxMNXnvHQRKsC8VZLdFRjH0mWZrtLKxh/lkgHQHjcolIQZWA4eoaet73dAMV7m3QHQExzyNklI/9kpNTqFeGKLYziLDRksbQMneNirPDDSPYDDHDYVjrDUpsw6spr1kp2eY3nlE8BtOd2iq4TgqjcDSA1A4Usbd8jpwJKhQseKPNuD1CkvXuM/f0x93Dph8Tujy1yGEAZL/xeG8bYET9+xxDZL9R36umzYpWBYHdxGsYnYifSqlV5K9OjXWCHhbAHg22bL6ts4bNp/C4un1uEIEQ6uIeccVEBZZCJMBZCnBNO01zAqOhIaGBQ0LF50t2GQ1HgdJLRGZ3U5qs6QOKiBcHIwnZYH+TiCAmYDWCFya2COtpvUZpRaUmsByAElrAyC1Q9MvhxWpjDVWAMSE5rkpyp5D5BgaMqf1wrDNvwI8swiO/rl8r7whLc6i7RcTH7gnAJavDrUFECOg1MASYYMsMWC5tFBohyZNtg6nsEipz9YsTxpFkAZmRhgDvJ9hkOPrdNetdC6AzA9250ECmaARFB8/1++77yhdSy/xRkJq4kOJ+uOt3n7DifrDzBRHW8BW4vQBegbHGQSpq57cSUlHdmPgIEZTSTKPKTOLnZeBsrA6LtavH0tG+cQvxkAWbbUulz9TgTk7Ewm9IAsbFDZZi6m1Us4JyiIhtrZdkfUUjV1KVbOH+NhnAbBnRmMCo3TOKx9pKdxFpFP+DCSdQSoLrEVNGxQsxhgkN7Eliie6B4laCkqtqLWqrY0cJA3cvW5tcLS2984fNUepZQJsC4gbGhVVicMGKWtKQgdN8voJ4At2WdJvWN9mKrZcFzmT3+u0P2PgWpa8yo57yse+7bKTZSh/B++q5jUfu7/XQupzrqbrc8SJY+VLHuw0GFluc0e4awAytWPntNAvezteYoPewaxW0t9cid07uCvMRv/shQ8BON21jbzflYlWNR+joNsg5fKQT/YqymFo3r1NWtShRcVEQp7TVC0TRiHXnBEbs7QXmD3GwQNRP6m98gZl0dZRkfLuyEPYV1Vts+bl3D+EWVYSu5o7ZZCXCJP1FEsRECWtlPD8EhZ1DUtgsbCeYt5iF47ieaZNgCQUFN9cS7avNi93X1f+/EbhldaqKlR1a4XSOSLI34xgLqndi6oqBmYdLBCBuYDQJN8GZ+zN4yBnTrtgi+TfG5SH2as3nTQ9E7lRsGF2cI3dFE0r6SMUWD3qoTWOYLjueF1/7r4OoHT2annIgy2ENVts62oEWvUxHs7Pl85ug6R4T1+oAEd3uFh+x7/Dd12sFvXF9fs4LCBW/q5eO4amr8ngY3LuI9ahpA3PiRGP79k6Hp+ySdsCHAMwD+SAaAUqlGllAbjEj5mBxcIgGkBFQJkh5XC7zFBxBgb2ZtJn2z2rrSeiJP5eDy9Ki81C+UgAhRSGinqIm6nZqtpCPhWi5k66ErdsfIVuP+gG1hVvxAZJzFiKqNjuELF6NoAk9fxS8Q5XijlpRoBEMMhUaW0R9ohq74lOWUj2nSkUy5u5Kp+AtIse8M5tYMYOhO5KIon8KcocoffFR+tfn9KzwqRmU69J5HOzXprVIN+XJyw0aMA8pfcQeQ48aF7rD8kRY7KzMtPH+JM6bQKLPJDrwCbmDgNHCY1r7oAxLImCsL039bXR8329dD4VOw0Ko83OXZNRUwkQ+yyZ8TZ8L8l4nCtOv9M3xp8DAEXY+C4PmenGjs0h2ufMg8zW7wFlitwDYfounyfO4M/SsWZtjzTVrULXq9SfNKBykfAXYtkNr4gToHlMYtSdA67hn4Hj0I6ZFWQTigld03czS0cOAJARJoOk7oMm5qhCApJFZq9UwLcq2JGo2JPu52J7XYGlzG1hLGw2SKnfCrEPtiUBkJWsxKqKI7srCsJk6rwxSGCwKjS0IhUjDkNxqgSTMuZYfe1H85DL95avrDwbyAiMMUkYi4ToSPhP0TIzExaGgkFDY2ljYYCmIgc7XGDssB8gGTGVltM9DYRGLH/Vts6AA4/Vpi3FZheatq+vSkQho3JP3+eH7rLBdHouGK2YiANY8yDvFhNAUS1Ja4INPyRMKo9L1r/O3MdxHgZZqJO/CNeBVmpHT7xo7tMc7jVxYcdRs6zkl25lhP1vMDEK204Cn5RdrxmrIAfnzBh5+IwPGltZ3zeCbffXQTEPaehZpMtUqBJWexItoZ276mIOlbxQXCQ+UIScZZ8UFZcCRls4+SxSJ1UGeRAgU8SKVjFSdWNpwh8k7tJCVKx4qZMQabhPUfU1rZ7Nqmor0EnRCiZ1ntRCTkbAjIaCwg0LingxC8STDYstjBYnBUg7tjoU4DT7JgVIlqiLLv4b6gDj1Khu7BSAdIZKHs7tVTkmh00TPhLgLWyB0OxrikpAdoOsOynhTeZplkFKgM1k2tTqzCALjDWSH/fquYZj2WDKrI4aOXb13EBRn4NMjmCyZF08+nYn5ixxjAbC9o0oCFIKhme4qzOOVnW7o2GGvCOYvY/Thgl2kTdx+ZrpHCo2xcsVIKEVz13tjB9rwN4AnUVMRlOpGPZi9xUUL4/CWuGzFxBA2CPtx44QPRoaD7Dfepwl4lmrfHD6ogM+gPJwxTkfdj3A3X8/DmkKkuAMktHBS4F0fIpHLSCUxuLjVDZXqElIBEV/jhYIh0RmkzrBWtX4aEPrCMJAoozNy2wzXKze/IXyp0BCV2xRigSOeSkv3zaV1BapTg8bxRrrAMACGrZobVNrWgZnQpE4PBgQFtdcspotgFhgUTpRN9b32RmJN+wIxB5ApCVhq+Xcttaloy2yZAnUSSB2KTIAsHrprVdkx4s7UpSaGDiGTbInAw4ilK6bnGrMKob2ZbIcZTupgnJ05VWf97Jx7ut5PlyuDXYTioNe6mPeH9PH/6UeSK1tyONPN/rbOdOZAbKlynCMKT2LzGp1B1DW0QaxyIoK+5UB5bUD9np3Om79aQeUG+XI4QT5XDquxYLFCL8FlP0QhS5/KzD34nMH5v7bPG/aTTfGQMiBo5jjQg1zPs0NwLI0NGpivG6kbEOApBRKdUf6mgAJazrPeLJ1ghBz5SkCpq1TLpZ9Y4+JSTowqffEw1hQZN4z2RJe7CFAFRb2I6E/k4b7+JJYKPoMBeYqCpcvapyAS1RcQT1ftJaSmk1wj3OuB6tXX4MiCZGDpmOlAUGqtHGFZQccSs8w+6+RBwaoui21tYZCYlNmLR+p7bNxQyUS5swGq8omGcoouWOKBpImalnVJkhgdqMUo2sygTzI24XoofJcDmywEqXnM3RmjwKxhaTlajR07wA0ARppfZqKbZMmKvLgmPLEwbI5VQKj7/tnSef2Yo/2qB7aKf3NYBk1GDCyBV9nSImldUDIuTG3gQpKkLjYb7VjWz4NJP0dabgds5ve3ZdGA37tpgzufjmNkPn36XqsR2g2srTPyVQFJCHCUEFoVLBQA7UFhQkLkccMSmMlDk+mdgZYWLMZKBpqeVyoMgmbmztz087FmAEJw1EVilkXnDVgLGY/Yu0korgVZgF9ZlezJxg4ksRE1hA8CztZdAobGoGKxgAyd7KVg7elrDU5E4ayGzPX7w2UBTABj8RzkU4De/KEcNO4RQ0JchFIMmn+NSXqWu/SsYvaOcX2yGjFwiVFQWZqaFx0brK0hdS/2hABZ5ameq4cNXps95ICZ1O7MUies2j/IO3o3CFL1vHIz3r6k8pOZjYgcIsYRvdGWt9wkMx9Bd6HrM5zaE8cUw+MWsjVZIlzpnPZIP1YhcRmxZhdcR3+o+fd3/7MqzmRMaPVK1rcHbODooNjAk5/W36tNQxDVQ0dmRQYcz465kgJUFf5SI/PeUCwpsijOm8Q4NHXiQop21mAZMTrqVPA6p7N0K4hxaqzEkTIbV6xAQVgs1pyzB46O2QGSDghsiW61OtJBUQNi9uftV5hqrYtwMG6C5+KsVaIqHWcptVxxyZ9TrabaARoWSPmmSQMyL3qNmBbN6LE0oiCEfp5Asgi3yMdZwcT1H5qz7G2sRg+A0kfcynahtnKHmAoJgatdwukBtTmaGUKe1ujArDMgW4QrzYpg7QyNY440QVhO6RUNxkg86Ds0QYcwOegAxnsvJ5VtosCsoNokmLvJiaf1h8tfjcTDs8MJSRL3+UOZv3Iz4ffKLmhdG/3GR53lnRuG6Tly2hHt0KO1/rh1AEjD+c4AI6rwqabEkA6IxsrMSFRnIa9k5g6G6Tfbj0TG/W61QBbDeLl4pTfNKzl+6zzZRBFYpGqLta0Okq/MrU4bVDghn6f16ovkKbTYJISANS13wZAQgHSVlmh1mCUymImCbYeRx4ECKUkz37TBWh1XnP2+BvUZpC06X+ATCusmscKdOddlVuZMDgPVudWF3AADHaJdJwBMgYawELbADf/th6Qoo25e6c747xtDJDkiMmGy7AcM8Reuugui+aFtmcXMuYX7zcR7iZ2ePm5kzemYLgwMUUSW/2eUx2a59jNgcwrkc7dOJNGz8ihxOjskUYy3Pky4oP/JvAgH58XHIEbVrEVHGNY7sN+rpVyRmnrmFcF7s8zyKAHR/usXmhSoaEJJUZ1j6vSjztqND/J5NIL/FYeh++CPSYQ4KSCM9IPtUxCYwIkVRjDcREsMtqkuB2SGFiwoC2iZrOGQWRzRJ6NYI4KexYlZKJsh4SqbCxhKAsKKhbMbPY8icdsxK6uGdgZgySzmC1p0htBzkvYT4mLs8mKPln1iiopdrfCHJsRQstKCLSiIrFIVqE2pcTbNtkgU33bOoZm/y2l6HcRZ8XNPoxl1sGJY7aTibNDqrdrgKQlShDpZVUAL1QgC+zKsS+XpvXUCL7hlc2CsXoy2TVboZnlFgSoLqmOzcmTVzC3pcY6kLS+ttGPTbSbV7f8SPoTh8zZjTo4rRobCpLMNvp2/W0FhFbnql5DQ33s3vNi5NmdNKOKPQzFeeZFjpeMAlkuqQMIHs+RC60l9+8Hb3WKvyNGbKg+gmRuDM9bSIusJZlV4sgTa54PJdr89MNZjpH01uTcWlE3frM6V8jVzqJzhwtqqai6nBQgnbOSLJlVWbzarTQsrcn84VxnCRAMHM2wTopoRBAbnwcvArIPuK1hyJipqVOloTBh5gULy5JczVaG1fooEBCT9loAZsjyYWIM5kVnqCyAGN4Wn0MnsYLWxs3rjpjRraGY6lOajpTVqIe4FFXvZUCsXMBMysTl2Adbc97YpxYNObJAcIuv1CwsUOee5ImamBCydz2DZPQFyFzsIKSwDmOMzr5oyioZAlC+rq62qcRKAmRTMon8ewFIsyGn8SEBo0qS9znJt4U32WwsjoVzDR1Z2SPZ70OcVdTcgUIqhH15ScExOmfUU6orQMEu9Sm21ZC0gtNiw36e7zX5P0e6MRukFc5Kqg0X4BPJLSE2UjDcrY+oY38wIRqlAykHSq1kLXT/F/7MbUjTtzUFZnWJOntUcJR3J7BP55YhF/hc4emYDAj9B2uwtzrh7gEUIIBQy2xFnKIxfK5m6xMY5FOwCha0VlCbsMresaXCqgBSFBy1GXX5LgFIMh1XPC6hYi8M4sUHEzTpaVS8z3eC68u4JaQ2o7oA46LxKSSI08TmRko/lHQo+24KlBalzgA39aBnMQsbJEyNNICEMVxhgkw2UJe+PYEASmXtVWfNmOIrDg5tw9KUHIR1LqN2NzZ372GXTvgvE8vyskhxjfW53CLqXZozyQ3gdk2zMCYM64585XKETdicX8Zu3ZyT3uHinG2MCFH2VcGTnHUFpsykw+TgA5Yljb3tAJHhoW1Zq1wfx4POA5I3xCDjTUBmktbMq7ZP7DEyS2G9tZEIAxhmttDZIjhNERBDv7NIjkYUwZJ35qBW0xcYxtKCOcq5gaOxP43iosiCA7LnSQ9yw/iXqQ66stl1FVyr46aRZwqSFifo+y1nZw1JZhqJja4oULZFF5YtrctTLGFv/wGOkQaOhVASQNrfBhmxl8LCaLmgGFAuC1TDxMIQddDah5X9aUlto6bGRUJOiq483wi8VKA2Z5Ae4AekBWXTR89tuqNUbY65U0kg0vhRkvatRdm52RfFSwxUbxOPlywynbCWgskAUoFW7IC6pHGx/ZcAkM6QUYXWBnKva06yb8AiZEptomzkyuWWSW2vLKYII0tgyArr2sUWjkBxx4/EHA1PIqkMqsrOzKhpO4isxgdQWv4j5tDtnA6Q0WfJOngUB9bjzb5r33YA2Wex72dWEF86Lhc6MKHrj+cAR+BGbJD53MARdpympaVRxI9t+DNgtLg/D1K1jiQVYHWRP9n7mzc2AkN2g0uV6lCdBU3pGDVhL00DxEy7lTzo1CmNhzT2v1W3G8NGymuANvS5BDtPQGmpsWSkis1MZlBE9drskqlU6agaBwgNC5HphQu4FLSqK1YvAVQmTJQKk1lAXgGaKqFMJe0rYnGQjGVhnNKMHRXsS8EEQlkkXnMhwtxmDypnakktkpe21iS8pBAaqm53IWEtPBVZ3l+m1USdMBQI02dp4GUR4GwKEqxMkgmMBT4yqnzKjKQiDLsWMOsMGAJABVQaYoKi1IxMewzTRqUKm9stAKmrCS0LWtp0u2FRMATC2cAxZhI7EHZ4XhJ22sc8SRDGWiEq9KK2tkYyJZFazFHOeAHPq4onRShZ6ooAgpD4YKPPku0OeGDB7H0jZrXYkD92CpPlBBIOIX2dd7LpskoxyCdM4Q4keaVid+TqnOnGGWQa1bzCVmhhiKPMseuYHAGi2XqaAWNUS42jOiux7+Es0sEJVtkx0mX0ZAVqAsAVgNodzaNtoJgdN5aDrgk5VILRSZQbOLy1UYbRbODSYtu7aWGyUzmW6IrpciarYieELK5QWBgZxRziPLL2Qz18FC9FbG5lEiAptnR+sZ8wliIxkLLSNcmc1zm1SdF1GlvTGEWgsyfrAMSsJLESWgW4FQU7BTZfRseav2k7Sydg3QGQh0/uF+ZlFZOKMj8Wpmhy25rO2ElhI9EiYa91VZuqOrko2lXnvhezO7o8cAz2LVi01DlLmwV+u5D5HkXOYk2Y1B6pDNLiTZemcgsN+SENLI8u0TlvrFtCxx+byp4/ss2BzJk3YHTHGMEDtsOTKWVrqYyRUuRk55iyWo7gpvxd7lsrltaBIHomqR/vXh2u4MzphsJ8MhDm1XtM4AREDEoCJDPyj3l02RhtBfYTZyH2nXw52iKdua2z2ufHKtxBUdS+sNFJXtzKpywmZJj83/Gd9ib/zsoCy6vZF/PHH9z9PkZ4ijCU9KFSfPSXBXRZQampbc0M5wzbMgBO0rnLs69nqOsjllpV1S7itIGwtEKLYHiVumylYSlVQoxIpvjNZhCDdiTPg251aqyjEXz3rZadMOo9M7XUBi0OhgMEe7BtUy1QvcUtUsPaiSW0qYAWrc9G3e6C1gbOZ6j/xGo9umuOCilTME/rBLFZm4JkNxhqBhODpKRRGdh5UEEJO6Soz3pPA2yzNlJ5lfhX1kBydEwyq9mSTeuf8mdBDMY1sq7XLODb5MZ+yD44S5v0ABnQqeVbQUkKgRoAsqS7RtAgpcQm3z7BoztPANpn6Ezp7Ayy0uZ1AiL0JxWOkUAhjzSNQ7XWyk3E0uDIUBEuSB2LTIX2DiMV1qngiMGNvbZFvQagqimp15F6FZttpAvpyX6asEFSV/JwAKQ8IGZN9B8e7o1RtAuT0XKYHVJi90rERAJRP1OVza00mBoFMQWwKOMiqM0ugTwQYS2lYKpFAVJXy66yCWtrDcuibKtpBy0LWi3AYhtYFUCDyPPAx43Bi3ZVYs0jxIu9kO49ok6YVrrye2wl2NvcGXlSsdh2CWSEH8dyUWyeNBwcyRfDrZ2oRtsZ8ywOiuKkKf4cJlsyAi4rIabGbJt2WuNXFu7DKCWxuhKwIIMgdE461OMu71hYGN6ii1ks1FCb/F1SlTCyL0OdeZqvUghoBaUwioZBxQrmvUPHgVO7rxOCDPoKuIFTct3cBXGFvX7HPpLPhS3De4RFhULf7wM/c7JBs9d5V4C7GiAHKta9y6uUDtyWgdJAkkijUql/CGJ0MowM4EpMYjjPDhrLrs0OEAbCyXhMCWA1484eegFHwkg7HKvl0KcPdoYvEEtQQSPyEJiKfi8W6xz+wfYncN9GWWFJzpwa6+wTqMotFTdOQoA+p6b32WIRApqiEzQSxtpaUwAuWErBTOocsvwnSbBgdVM1w1EUoCdrPMLLblux1s71yf6dOaQ26947jzWYNqANzkPHofxfVsU13jF4ob7BBN1AWOMguUHtog1taVjmhjbHjoXcQv8jE2xSldW8IGAh4SRz1mWKKVBrCRZJQGUJp1qYsLSGpckc7oWBxRZPVpnPWGEzbljvIzR16ojdm4uuS8kQ+7F2h4nhqnNL9ZzJgU/3ZBsCMjZtWyUzXiR/nH4Xy5tZ/ZtPHQ7QBz7jd/ml50g3FObTfzGcU/envzEDVD5PP7WRdfV87p+ZG2dsrFHFZj0gbTip4wSSXunWs8jfmVtxqwaC8Wk4CyV7FRJIFllxR/5CQEc3IgpQRMy5phSnSEkQB1uezWjZ+m6UFAbrQDHeZ4Up8Vx9nrHcAvLgeQYSOHGq83CajdMqoSDps4O87tiZcZ4xlOeee8fQubxMiAUvSAN2bNkwCHtlkwGKAXWAQhBFXGNJLLFQEe+23hODqIChL+wAgBd2MFzmBfPSMC+LHM/yl5uAZF45wcJ4SonoCFsmDC5HApy1EKptn6paQdOphyJTkNWcmtqF3WQAn93j4GaAyazTK1Uu0cQ5BiMWep+qSo0YReeAC4tMavHQjw0os/Tl7pT7DsOY4zZyMQwYe0D1bpseygeu35F0YzbIlNz/gp5hGdg5OvnFsTbRsbN4RhJCG2zRf7p82LMGcLTnbLJHu8PY5TXZI7utJ16gdiAy73mEznjc4tDxCwSHbGVtY2vCIMMOGAySHEy8Tlh7KgOyND/Qe0nDlmeg5HaaFoHV7v2zei7ahwlg28/Z7bJqq7X65wDGbOeh9M7u/c4CuGO+uZyy5YKFMUVYjbcDC8PhArTCGjgv0QilWQwiBHKJfRy2ucSyGIVtvVDcTJEDwO08q9TGWFjj8JqumQMGloWxzAGI837GvF+w7Bf5u8wKkPIAgqiyIgesqq6CABVFC5lx5NpD0aXfFCQt7rgxC7g1Adqmx7WEB7oxJDpARd7nbDNQdF6iDTY2UwfQVYWUUDCzLCgCW/qMHCxXvobc11JXGo9z6rpm6rv5/owtgJGd4SEJKD07I0qPx9dJ595yAejBSWx16TjlIe7TM69LYQMiDNLxVnk+QFizp/ig1/iaBYm8upqpJ0Eoh3HOWOVGHYilgJzliZrMsbgEq7rKLLGL3NyOI+fsi8WWAp9OOFXdesDKpcAn4S2LmACZgcIxN9ZBqSU7DLsN0sN+lti0PgNkYwJYliynwmiLCS0H+DBvhNssQFskX0vrvjfHiwMnenCsIAXCgqlW7KaKXZ1wNE24tNtht4vJhkUdIQUy06aJexVQ0wEVYEFzVbpoUzYTXl9Ts6LWCVOtCsQT6jRhmiqmOskWCqU6SMozrNxQBxJ8eqEB4unxjNOTU8ynApL7kxnzPOsWr4k9kkRyUZG/FtJE1NBaAbjKQKL3TRNht9NAdV8sWfzlrTU0rliarja+LMmLz6FeG+BpWRoDc2FMzJiXpur2gkINc5P1RImKzJ3XthSMF0vmgoKCpqFCaycjvLTr4/z9SIx6kkWdQpfvOZi4+7M+OQc4AndkJo2m7JnOlZSRX66lnBM5jYerTzGCdDTaGGB+p95BFM4VZOo4gGY/iKgSn2yQDAoG5t9bXtdNIpqJ3GP2RYLaGBXUCoDamqhHIFQUTI01NEYM8zYqy17R0D1YdM51lc5Qq64uAwEzXhZwIVmxpRSwrchjdj3tyFDWZg4CB8h5kXjBJa7bUFumqiBcAGJdyYalNDoLhVlXMF8W//AiTKktM5ZlxjLPct1AclD5jRXVIuXclSrgWCt2ux2OdjscTbsASK3rSg1Ee2n/RmgzVGtdwCCZC23ssVncJqnZUadmFgldmsoOtVYBxWmHXdXzWlHqhGoL7DJpLLoyxwSOvAg4znthj/vjPU6u7oVFni7Yn85YLE5T1WtbvqAWoGh4U63ShsxVq4oAYlBhlEqoes80aQiWTTEFS7B9YyxN5twvraRYUPjz3FGj40drwFIZ+9YwF8bcGogqSllQFvW+UQOWBVQk/GpZFp3C2FCWJgx8CySH/notfIouGz2Vtm7YeMb4FaeP48rBXJ0t3SEVmwxMAI8XNBrZhehsJa8P7k4JGBfg6X5iYTf5GDAWu/YgjyOJZEtCIGKbABM4JPuJ2j2GEczeIu8MO5z5NbNq7dsGoOjeKwKKRecvV2ePLIZ4VTcrycK4tcQK25pRBzrJMGuQe9ggnWnywB5ZwLUZQLamIJmAy0OJK2gGlqodlaqGoMiI3pwtLmhtcWbalEFmpwSbum/tonZVmzYprEjYY1WQnKZJmOQ0YTdN3pAEY70ErkCtDZNNMfSlaFKsqbepclbVWSXgu2IqE2oR1lirHJdaUanqIrrkqrQ5mswTz00GCgfI/YLTkz1OTgQg9/sF88leIwqURcMcMJD8G58mQm0FsoWtF9YipkTFrlJPVaMKTKCZGQszqoc3kWyHkcakptqRavkOkHNj0CKL85ambU/CZJlki7RGEl2wgDAxsDChNhu0m5hdrNN6L1qnZDDrr3d9a4sbhl7Iqe+ZPdzvkbFMY6pJw9uiLo2MrUHh2unGnTSJNTqiMw9B1XlUsU6+8QxjjgqyZpRVzPOKkfMeHDNo+fJNXVbDexZQYGeq2nOwRhOqmK4WKZ8Rka/pZzFiojIaC5Sd8moDammYUAQgIbMgOnCEMgoggsBtOqECpAw4EkTdFjg42p7IbsbuqYOCFMCsDGAWD6sAZPMBAZAFHHy+BTFK1fombRxdGai1htZm+RhrXIQ1trb43+y5zTOCfE65AqN9DBSPpsmZ5LSbVNUStQ8MEBfwAky1qQeZxKhbG4hYZpcAaX9wRRvSuNGqKvYkYDzVnZ9XBWqTKZOFpuC47JfwUi8Ne1Wn59MFp8d7nByfCjjuZ8wnczhnWBcKVsBrjbCz4ZwKlsKYmBzMzYZaCsTcMhGmSUFyKtqd1FvtM4iEUVoklbPHFgNFWwIga2PU0rBvDXVhbXdZ5xMkoeFN2eRCDROApREWQAZ6D4/j1P8CxFIXl37YAelG2mSLKbY0v8EIGelbHFqEtrGyNOnf1N23icMH0o2H+ajw2VcBklawjXw4Uum346Ci4Gpgv/X6qKwI4s6RIFYPh16dz+MTXr8omP7dZM7x6bzQrHZELpggoDiBUfR8R+QgWSCVXyk+pUjoiqjb2pmLGeuFgXDT2b2q7hBpnJkyFLNB+kIf6kE1YJv3i4NjsznMXuwKYJG/xKgVkFVWGkCT1/6yNHFIzKpOz8pMlwXLPEvelqUHa+igQvCOPqmKu9tNODra4ejoCJf0c9PRDpeOjrDb7dyvNu8XiPNiBrSzg/cgLhD7mYDj0lhZlACOBEVXX7mHasFut0OdKupUsZt22E16nlZJAqD1JCaJZVHHy35Wp0zD6clebY4zTo9nnByriq1qtgEkgX1GTK0AT2qEhITz8ETKNDUGssBNLLUC065itysK4tTNRgmVmlU7kAYVUVD1usn50oRltgbMi6jWu4WxXxqIFp1z3lDKAoBk6qW25wxgWRYsJF7zSmYyMOeh8zvHhKTjDT1x6KyboJV7/bVRTbquBu6TXbs2qz1LuiEVm4ZCcrNrlEA6xTF2mUzgk56YWbAZ823EMQ3TG4FH5mgvShXCY8OksqTP+vrIHG2oiuc7QGobi3lO4/Y4x/LpPF42sFQ1GwKIk4LhRGF/tN/FitekS3UhALCl6Wxp3MwzTCych3WlG1mFZ9EOLx13UaeDe9dUnQYBpcl85dZI7HkLiQCyMBZuSzDRZs9c2x1d3dWVhyRkpSorMnV68r/+2U042u2wcwYJFC4ed9gqsK8NrUIcRKzmFeuwhRwgtxjkVCcHyGnaqZqt89uJfNAUcQyQnGcZZBb1WO/1M5/OOD2dcXq6V5ukMEurCyI1o+gKv6UAtZGv6ehMnlSuSO4plVAnddpN8aEkixa2Y3/ZBwf924JJ2keW4GSdJNHEQcQS9G7scWYBxAbdu5xNvtk3FivqB5Aypo4UXbvvgyPhoPUt4w2BNtrTM/ImVtr1XQ+eznmins2dIZ0bIDu2CA6SpSCSbQM5b+Qlkyt5RzJW9PMybfn7cZ1xxH+T2WmANDSLTH39jGA5/pXHyTNpqPBgkhHzqGFqGtunzgge1GtKG1OR7L1iTotCYpsiMlmy8BStce1Mkb0I/WEYKDW/p9lSYM4kA8Q4M0jInGSLpeMW4Fe4yAITRfcgdsdPWz3Lp9N5+0XYE5EyolLcIeIgaceqak91UvU3VGwwYVnYHQxTrVhqAII1iu3StwZIYYdlqqjmlClqi6yTxFzqDodiwWgwZ6L5vky1FhbdPNZx9k/zv4t59aFMnCGSQTrIIIdTp0FX1XBSmRC1Wk0SDpAl+gwj5liHRh/ltxlFLUBS1GPo4sUixVMjLFjAkLnZvlcQQ6ItNOqicNr7Wz9GYrZ7KG1fxoCX12SR1saI0EIHSVbOpRikN3EAiveWu0zFDhukqrbpXZIhbeSeV1/jgXG4ld9rO2r6zzUe7c/KQDienyt1wMgKjhET6DGNUGcLdKe+SrIhFYQ97grF5lTWEZIX28rqgM3sHcHO3c4IgJFYG+I7ILzVbAyP1XGwqgGJcnNDrHFqlri4eEVyAilLRffOBO56bItriEqtqvVUnSXudqLmHunfzCYNIAnqWGIIm5samqyXBkDsj1XZUXXPLcGGLN2ZSwByEsYo7GzncZelFq0JMdzJXy27sdeFHSSXWYByMWBcJFhcvg8nmM+gKrH6+UAZdaqnzlyqBow2iKR621XXLqz5TM0GpyntusNirApHwR6bca3mnx374mxYeJEQIFZZZmGNEqamK9q3Fg5C7xWMDvUomN+6s67v3U6KN1sMdbyNELbOxJk2LGVnSudf7kxVGWc04J5ZddT3HDkx4peBNz8LrgVuAuehR46yaJXsFXk9tEW82yragsOFVepfvSdPA5QtTIUhTul4R8DOwlsKYVcLpkrhtU401RwETYONF/VAGx7JbQGGUMCKnRWDUjhDUcACc2gGBNRJQotkgYq4N4Qr16Z8uqgBZYnCgiJovk622GzBbqq4dBSe6kuXLunnCEdqc5x2O1V7hVESkYfzLJMBJDBPLAHtaseTYGldmaaVAEi19bG6kGWOuYBjqcXZo68UnggP5Ra1ldJdFUkslbMEWP2o0wOsan/x+e7hLKop3GhC3U2Ydlo/drwTUNztphVAFlLw2wBICVFSr7Z+ltmd+S67MtGzYW6LmAF4wVQ1TK2qLLN4ugsX9XgjQHoUlGGQZL3XAcvrlgDdJuSa4Gh/OKSPhq+hrJKt8SibylTTzQBzxnQOgIyMGiobm2StbMucqRPuwh+ZrcN6nAcT7WMksaLJKW0U1Fl+ekRmjlYGpAaLvb2jrCt8N1BM8+1s5Oym25GJXMySMe927PdMwp5qxVQKdpN8phLhQYJ17LbCeRE7EYzZ6Erh/bYUbPEoCATNAMYOjiIwEYRNBJQpQkksnCSAkjvHQPwQ7pm2lYZYlygjhLfa7I67acLuyNhhVVC0z07V6uphP7ZdK4M1HlBAcJoY0zSpQyJAqLGut6tbKthESZACJJEu41Z1Zoqs72ir9IRMGrynvyQASAq43SIWafZNIdb1LStsoWAi+KIftZI6hMwOWoTRKlO0oPVqXvZpwrTT75VF5lWcUMhtmLaesPrqYOqvkmBZDxgAQwaUwsImxRxk13RKaFKp84pGsPK2fsOGkKUYdAGVCT1eDbYUa55ahEr+2p4/Yiiv7kmEB/DlDDPAsoHSOdjk+QFyC5VIQMbtNXHZf7GVp5EJEgDbvwIj6DJv/DZCQX0ymyGbVZTlmJTvdEwxj3wU5/od5YfoczsHlRU2DQT+JANHUPJUq0pdC3a14miy0JaKo11NcY+i5nIGR8yy/CoTWmOHZZmFZENrAsqNj83jlVspZuBArhcFMgdIW/6MKIKTOUrv4wpFbGMrYsOEAWYpriJOVVTqo6PkiDkK5ihq9tSBY7G52EXyPKnNsVXGboKokZApfKr1YWECc8QV2vZfrJmW5dt0AKgWKWCxpKYV+ejpYE8gtf0ZKFYQNZTSNK9NVubRRclLA8DFRUwAvuhAVBMoqiq9sxk9AojTLoAywFIBtSQ9w2xvbPbFAEheSAYO0qFc7YoAg4t4+0uRuFyf5srqgFFHDDU9Jo0CoNYzx9wvB/NQzzCxTokgmiFzvI2xQfxGONL3WhyksxVT+87BGnM6O0COiewfDq3CLnUj8Tpn1wLwkbltMbls63IWhaiX7tYs58MIw0Td3t7WqbV/r6ZXdqObq9ac3imOnKKAqDPIMBFhVwlHqk4flYJLuwk3He10St2Eo52yJg3rAasHcWk42c/YU8E8LwBk6tqsKyI1z0+uqNUFl+NarJNLud1bXghUSYOldbbJTlTRvIOiORFsDqSpTgI6MlMIqD6nudYiNka1J1462uHSpUvY7SqOdhMuXdrhppsu4aajCZcuHWF3dKSqZAClFwkAtwngIuErOwG+WhrmolsQNNJYQFMdDSCLtLd14FJAFb4vddEGTjWWwNEYapUZUIVlYYdGmCZIwGWb0SbCskjIEdEC0i2xLMRnmoBpJ9rCpUsFR5d2+rfi0qVJz6UejtTkcHS0c/PD0ZGp4j1ASr2YxqHdY1E2ScCiU0aJ4Ys8Mqv3upDMhGE10RaR59JYBoAmM3pU9VAEHkAv9R+LiDDA48Q8cz92VRgtKaeZSg0dOadMggyHPBKDJN6u6V9O31kDnwMszw6QxufdiBfvWztmUg42ypsvw36fbrsWgI4FHPhcp40bMMKZYzBIA8ioVP1op+/zQTGdkYMpqtHD3+9qCADiNCsmT6urFbtacOloh5suHQlAHu1w06XJAbKSTe2SFWKo7CUYnGa1vzVwqV75tiDtyNwt+aotWk5nA9XsYZpvnb1DJCynVGUM+r0u0+izJ2xrBmOcQl6Lzv6wdSWrd/TdJJ3/5puOlEHucNMl+Vw6mnB0acJud+Rqp4GzhfmASYCJZdpdWwQgl9Jc9W7KHEXFDoAMsETq3CxbAOsMHB1mwbyE2oFQm2X5sQqu1vFInUTqqmsFjYswsnlBLeIMI0hd7XYQU8quYOegWHHpUnGAPNK/OwXJ3aUdpqMdpqMJdXcAID2kh92JxdRj2aLbkTQFvMK2CpDOuRaB9mMqGgRe1Gln63wWgu1kIeNHYpFZy/Ql4+DOMdfiOpYYAebC3FkfRR254vRvz6CMJVrn1/6eN/B2zOJ157hOOp+K3aFheqGBjv51jXWrMBwXt/LasTQIZSaObzow5a3XjEekjRl/B/1wPRLGnz4pODowJiZpDWTkzNd7JDhQ+iIUedbIkbKqox12ky6eQAXcdOmsMgszWoQhLUtDLRVNV4Kx+WO27a5vsen1KaO/qcmmWpaiql6xRW5jFSFzJhgwmh2rAb64cFa9Y8c/AJW1P6kDYqqhPu92ODq6pCr1hCNVsXdHkzghzLNswGjmBpicF1/+q+ncZFbvNRFrULQGh3NF8/iCAEjz3UtnZQ0sZvjmBEndIDbFWma2CEawzNXW90wVsF2xpkVmmhAEdBqpT1hXy9ntSIO9i5T3aMLuSI6nI3PKTOKkMnusfjp7pZoFvJsZQDabTADfwEsWtIDaRaHqMYL9h/FgUIfTB/2xMXHvliv1OYWm2e+UopOBaoK83Ke38Ys7GBk6+PDDDinvcLrDC+aCA6C7PJJ9Jwc6QA1MM/+Otgu8wUptnnefjaByPZXHwBrRg6MzR2tA0t+lkZEFaGxSiav4ng8FJV88Q8ED0LhHBUf7TNVV60s7AcebL10KgCwFrWlcXa3S4ReZW7osDXNddPksqBohayFy9krD2KzMuyN1lpidsajdS2aOJO+q1guVVH5tNIZADbMAgNjcCkplCY9hqQNmcg/tNE3Cho6OcLQT9frSTZdwtBNAuHS0wyX10k47s8upN92YrrIOcy5xkZg8nsRpZaveyMrgYnc0gAxwFKdN05LImjR21DzIetGlzMIxE+DIDNQyAXXR74vaQAtAC7gVMM+yZ3iZsJRFJElF7OjIwLHg6FIRtnhUsTuS492Rne+w2x1hOjI7pNom1T5sgeImY25vVCdcs7+QL4sOpja3mxRIVdXZBkNgDYLel+J+u4ddvSbXZq1fuSzZb7zHWsigdCQy9Y/Vo2CEyvqY5cHPM1xkwND+3ZL4eubPl85ngyR7iWXOAMQqGQFQVtddRKc+hvpR45qv5KFcztr8BOPeKt2tlsWCaGgHS3K1OoZjZELoxfb3MmzqQhxboQqBmnmu+6DwSlAvbsHRruBoV3HpaMJNl3a4+aZLuPnmS+GgKEUWQpgX7Pd7YUbqkZMltlqqAt2Ss6mwtlmLqUKXVORpKqi75C3dCcMrI2OEqU/+Cg0UFxUNZJ5iaZxWgd2OsJQm0/9AvpyYqNESynO02+Gmm26SshpA7iYc7ST850jVT1v3kJzlGOCLs0Ck1phytU0gpQ50druo1wVMVcGxeHM1SAB9fGYQkwbVw8tfSLpxLVUGvULg2pRRWlSBLp9WFhAqiCbUKVYUd4AsApDGIC9dqrh004RLRwVHRxWXbqq46aYJl46qqtlH2LlqbSApg1qppCFJmiSaCLa4LUFjLYuwylI0uJ9MhTa7H6nKk8CHaPsDndtOIRvr+GIDOyTCUXyA7tUyixQJIBRoycDIfb+8HmaMOJF9I/b9GXHH0o2p2KuITUW8xBw9Q3ZutgJHzw6P+pQKsQoYz72Wow6Hn/lvQX2j55EtwNHQPDpkpv7j7onm2fO/xh5dHYNPh/QwHw8GL6FqWwDwpACxSwBZ1OsMYLdr2O13WBbGNC2Y6oQ2sdddg3TAmMq7oJt2RzplrdYIF5lErasWMlJspI+6lCKrilbUe64mlloBbhVgmYEBllVhWhHJNICcknq9c4/1kQJkVYZk8ZfZc56bRe1WFNdl5hEDFQ6kpWS2qJM6SQETuq4jbHrfgsUAstn2qVp4nVxCpN5zY5UkDHUxZqlB5HrFA7MLNbQiC0EEQIp6bZ+jI41tVAaZYx5dpdb1Kn19Sg8lMu2AogN1RCRUrN5U5NIShUwfZ4Pp09Knu0ZuWYjBlADOeaLoU66+byUDRT+VtubVd+wE0YXU32udNr0j37t1foZ0417snBwEh49leIxlTMbXs4DkKjFWy6kduj2PdAaWK3B0G0rkglI+xy0D0rr1ziDjEazB7L17oN9jxlbrCQdHXtWm1oKm6Cwxfwumaca0RFycqdhkjKiJR17mxVNikLpIgjpTOoCcxPCfGWSuN5sjXFR9tWcCEpMo4SWLAoOAOmtcYtUVc7L9cTftIoTFVcYp1r10FgskTQ9mPklKCgDzjgZgMptKbTPeJ419NIAkZY+2TWoToMzCRDZlE0ABbF2fpvparVK/C7XAJRZwlQUnCEQy4ydskDIH28BRpgta4HcKg7KQHpsXXiqoWBSBbQFhwy5MQQ05t2gOF3aGzcmOu8bOapwzwM1B0GIrEQBpJoqYJKnvNPJofQ1rPKTuYk/tuCtNgKQdb/8uLnWc7ZwgeK105wAksM0cibqCDu0SDA3bKrL/pj9wYHCWx/F98CooIPZMMmwnCHDMv7H35BpXwDNglFWWjUVK/s12Io8sHnBrm1HZZljBJHURVGOVNdb8m2qFzSPlCdjtFuymncykWRi7nbgRaylYasUyFzDLYhHQFXk6Bqlze6epoh5FR9wdyQo25s3O7BGAzuCRD7O8uxSZZiehPAsWqurd1fsaQIglxcQxo6zxSD7TJMuYuUOmwle68S5KHKRoJQIibMYc3dlg4KGgCFs3qUwACmyP7gbIYhzcsLSC0tSep+FBAKFRU7OCLuSgC3IUkp0DSyMs+j4BLtvZsKCWhlaaNAWi/s1Js9uJB3u3qzg6qqp6h4d6mib14E9qh61eNgO8xgYr0MlSrGtUsk+HlAU2ECZz1kGts1kFOAYwhiliYcbMsubkwhxbNgBqx42/BpjWm7f7dJrfQsra/ULPJDvTHfOm4uqnfo2DOB24/zzpzgNIwFXYCPDmzs4qrCyxLaCL5wTOUAZlcnJvP9m/S+m9AIIxDrbIjsJ2nTE9M7PFRfUxoVgCltCOWmVDLiKILdICbiH+0+psktMsG9lDxL3dakhniM0QYOzmHY6OpAZZp49NdS8LJSwLWjuSeda8qA1y6RikddA6FVGraw5Mlj2vbcWgXPRFGWQGSFtOa5kb5tp0TxbGcgSf8wsuyUkjy5YdHUloz9HuSD201QcEjwIh+Ihne2d7e2tjdtPo/IZgVdaOHqJCFbJC0QSbGMlgLK3qroALWiuoC2FpBQsX1GXxhTqWtmAutkScgmORhTmW2lBoQS0k9UEFRHnFdgZMa1AVe3KA1DCfo6KfSQDU1qS0LR9UYFsjDfZGaFCKBLywA6S1ke2y2BaJg2z6ySv9wOuDHBgXBhZumFlibWdmzNwwQ473YOz1+wWMGfK36UfcUuantn46sqfN7hrujWTmMtIRcukGl9y777J0doBMMX/bKTIswaEquGbuQFRcTPexKXrjiBPB1+Mrs3PCGKRf8r+pMZw5poFTl93pZ9WsHhJqdgJIGj9qa3QbpHV2VeFkE3h2lVvui2PYijEq9c6KdOZIzESZ0BjYNQv8rag1Fqhl7gHSwVEBkorM4hDHjE4n3AlYCkCajS2YeGH28JHGHLGGDVgKo9aGNjOWSfLki9dCZ6fo3i8+x1q9sXkKoUzbYx3QEgdRtmLKJCu9iVWxbWUaUa1FUzEuFMySSkwDlCmHGuPaxP5Y9DlE0HnGMqvGVzJSdtmasMJCC4pusVoW6MQADY/S/1rjCMMC3ARwdFQxeZiPhjbtqqvZ1UOwYj8cGxRbC6+07/ls5wvrPkO6HYYzSnVeLdIubdFz7sWa03ljVpBUYGyyR83cGmaWmV3GJO2vschGdhxGgOj31DtTKTTIuKT9dLRJIobA+I47DuMylOhkZpYdyzxHOgdApoONFzlDdEZGSovVpqR3+D2qenes0kscwaPy8PzCYBOerXxrpt3UXwt7o7HHRGW7cqYy+mYeUFBsnaqtuo4SlyLXiWUOrjJIst3sEoeRaZnZ+tMfGwuS2SiM3a6qN1lAoRRjj7LVgSx0awDZAFLgVY+l2yF31ecET5Ouf1jN7qcjN3MqvrKRxN5subFlYbRJFkAQVU4dFSBXDat6snM8nzscSmoLrQ95t2kGTYLg4+vo3A6O5B3c5mTngSbiOsVhYyolFdKAcgXIQmhNAbIkgFzk97ZKOgEKkKJim11QVn03MAU4A6Q6noRB1g4gPfBbw3civCl2VGxNbMuLDViU5JJtjyAByWXROrSFc5nQFouTDJZv6rZ1JR7AcXEG2bDoJ1RtOTc1W8DROFTW6QQqbYk+D/52pojU/waWOThqGMosbSBVphnh/fHPSJiQ5Pm86U5VsT0RiQ2tiKopLDJVgM3GMHDR0SRru0jHGQQFW9kBOa+NaKmDUwfCUcUeXpR/naWGAVoMFBuwMGhpvR1Sy0xoOgOBRcVui27LQCkaz8BS/pIus+JLkPkyZGKTA0TtEidJBZUJpS6yKX3jWKTWGCQLQNo7YAySdCKEzpBxj/GktrtCXR1YtTarb4ujZsS6goscL8ZQFLDA0cmLxnXaYrTVp1NGSJF1K1F9JRyFqaGYa8DWmeRQE63TN9vTFACjpfAXC+yWsjpIoniXkhAf2/hqEYdNa2itus1xXgqmZcGyyALCmV0uZcFSCurc0GqTrTWSGYJ1zUWLMd35YhMxvdBWCd/tJPB9Umeam6ZUJJYZ4lFfRI2Frb/ZDBxbqNg2m0ZZAvtAIp78pREWZ4xqNWL5zCxbMOzbgtPWcLo0nC4LTpYF+7a4ei0qdxyLes0eetjAuiqVtqzuJ+xLtB3yaCO6auPQBBgC4BlkTUa3lG1OR7xx9azpzAA5LhZx8F15lDBq5fRSb8kHxh4hQ2Nx9kaOjJ29BVDPbYCkYxr12ers0Q6KpABOkUdARiwfemJ0NkkiN9A0B0gsqjqTRQ6pwb+Jil24wDY2GtmiZNrWaTRwXMC6OXUsiiCq8W6yIG4GlVj2y8BRniVM0p00yiRt9BGTHKV1B6E7/SGBVd+8bllheKcTRhPMRv6W5ASwidoSklLTQgcl2RztTb7WJQGFFjA34Y5ky7axMiWO97GBpLETVv4oS3dZe5PODiqq1kvYj3VckSJhkRWsoT8Chgtaq6itYJ5ntIWwLBKJIMydsBSJ/VyKsLRaSmenNTkOBqn74OwKdkeyQIkzSgfHcFbZfuZNbd7cbFqpLnnXBBiXuQVAzhL0LoOKCb159xtkEY/YDnbh3hlj6vS+MfbLgn1rOG1NrwV4zryow6b/RP+TuFIzO9igZBMwIj5a66hb80xAIVRqvcv6f3RZH0ioY69reMz/nifdmIo9Xnc8TLDupco3Zv0Xjp/QypJOrag58mRjdQaM18Brw9f+JYkyZu815R+l97CwQKj9UM4jzMfYowG4PLIBrSTbJDszDtU6juVFyvR8EVqdv2tT7BxYdJ0BAkBF4xBNJTW9adHnZRbJsNUIjM2YTVIco1thNVF3PUCSgyAzgRed2jcCZCvpgSlmjzIQc1LJ7D9hdKCmA2XTskk7xLYC5H/lI+yEKQ1CFOzD4jyN1bIGR0voj+y13XTbhsIyM2dpBG5NxhcwFq0/qB3NHCb2kf2BNBaUjflRgHSRpd6mXR/WY2FdtvdMxIC6IKoNMhMC9U43Vatns0PGIr3SFhw23gRREgsKB0rb9kH28pGP2RxnBcewPzZXwbNzJhtGIqlDhdnjmb3rJ6yQejWQRKDeAGtjV+3gxlOUM67w8KSzpxtnkNe8eSwK4IzQOiO4c87kUJ8OU+0vp3MEm9xUk2k46GyOlDCT+iZ15gjTLQUEPbSndXZIas3DfMQOxeqc6WMhFYd0LGdnzGaHlM5varYeW32YHVKHTmrSgWQdRP2tggmpoiNrl6v67gxSO3hBHAeGHQZIr5sAR0AAsVWo+la6vzDbFo8tyqn5OHUejRk0W63FD+YQZbO/meNBbZ22C+DYOb2pnS2rqk1mjzQboTArWSJN2Hwpug1rW1AasHfAlTYtpCFPeq0Q0BqjEmGxgH2rK7XBl1I87jGv65h3KvT1IArg1j0Wh5T3D6sHBcLWAWTMsrKP7FBoHE4GERs6xXKkoMhwINxzzxbtY4BpdkgL+cl1n2GJ9F/WgHomSsSGlUnmLhrUMPPJnAZE2bze5+LG7Y/AnWGDPMvblRT63/M8dwBH0mtnelwijd1wD2zXvr+bfZYMGJ3XWlTsBJJuCGGAbYFRi4G0hSoiBtI9npSz0wOl5cGkhwgoteooG6jGPsrmKDZZZitskAqQYAdKr0Snt4dq0XNn1DVYpDIxLqpqFwXJosxJiyCOnX78tn2irasyFtnSlZZQsanJNTRXNfMzIoshHA76tpCGM7OwgZqanUdhRkNVNimea7FttlbEUw3GUoClAHtukGXPYhmxVkjU22L7U8dgEiyWZDEOXXDCAsWdPWpsrAAup2YJFmn78rAxSAvfMSeZDx6JWSWOYBxlAYfN0UJ5mtgT90vTz4JT/QhAtgDJLQeNy0nXkSIT2WDYfTXQQB2liUNu8k8Og+Jdk25AxT5POkNxxqHH+nI63hie1nO0V6/OrBE9OKbs0fBeAyqbPRNTCqGMUpkOG4MMb3wwR2WLFIAo+2THijmxAGkSAObu0xFgFrVEptMZ55aM+xnFXARZw9AAcgBJrUC31WmFr5WEHiBZKbuplDFCkduAbEqFWiLCDGDMB7ahmLFEAUf2zwKUppzQAHTIEomZQPRc0jwlpugrDOW/seqQASRp2VmBvzmgMRpZ3KHZdmU+d60St+ariwNoxLpABKcVheADmi0lN9XqC4TYikWyWO8QC9ppNeLuiMnVnBjqur08PtDaU1msGx9YrdQdOLKr0sEaZQuG2Y/Dm20qeTKzrtHL8586tCOooTXnQse5/xYaU815dO2PrwdMIyCfM90xFbujcNdCq5S0knRczARASRMroWKvIP/dVhY2gDIW51SLZv6eNg5z2ZKa7fGLCTT9vKXrMCKWVGpAt13oQdE7rDHJjXpzz7yCpAihhJEwdEogm13P4v4aLMyFtGN7IQ1hTaQdGGDIm6qhr2TZg6i/6tVlzE439bJFNWLZf/PmNsTWpqYyK0B24NiAot59YlnTD2EOsLb1KiukMzHkhgyCbnP0ZdlivjmtAJKtqsV0QRZ0zmDdoIpZWF4rSa3nbCZSexvJiuqsDWeLE9tqSrYxmAOjASgsqMdMTdLGLl05fi07wvRD2sYyfzwcjuYUMcl0+yMinCcCw5PNsS0OmAaUrl4j2R3J3sE9SI5MUr+zes5KNJks0dAbnEVan7RRgfvjIXWmIhXvnouePd0xFXusBPQdvm/w+EEAIXu5kc4zg2PEvWMScKQQk6RG9LQ8U8g8QnF6Vqp0A8DslOGm9ki4ik2Lip0KJjXd4wNwYKxUMJGyh5L2WkmqVzcV07HxsGFZQD85V/R9UgcVAiq6MjkoGCSZaBpzrCLqFHUxjhVAqLfWWOKIWTzcZ5k19GeBTofUKrQZJSxeV2aG75vHDRYoP1VbjLYBVW2nFi5lwKgdnWwVJnME2aJypBtx6ZJuZUpA5GE+AZxekTpCW3yl4EyVv+oJrgZIVRbFBWu8JYvX3LY0sCXReAO8ZAvX6vmaNMzKFp+IDFmTl5BjZhh7tONY2VKmtUo7Sn0tytot5MaYPxswQtXpxu6lPl0W/7vXjx+3Bft5dvXaWGQ39a/ogNANsgFsrOgYGjUFcimAjwtVxzicNBAGzEYfr2K/WZ5P3i+s/JmYnifdqXGQI1HbzNMwABBDwgHUW8iqykZlo6vv1YvGDLisUU/fr5XvPDoxnB12zpgcHO76YyBahl/beKsWU6sm37nOppFZwHSxGROBy7q0GYNKQ4y3LZFb8UyKjWtdfrdxJlYRBTVGEaO4BaZb3JkxQ2eO1mamLjdgnhnLLHa4OBZAbAuwaJye2MSMQY7qtc7trqxz0xlHuyZbIcBIUowCMgB4IQG24G/b3kHmL/tybgaYJcwbVIw9Wg+SAaSwzv1gKI2UOpaV0gssZqwV3bZCK7tBHXQJHIP1FbcZS7hT9fhThc5OZDnJd5iFdRAwUbObdP9uJwVFBKixmIMaix180f4lssXCBEHiiAE7KJ60BSfzHifLHifzLJ/9HqfLjNM249QcNcgMUuWDbPwNpyeZ3FjZVMCZrU05/TiRKirxXIay+wBD7gSyr5JrscnrYseBdCd6sRNdHv5mv0Cmyha24aNOM5XNkcA7aedHSBPc3c9K6f2EmBfuFXKgZhJbzfnzKYRZ1R/V7KRSi5Bw2JyIYl+WUgIYS032p5xBsxElm51XjtihTFgAlhhBV+NEkGwWiZWblFaTXzMBDsBhBVqvoZG52vtcZTYQFO/pvDDmvXx8XvbS4r7GCSDNCaXsEECtDVxlTUmwTGGUzb/01bYFQGeSUAMG66IUVFGohiOm23TMmFrvMBFmZwJa4Ht/E6H4QoKkAfr2O132LS2h6Iv4sjIXgX2RUfQAGQ66cNSFHPbHYbgByAAypcB3a0i5obAwSJuqaY4nDyonU6HRxzYu9lcZo/+dXcVeNAyrUajXkgUtI2dwNM3R5CjJVoclaaCCDdp9teRVijJrZPaf+Xsdf7ur+T7qfnO9dA6APMtdjLWK7V85SOZBABoqA+9AcPbog6VVBrq61AsGhNRXEJmwauOtsxr56kalCKcIT3a2Q8JBw4OYIU6KmByWbI82J9m2IMi79Rl7dJspXDUlq3T9iFoR1eYoyBb/F2VwyCXofitx+7oiDCatnruRwjNlgC2sUMJJ5rlhnhnz6YLTvQYpL4z93HROsAJpsxhPYR5mJy0k7JEnKUcBoU3wbRNc5IkcjIJzFcgCtVGPtv+2eatFtaZuMWAD22DVSag0ZpDSf+5MKxZiJW3LxbbywgCQWfU39TkGTEpg7aLMJkUmkvZdqoPUppbxvCakDZaigZu8Np0eqT5n1hgHRqdi+98VOC4Bjq1hQYNE2obnGjYIJ3GixGgs3C2oHufiaP/0mlgJqHXNUKkTkI6kCUYWKB7pBCJnEGdOd6KKPcC5XTIASv0N2tHIAlodhKQOqQ0Cw9crU1SOsUepm9EmOjSCg2PKn6vSCo5AAGUCy+yQyR/bpMs2rJqq7ei3Q51i32dfCNVWbNE5wYDE1y3cNHhSrEYaNYiAyeKqHQBdHUBsoLKkUCqcg2MPpM4U82iUBgppm+aOF9tlcVZw3O8XzPuG05OG09MFy15WQbfvffEEFvbCiIa01YumHcQb3QiEgmkW0wQXtS3C9mwW/BKTRIXs6V10YYcJRDvZK6ZOomaXwQaZGeQGOArYiN9cbJQSu+e2xQYBRWWQRQWUSw6/8eh7+N7ZNmRa4H/e1iJLNWsfzwHTicn2x1YPtmdPlMmwadF4n2URpbhhlgBwEE4BnDKLWr0sOFlm/Sw4mfV8VtXapxiqek1iIDFS4oOXZD1UYDXVhIprMhZsUMRBB+fRDut3BnPMtnJttd4UlJ7Qrb/QPfV86ewMsl2DQg60zvOWWZr+zRUljcnOLuxeTr8ZYTfj7Pq9ISTWcHlokzwkMDUuFhRAXzKwyKw6IBE7wNd7FGGFC614LMMpU2sc20KxtmqLPdkGixkSrGwB11H2oss3VF2nkLK8AMX82BydjKAqdrBSTk8M2jGEGIFDLVY1mZuti9iwLAuWedZtIWacni6Y9wKSewXJULN1phBELSuI9R+FOcogUWtB01XKY3SFs8dquyzCNq2quje1eIR9j2s1aZAemxfbFwX2zp0FVYRFZtXAWaOFCEm8p+ZF/pHhsbEMcP6sAEZrW2eCBiipyUaQDMFO0k823OsvSGypoqarA0rvY8igRhbeBegUxYIFpGyRV/OsT5cFp/PsDpr9opvGGYPkpqq1OskQAKmiCh0nQI1iDQQzVWVZc/NV7pecyh5HXf93rz47K7Q6KflXppEN+HBe9gicd7mzAykaOQOJ/psYZMIkB8tuBBjIjFXAuurSmY3GBK+U3ms95gieL3tLb99M4Ng9IaDZ+pZ/CJ1tScJ4orOWQb0mX87KqZ3bXhvIpzCCYnarMUg27yZbZyffVkRWZ0kjLPWe4DRKSd3bf9nuyTrvdwDL0TYaQKlgucxYjD3u58EO2eI5JOEcppIWKt36hcGUQpasmsJ2Z9sy2EeBsESdG7j1zFGeuM0iAyTz/e4UovF6Btmo4FCpTb02Fgl/TmqgDiDXx+m5LnvGdm2uvv0NGDIBZbIlUcb51g3zEvbHOf21j29HwWmgxAYz03oQH5awaSJOchh5F0E2UqLfuq0yiegqpbbqKMPwC0r1mwfAAQfOA5J33EmTjLMGZ8kamOOSQ+7zB1Jn9n0eMfqnniWTaWSLi91RrmM7HuuLxvuxcQ8lBcptZNpJKTlm/GOqX9pjJC9pJXiogGYOgwBE+ei0LSgbYmE21NRiRgaSOdMB6gG2BozNAbKpKp3tjRkopW0CRINNynqUbVkUKBfMcwZIm0Ipvycw2CkHgWsD8xQ2PDYPMpw5utMrrQKUbbhbAOnfK1DaGqABegZwHIOUCmAE8K/BMg9qBcaUrLJD3TXg1qf00jTKZwJZa8fuV4l1Rh50AQpl1DacCEsvPstF7I1qc1Rw3C+LeKWX4WO2RrUzh2NtozfluihFGaTeWIo6GKV+++JSB5K5j231Mwtwt+8yBXODkQ00lM7zADi+4a4ASKd4XVJwpJz1BEgGRBay1bHHjccdei36wWA1xfcMKRTZOE+P7NIIjC4IbnMxoz2nYHDxljowThMmszvudrLNwLTzEJRYEBXuRTaWRVqfsiufrMDCbofUjqjB5rXqeZVc16kqGJETllj4ALCZLE3nfpsDxVcOb80r3YK9I3A9N8zIKg00dapgW9IsmmCQIbAGARa3GbZGX6ZMB52avNK1c24VD5PKweCdWj04RnpwRJLd3MnGewMs7bfGmswOZhJTtt6TAXIFjiFpDsj6u5J+T5YRF2CbelMcRJiFKe6bzI7ZLxLjeDIvOJlnHO9nXN3vcXWecbKfcTzPuDrL39MlzZjRgHAblKNsyhJLERBK03+ySs2NxYZsFgK0yLviRcwt7ymMWzv03CGDKPJDMTPI5MnUfsnL+0HFptY2rhoCDgZnaJMafW5RGd1kDgXYUUV3ExSijD0rgi9t5gS9E3jLAflPdI2GNBInAB9z7qOjtb82itmvjLUVtacl25eFmZSqgcu+6Xu/mrbvQQ0zyxiQRKeQZb9k9oPZI5uBi9rTplYQHlNVVwsBFruXKXxaDMPYnwFkMMjUurFOxHoso6hya4mYfNZA3XnYRKkDQdluohogVohzpeQNzWyDs4EZKjgQEjhSqLa2+Zd5ejP7I/OGDwMtd4KWBS5AYgV4PqCtP+Flpugjdi8lGTUwRYQ0ka5rGSo1/HfMJsB5CmGs8p0DvU/mGafz7CBp8Y0n8x7H8x6n84zTZZZAcFOt4cFgMHNGgQm7roZUtP7V2xZ9lmWx4MaGqAKkKtu6VH4MBEP/67W/YJBmOmgUv+h3YQyBZP1O7kkNnOrwrOlOsUH6i41FDrdGIHb+zipIhSxnvBtFrHL6VzGFeuMjxsZISzoqmSbljWJgyWMDwZ/h7EuB1zb9IgNJDocDaRByhJmUTr2Oj8bqkczACHDktLADe5U3aNhLOjeAtJ38SPddcXseV31m4slmc8zguOgCscoAmzvitB67BTWRBsIk1t6/e1uSqVbU/1wAUjud7WaYdzUUlqgLTQzTBgMgEwCl46z2BesPkHH237Eyk7FRVxl7kQtbfy2rnzT+ZnxiAksCIgwIyLsWktqwOxVfn2CqbJhc4OpwMxXa5lMvC05ndcLMswSCO0jOAY6ZPWaVmjSkCQUVim0lwBHWFg5M2ldaUVuzFZe16oJkjf0x6mobIKXs1NVxMxCEEgfFASIjEnc8nR0gl2W4YA1rRZJZHy4MAzuLtRXh9M8WzRT84RA+F9r4l7IsJjn1PrxRHwaESvwEzJLdI36W2aaqANbBdYqEhLiR7KpFDLQG21IpWGPR1VqELYpqfaR7Qx9hmo5kPxhlO7GSuIDj4g4NUXNlZqOuso04JwPISqhLATDBNqea5p0sQ1YCHF1NaYsY3ptsHzDPs9qcmnqa+7rIIUj2TQaZDICklW2qsm5No9umRqMWXRDWmKEt+WV7g8v+LHIe6yVWV7OdXVH1FtxWo+16SYHhvforvXfDfDCCpNOUDJLk9WX3urXJ6judu3x5HtQ+6fmufl7UK12cDae2VHC0DdVsEd2ZJd70dJaQndN5j9N5j6unp7hyeoqT+QRX93J8fLrHyXyKq/tTnT0jKvZiIV2KkLaIhhGYlgCSavFYLcufrdIEgm45ITJArA64QmK3Ie5IS9QiuYsOSOo1TG0mZ4cMn92IrIPaHK2qgD3SOl619bXTHWaQZLXgry8g5dsuFGZ+Cj7cAWiwEjXoWil8iOeQxSyA6wkGka/hU1nBkUwhHe+z4cw6vgS7UWMRBh0RZZUdFRKWNQCt89bdhKp7PsuWqjsHyTodYdodYbIZNAzIKjHSsZZ5EY9ws5kPtjlSc5ZnGyQZayuV1J45gSCf3Y4Ti5R9FEnbj8HiRFk0PEfDdMwjbeq9hc1wkXopqdI7lqNAb44UY4BUgIlIYwejnmF5LrEHi+zoV30DK9mrZer3ilazRNUIAIFdH/J6FultqWCu9/UOFs9Rn6JHWjRKJyXhADOQLOlHymayRmKynAaRAHgClZiTD4uJ9QiI/J0ApoHjzE23AWloWCLIe5lxuhfV+WTe42S/x5WTE1w5Pcbx/hRXT49x+eQYx6cKjPMpjud9rPm42CrhktmCgokoAsMteNU/Rbf5IW9gbuwy7QNJY7GRN506y7pGJQOlM7FpCJWbdKhXo0nMTUaO0nZ37qnXSbh6TujjOs6f7oANMgtaU+w3idBtBpBGzg4cA0Ct47hxlnsZdmvIhlyv7Au0fepLB3DELfriDroYqT/JVWjIwhQFEtdV5A6uGvPWdC4xUbI56nzgqUrQ8jR16nUpk26QpULgXmM4g1w0xtDDaFhi0TiFXIDUZqc0bZ5lx8G6AG2p/eregHb4YKe+nakCZGaQpKp/LcLoC0lojqOydXIHyrycGOmqNbpGpAOEaQnQhRrSHuBT7Oq3ywvIJtZo86mLrYwDnc43ALc03wB8jpWU75JjwrrzMLoutfJP5RV2DAhyHoj6H1D/vdVb7NljTLcmcCwKnmFPtbw008JMqwDcM703O+NewPF43uN4f4Lj01Mc7/Wjx6Zyn9pUwuSws3qspJtYkGgETD1A+qZrhUzIgkhkF3WpAorFKphCxfbbpIyFoqysYGgORwFE0vCliPm1FQukWyb7qT8nmmRjWLxmujEGOUiV2aBgGcuqh8c5kQCjskPi6DSS895ZwyvUzC/cON4ARv8wvKFdLWYT2NhcSMpCMSDanFvtCGzr8ekXBGGQRVeErpOAY9gaBSjjo84Zq0SyfUJsu23WuMIAyUX3/1jaAl8Nh9TJ0QjApJs9sUzTaxMaFwVHW8kZ+h61P3rsonSqZRHGKuWXekK1PLIvipANER56Q719sJSmziwdBHRBDe9LRdRnc8AYOJpqPelKN7X2jhmLLU3DnYqXAhWinUa1zW4nFbhDtkKTPGOPIqoDGwIFSI7r7MHESN+Zwy3cBqr1mMDRQpTsWOzYEStr+RVw5A4cZfdBc8oIOBpDPNbj49MTHO/3uKrAebLfhyq+zL6NQtPprCAFKh3YWNvP21UYRrI/Wv+RXMliys1+FCgI7XiN3exFnOXKBt+o+0aZRZKySPf1Bltkm28m9zWloQ45SIPdOVDy/AAZQykCBKHZCnbo52nMtt/28U+UnsUhWNcDx0OAiREY1XQI6OQ0uZaVNAJcGMCsXmqPxhP1gJVJESHtyISJSKYUThXTTkN7dlMK8wkmWcok6jWgU+9EbVt0n+V5DnAU+6Cwu7ntdde9psuFAVShIT4Lph1jmhrmBVjaBNmYqQ+4tlAcCeaeMe8X7E9nnO5nBWO5r1BFLQzmCeAGqsoiSmL7yhzzHOdShdFWFhCTpRzZf2NqeC2UADJU7NjASj47W/1IPdiiWvcgNQqBgeOaKSSEPACOXlPcX7SOigEkSdlMr8SNQzMCGP2/miYRxKK5oNrFcpIz5mxLtv+aAmPDfhGHy9XTU5ycnuLK6TGunpzgWO2Ll0+u4srJCU72p7i6P8GVk2NRq5dZPi0233KfnoJytc5jIFMolsdXOQg2AcgcYYm+QKkyeJUWdcdF+k5l2yZJ8Vaf16GFPNtUawE9OHtMOy65974CaSsIMQtYLKc9djMi4xrp7AC5mmroOA8du/VYKfSh52R2mVibwz2AtYdneO3W8XDJRLTkvxzgqIOgf2dQblkpamM00ASrAC0kjhoWe+mkHVi29NwpSAooFp85o7MdOuYjWkdz9VcXgVg4gNIY5DKj2S5yus+M5LlgLrE9gK2Uw97dbVAzVZ599ss8z9jvZ+xP985aCYRSZesA0eQ1jAbmVICDhLBCjU2sNk3QFpEtHdhYuJzdu7PwnamI3XEa9mepBaX2ThWvOzJGpyXsBmz27+zKKCI2GPod3P18fdxJk8mJSA2RtF3XrY31GUtMLLDzyCe2GM6ksKfCAThAwJYr22vozqnaGY9PT3F8Iqr01RMFwf2p2iCPcfXUAFJU7FOzOy6yxqMEl0NnZ5VwZFJ11o0UbO8ssqPqDLcAZnaSO6KG/thOoGLfTkOOMuxc3+6codhZvlEwSFnOLRxVhSlWPGddeYilV4yM8izpDs6kIc3yGbX7AznzyzRe5wSWocLHfZSuW15sZKIQM6aunXxgZA050bcRQr32D1Gs2pNILYAAyFpkEQrbc0Q9r86y0oo9Vo0ehN0CHPPSYGaD9IVmm23iZCuzmNqvsY0JGLvuTIn8+/sMJGX+9LwsAZANqFXAsRYZt2Xla/YyWN27Q6cEUDKLPToN2gqQ5r0uqJPcO7lpQhno5vTAnimG8ySFSGm5C9msn15NPqN0ShPnsWVgjQaOETJUOrk08A1QTACZVGjKwJgC3UOmtZ5tUAPU3GIAqSB5OuPkdI+T01Mcn5wGUBqD3O9xcnKCk9MTsTfu99if7n0Js6XNWMyrpO8P0hDTOt3WmIGxHzO8/gBoDGJZE2pVr82s5dq61a2abOKhql4nJtkGFVtMA/KXuDlA5q0hBBQjfOkuAsjxSlYr0EngmYTRn8CrK84GHJRiwYgcRO74SAGOGJwuMkpxN1plkAyPNoEoHEc2i8EA0oGZY25tKcBUqnb2it3RzoHS7Y0azuF7P2s+TeAttMLBMYOkf2I1brYgbAWHTHcIGrTs8pyABbbat+yjvMyqYp+qo2aRxS9qha7NWFBpAUHAsZht0qreWRF5zKeAUxWDfloUpA4M0uIeJ/VkV3XcxBqO5OUAMksMQedmBnmTE6mXVmSh2MY6cG8O7IMcKpLaALZNBYIV2pAaDqyEGQqMAo61H0TSXHzTLDKDNOUqZi9ZnKPMnV5aw7wsONkLIAowHuPq1WNcPTnG8ckJrp4c48rJVWGW+z2unhzj6ukpThcJ+zk5PVWnjNi1G2TgM5VegjcIsl5STO2MmTvow0Asz5JT6SKUwBFwW76oZvD+ZOSkpKB4c8TZg41BwsCRbMJE2GBNxS4e8dGwcIlZYpB+d5cC5FlSP9aP30Tia161swSWnJhkpgMqRNn4ZMAgTNKALWi9Rp95G/egaaMopYFS7GbWYQsZGxLhkVg9AYibjy7h0k2XsLt0hN1uJ2EpSuFkuTAVIJY5yhbaM6vDpGnj+tQ9NsbIsNgxq2QzT5Ee22rZJmR5JRuTKGb2ZcpOTmacHO9xfPUUe507TSioU0OdGtoMFK5AKyCWzm5rfInjJRYDblPBjsW+utSiW7KyE5NSOIEpsJsMVNWjvbPpmbaIMLy9G0vkQawwpFMvuclq5YoqRLbHtQ4ixr5YAPPAtKle3jg+wVShdnWRGgJpCBSJY0uZtcmce6cpqc4KQD6LigpInXihXZgOlAZGHTjnRRjjfha1+OrJMa4cH7tKfdvtl3Hl+Cqunp7g8tWruHpy7GE+x6cnONnvsW/ikDmd976/ddMpgFQIVMX2XhpjKsAE2SokL/7h9shiNUap9roSiIx634OEBKmDqRBrHUk/q8lbbwHyqttLPSsjzQzSQDLvq+NzyZnFfs8t5pSbnnFgADyUbsCLTSshE+aGdJ3Gb1eHXXSSPzvYkJylSdxJZ/OQITMY+sODcoQtiEPl5jVzdLD0YlGMbEB4abUBa7IjyTYKsazZpUtHODo6krUf65TYo6zPRws5q1nmeQWMNj+6qV0oG9TcFqR2GgMb29a0+N+YyWNMpilvbQvQFlYHjYDk8cke+72sxCOLzjKmiUGtYCoLiBaJQaw6E3xgkUWDvQHxdhfdKxuwmTSyrYLUn+R7siB3DQkSFinlMfJhYmEgZx3PbK1Sp8FUQYSie+wAYpqgost7MHeEZ1QJHRu792ZxDNW6KB1itskDxhrRxzFa6I4FsRtAkjLHKttF2AZiDNIZTQGKszrs9hq6s98LwF09Ocblq8IYrxwf47bbb8eVY7E1XjkWp8zpXhnjXmyOs22ZsMzhlAELgFdlxI1RlT1OIOxKQaWIz3SApOjBEVRjlRWLjchAPkw3LhFmZ1uTmE02wp9M5ZZ4Y/GKG4ssHYOcVcOauWFalphL3grm1tBKrKxuAHmedHYVO59w/8VqUE6GZrupyxebO14r1RA22/rA3ccA1d91nYKSPpdA6FcbMlUocujuE72/A8g8D7jmVXrk726KNR6PjnbY7UTNNhukFlfUWyzObCzMZkkgabNqstfN/vh4gAAaWzF73MkvT8kTkGbPh22LMM9NVOwTcdbMs6jT0wTwDiio2E0LammYS8NuEoN37Omt6lCR/Ao7grBHZ72ihgqDVDZZxTtaFCSnQrKRVY6rIxMHTjbGpluqiv21NQVIl4jm4AhilCbMMbabzVKo9WGylXQvN124AdPs1wkkSafSaeOYuaE6czcWP0WnL6SM0bzVtj/3UF7o3jEagjVbXKOC3sl+FhC8ehXHJye4fPUYt1+5Iuenp8IkT07EETPPOJlPJZRLYx2bqtXmSClV5joREUgczJhYARIF1VikNqDNXAtORlF/gPYaZaZUXHOTvqgzrayPkW2HHO+gqgM7QrVnVe9ZF+YwNVu2r20e6lSp6FJuC2YiVGqDmepM0NGlGwPIgzcYNUaApJ0P1JaBZNdkF1l5Sl7+p5uu3g8BnEU3he+SdFLi8E9nlmganIGhsMr8V0c2om7jJw/nmWxjqEk8sHrP0W6HI70+TdI5BBzFa2wrIouKLQvOLvOcAsNDlXS1WstjoRAyKhOKOTqmCFCXfW+SKleqC5psyypq/n5u2J82nJ4sOLm6dzskgTDtZAMuwoTdNKOWSUByaqoaMnxIKdnkIPPCHRw1/w6O+tdnqCnzmmowdFmpJ+Qj5qYLK2nLgobi28t64LwGbVUskHnDC0pZUGpDKTHAOkgmqpi32M1Luxk4OgqaHFFR5simawZAJgeMfKYIgyoF0NAeImWQ1tltEGCLZFDWeLrH6ele7IzHJzjRUJ7br17FlatXHCD/+vbbceX4qjPK45MTCQBfZuxnjVJQGzaU+ZIyeuzEhFCIUBdGbQKQOxQcUTDIWpTpEungEMuh2TRjWykgdDNz8pFCQYT312LLApLu1RShT9nrz1pvOrpqqA95mI8FuFss6H5ZMFcFyWaxxCKTw1osZ0p3og0yUUknjxvqdWLkBhjxBaDm1ASKGSjXvNSPmSISARb3yN5UomrH6t8dUI7gaB1WBWOqk4PR0e7IQ3l2OqXQbJA7mxlSis5CEOFfpKdr8KrQfQHIva6oo4HgzoKsjoyxQA3fDBTbATCC0917buFFeb1JKsqGBKznmTHvm6vYJ8czTk9FfSMqmGZgmQmF9thNR6gkIHm0EwcIQbzcwg4JUHtctcB0aytbrLdAbU6c7JEGrMokrUOQucgoHFpqcwQalqWoKQLghTDHymwyCGBBRQGwoFapd2oNpTWUvDRRthN0wMijyHl+DBgZrKslyf0Fyfbrg5OG8dSqrFjZvAV/W4eHWe1kIPCg73nBycmpg+KV46u4fOWKeKpPxM54+cplXD0+weWrV/DXt1/G1eNw0hyfnLj5xlRqiDgpky+wfbqNZBQqmBqwY8KOCUdccISKyUEyGGQzc4f3WmWRrHZGX3NK3lVYJxtwNltVAUfSQd40tTqpOSZCn9gM7rVETCTZLCJ2gDydZ+yLrEq0L1r2FBXizXoOlDwHQI4c0kIw4GqCD7amT1FSgVRtlj/CLuRvznFmjPYW7oFw62OdDeSdzyL4CizuMYPierkl46G2mbvH+JVgZba/zKRq9NFuh92R7TGjHln/fXR0ZtnsCGDAwnaWWTe26tdklJg09mqVvzYzAYDaHvMCGd1yazZ1zdXrKCE3YZFtCW+2rAAuH2myhkKyMrg4kTjNEReAY60/aH33c6ADDA0kq0YFS9uEuu1ACbPvUYhKJyYiN+KhJGWQ8Pnj8jN1mCzyTpvPHmtSmh2K/A2xW17L/pls+nXJMG2IisWbmqkBHWusKfg77IxqPkhRDSAL5QkTjIVf7ecZp3vzUp/g6vExrly5qgB4Iir1lSu4enyMy1ev4srlK7hyfIyT0xMF1RNfqWlpsciMsdmq+4QDAFVGWRilsoR4MVBNxSZSgMwDhLJ7lHAo6hx13wFUqKZGjwiDjHUQzMQig8hUCibbQ6gUtd2bWUJNETagVJkonJ01tgJ6XQqIZRejPS3yHgALaagclc6TfdZ0Ayq2MUXtFJQaO5Bq/BFUFkLunEkGSMqKPva7FNNoj8YIaMb+zAYUTLCgIKaNml2R4nfM3XM8/osMKGNebK1Vw3mqM0dZoUdAMlTbeI84+lRt8p5nK/UIg2wt5ly7YwYCLF3Fk9ljIGptFVtND4y2cIQauT22Tj+RBQEN26LVdydsWlcNpQQ4RuhRfIp6sw0cxZQmtk4HwbINkETsIAkEQALWseB5BTSvMBDRFY2ahSwFgyEStijgaADJ4fQy00bWTgx82Zh9UrFVRjtwZAGJPFgb2+n2wqFeXYQB5ACOAcgpNnWZMc+y6MTJqcY0Hh+7zVHA8gouG0BeUXapXu3jkxOcnp76LCyb4GGznmqtMrmlqlwZOLKBoxCKWP5Ew31gK9tbuFrMaDE7L6U6I5g2J780E1aFLmdH6twsRfqWDy7DjKJaTdUQeyTVjkFWavoh1RDJAbmwBMUtVGQ5PydoOHM6p4rdg6MDYj7ubI+BkLISTndpYJAcrzBQBNwpYNuxG4DFdxEaYOECBQF6JTVWDutZAy955Vpj2TQ32bJVgVF3KHQGuduF3S+BOHRuc6zS3cC66ISdtzbD5kf3ul2uQkPuInY8NcnUGqEyvop2UYNCUlW9qtEzJPeHtVAtPbRkYQVS3b7VPi0FqCtziGa3+EUL6YHmVefdFmWWSOwyi1YePRninU6gbsAo2wGIM8jCN1xPaQwq5sWUUBZTsVtbPF/+yhaA2NkeTWAtM1Q0xMrCxkqnhVjdV9tu1kNjzAmT+om+QmyocK/r3kDxZC9AeFXiG69cvYrbbr+M22+/LPbFY3HKGEBeuXoVly8LQJ7sJc7x9PQ0ls0z2bb8VQZP8HzXJnZH+0yqYk/pU9kGfANEnffcFIRslh2HxAlAWZ8Tz7j3LxAqVTVhVexK2M9t/yYDSANGF3wqnZNmooKZGmYApcp0wxkid5lBxso/52ORZ2eQJXU4A8kMhtmuQxa7lIXC1Bm7bCP1oE6KPCrgmfldQ1pgKrRWttl6jO2lwFZboMLA0MIMiNmnUlkPdEFHEhwN6bEtWm3xhFrSQgoU82pjmhQCHG0RCF1/EQqOwiIVLEeHVDe2UAolgSI+66yTNB2v9KquPy2NP7aog9mgMjuupaFVhsyeUTU9tX3eVqEthIWAUmoCWzKSKsDcjZUGltn2GG1OgG7WZUwxxauxAKEdCziS7b7bGd2tbY3hmt1paQvKIk4bwFbfUQbrAMwBliGSWpYCQtM4PkhjMPs7g0FSAkcKWyNCzBtUnYd5qnXDrHnB6f4Ux8cnCo7ipb6s3ukrV67g9stiZxTWeEVtksIor169ipOTU2Wdp+IQVLON1HnxKd1EFYXFw++MkUOt9vOW/hb4lGoLm2IdtRpLxIBMuZR6KZApfw6OhASQEmNprNRU+aoEx01URoM84Fc+ZnBjVahisQulUFREvqiiEKMV2QtclP27kkFmwDsIkPl8nQ8foF2qRcKzSil9ilw9rlqppcA9y0XtIlWDbks+VgYF1tk32pNYPXj2WkCnIJrdhKExbOkdbldSQ7WrsmkKWZRaC2kzPZRxLUsEfSuDVHoW4Gjqvj9MgIztmlJfO44FYu3vMECx5UNFWs/l0aGK2wygNjW/N1bRMcDXcdd3uRNmIMHg1t6iQhkJI8TqOpYrXyXJ2GP8VGVFB1Cwg6QDoYJkYxIbZAJmTjJpwJqnatLSsBQBSasLY7muyjcOJaYDRxPTEnlO+n8PkIMNWNuFTSagMY5qclka+xJl+0WcMicnJzg5CbVawC990rXjY7n35PhEpg+e7jHPe4+MEJmXdkExEwQpOMJZoTgzdZdxY4vG/pr9NZsze78ibkBr4kRrzafjioyx1Bm0D7ORGnlvJfL3hhpP7iuopgWZTDurD0205aZQuZuCEunq4wULi9lI4ic5N/GZ0jkYZDIser5N4rNtRaCdYVqK3azAQSFg/jgTSgpwrARMhTApY6xF6LSFB0zK7gLIqo8+AGVjlcxawSzq5BKqR1fjUKaqo1iAY3UwMRCOBRS8aCKQSte4NbAG+YqjYEFb5g4cgzEyZA/jGBwEIFssWe8TxxnogtU1fMShKKu/osaRZ1BKaKFLU63ihd9NcEYFDWuadFYLBUj6DoZNFpeXrT3NaA4HaCqxr7S8NoDbVVwfQ41CGgjL36ZLwKklQsN9KLFJAcoYmsjboTGD3OGxACggWlBoBjOjlAayKYCGqs2lwWfNWM6FoecIin4wtwkEOcg5Fp2IxD6tVFVqd8bIDJmrJ2FnvHJF1OYrl6+ox1pV6qvHbo+8evWqhP2cnOD0+AT7/V7X95zRlsUHRChQWARHARyQJhT9KFA5eyQHRgFH1hlKnJZcEy3I+hizRYxwWIUIzhptEaDKylIVEE2FrwNIEhNKWlnCBxomNPNVcKzKZ3PWQ+aMVxaxlVKEeqFvmmumszPIYvQlXwzmaJPKHSjtOBVQJ1joMzgWgUiZNqY4FcKukKz8oudHNWyDu6qLQ+hK07s6xZQlEHhpvh3pvJ+xJ8Iyzzp6w9fUs+U2LQyBAH/mVMVDPdXJ31uoKPPUhtGRuinKc1NQ1DjHZkHgbQG3WcrtwKjArNVoNjwBRcuQftTASYVhQeISeNwzSIaCytJk1gfgW7iSguNukqD2m246AreG033BvJ/AgHtfdw6SsIIq4LDKQUXhJp5+XX+yFpkaVquBj019JAVTUlHK7IB9pXZ3qFicY2OZ/WMsEsoaOT8vyaAySlevlwXmYZWyVTWL6EIiqROJCGZwDJOJDIoJjoMjREgKmbOqZ/OxQLHG5s3yOZ1lFtPJXhaRuHJ8FVeuXHXHy1/99W24/fbLEsZz2+3469tuF0fMyYkAqYLiqS5UkeNpucWOLFRNbRYwPCKx+e1KxRFJKM8RVRyhYqcgOTGjNkZZmjJP6JJ/zWwSQJuBJdnUXQewJmnIu01WYjFbMeQdzTREdMBs6jwVdu1OZKShsZo79B3MUJOZ5JfVdg7tx3Z3g6672g1vZ0vnZJDGEj2PHShCXfB528WMgbBDYzSuYkmJijIMYTmy2suuClvc1YKjWp05Hul6iwJi+iGb+4wuEHsPAi1NtsMkjd+yPNjMKA4nkKug6eM2Thi+m01Oj0GQ2R66/WkCSGORssWCNFMpERMIkEcy2F4zZIvlGXO03qnT9oKtBNi47DYB7Ea66LwKmtfrVHF0NAlAckOdCuZJFuS1mTnCMpWlkobI6Ei9LNohqgSHMzdUKEgqeGmlghZGKySdzG0I5kwyqTBGrSDH4TlfmNOycKqhsLvshk+o300BqehvmPeoRTp8KU0HU4030LnBQFK/yUAyx2kmlo9gwW4+KFZGN0x4QPvSZPWk/X6P/bzIPOrjUw/+Npuj2Rhvv/123H75snipb7+My5cvi1p9KnbHk5NTzHsJB1r2Mm3VPPcw2yOMncnWCRMV7PRzRAKWR0XOJ2WUFcEeqUG2djAykNcJ0AHf5N06ORFcvbVB32MfdVirJCJdG+KYgcoSalSIda93ViXPnt067HE/Q5OFKqg1VDYzALRH6uIWVM4NjsC5GKRDgwu2MYLOIePsEcPaa4REcv0MOmXLZMt3vCukACmLQRzVgkvO6CouqQd5V3UFnTpFQCuAZb9gLgUzFemkVdmcqsZshgsg1BHkTjGE0DgQkds+3HHh61+yqNPN2GsLBsmLq9b9nGbyDmqOOtvXJehKgKTt7JY/ZrC0PLUmNpemXle3DVJslLXbTTg6msBth1oI81R1s3dVhX1apT3e1F5jkAyG7p6IAiwMCei2hRfChOD2QtbZNjHMRB2oritzr83maOo14hkRh4BUSSp38HZopgZi0TYjsA5KrTFQKmz1dOvILsIwB0GKkqBwEJopyHJuWyKQtpeZCszUYas2zfPs3upTVY99HUd1zFy9KjGP9rl6RdTpqzr32myVp6ehUs/znKIlInxNVFux+U0ec1iwK3E8FQNPCnBkyIDWpH8Smo5hyX6+LKEtKUCKnFiFQrUysd+WotM/FUrMUWRAWQp7aE5pDCocE3SoeatkkHOAVLOKsUlXGTQW0zaEuWsBsoYNMpYsT8IJ7/oAkNZw05ypITzbAHIXAdRmocA4GdOpFbup4GiacJPZzeqES0dHuHR0ycHx0u4IJTHIZT/j9LRiX/diqN/v0eaCRgUzEKgBuDs0M0hxzNgnAsflp2pwh6hwSaHqAJLnOcU6zvCgd5thorFlvlKQMrY6qYC5ik3axznqLjsEYCpmLJ+2UIPFoHkoD6CL1E7qRLqEUoCjeZLNu3KdFFU/S9HQGANIG15Iwbig6GwXCSK3+zWfVFAWOBODrwyu20KYTSAFa4szRpaD8+1wJVOuVgvjs15o58GkJXTKwpLE/iz2x4Kik44LVdkRgACCLsiQQLIgx5dmsAwwdDkmdlkPG3DzWVIR22iM8RiXrxy75/q2y5dx5YoEfF++cgW33X47br/9Mq5evSqq9uUrurajAKTEqS5YFgNH633kzs1KRYFR4nh3peKoVBwV7UN1wlExlbtgggBCVaBy9lis/tlBEQ6Qi2/X4SvuUxHd2cbSxcKhdNJG09lgrOeJOVa1eVJrgDpW1kiBAEE7YUYNlgBi00t0+4UbUK+B/x9x79rguK0rARZIuXP3///SPScz022LwH4ACgBl96Q7ubmrxNN+ShQfhcKT37ZBXpFNXjyKORpvRJBJ7nyz33YSJYEzxwDJtznwdjvwdkz8cUz8P8cNt+OGt8MB8n/++AO344bbceDteHObXIzm43jgEMEdAnuceHxMrDGwBOGFQ6qetHekDVJqUfQg7PTKUs2AhHrth0UxAKrYGgBJD7ZA6z7NAabUahcOc7qKPSKQl71lg7yMcYbSPme9O1dHl7pqvWwBMjLjRODbQ9hNIbgBMBxTIuvCA8O7Kr1FKgriKu4gkcjtHkL+T2CwKLhbAz6n259VwoGjM0KJyHypqq8NJLlRWU3sLZIVaPUWO6O0GCdV7281jU31DF6qzGffDFphx8i5UOmdEb6FXYPw+Ma2BFLn80gJznvuRnlGibL7eeLjfnf1OBjjjx+eLvjr/QP//fEnfvxwFfvPnz/x539dxf716z0dNve7h/LcH3esc2WkRAaBBLudcMHubNFJxh/zwB9z4u244Y954DanvyZ4ykxV20ELzsqgOY5mdMgs2FqI8lBeNQSW2g0iTM8dLW5OmmSPUir2ANVr84cGow9NhsyReLGBilX/N9917JLo842+nInKEv/u8U0VG95gybaBzav8hHqOWNB5Px1Ht7AAlHrdVGyPQ3T7443Ohdj97u028XaLfOjjwNtxA4szwABRhR0Teo7IjS5bEkHuVZX09AeHGpWhPFchFizSpL+lOWkr1Y2OB7eGsDaidemBYFgZD0uDf30jBQzHgKqMNLtbsBbVMkmLFOBRlfQc3AmzA2MY1hpYOtq+3BZs1NvYiWXda6iRQKiz2VKIuA20slro7HRP9FDGsg2wTqVlncea+dVHOQkTBMkay+vfgDOmlwjyfGqKMUJr43XiM7vMg26+qLJxLWqAsonC38p4xMox7pShY+Z0h0o8PlrlbwfI9wrreX/HR7BKV6c98PvxeLin+vHAeniaaq/8VEVWKjSt1OjKWLnFmiKrdBV7jz8cEepTPsJApvRec7K1BywFB8kGwSr/5qOFEhEL27W402gff34H8R1+VMNQIAn4eWsmdS3ve8eXAdJG53v7xbsFb1OzrS3qNqHaaXJKlw0SqeL6QEqB5EF12zd1qodX1RlCgDTYDMbIoO9uZGdzrD+pwe3tygcnSltM+/q1BMgCRssKPVe7VO8HCgjJ0B1pgb3tEtJYHYVUW+SsnVgAqel8yFNE38phMJmQYZjqdRbX0gJYU4xlWVFH2e46UfaBtOt7OI65BJeMAsm1pZFHDUiaQ7xbRy4I7yrbAM6vWbnMDKcBmT1V7uic5yVR7d+2ZRArocjzUjq11wWaBZCW7TNY2k1bJk/s+/II22OC5P1e2yREzOP7u8c0Fjh+BGNs4Bieag8fq7nIyA0SDALjZHrsZLpsVKQKYJzbI8A1+yKALQZYzOqh7a+2qUz7p9VzZ4qVfrv9ZTB5A8pBFRkOkum+awDq06QQcEeffd2U2P57xzcCxaspr8AxmaPVd1Kl7kgTp8mOBEHRPVquYldozy3skG/HdJV7+t8/4vWNVampdsIvXEHlKEa4NSWkXizGfADV+RsghpxqC7aWYAPABpDIxcOh2s+VzLkD4xRIlArsnW+XIU4wQXmtFxSnKAbWNjs8s8g720uKuc1HJmBHhcXUtg9R4n9ZpBhaFNS4dFMCSOvVRjQIkOssBs7xKNsfYldHBoSHjTJW5hgcjrjGKLAiYFbM4cC+YVzTelp/Pzm5QBCsBIDnnQcbII8Yw+hfJ1G+yyB3pDzXSrZ4f9yzFNnPX7+2bBgGgP+kk+b9He+/fnnq4McHHvc7HvcI4zlXxjiSPbEGAYHuOI5wbDoIvoV29cbU2CjRR83rFlsUzzHDoVPZadJ6MP0lKCao7TUC+OgU2s4F2d73dMMKUk9maaj1GOdM0pJLKAD0Ao5WM8Rfpxnk73JHP77OIOfORDhpA2YK0Bty1xtWvWsNrCQ6RpBg5oMNHNNjIG8BmLcRajY3exIkK/QOtihpFdkSttzQq7ZLv1QX8k7aIBgykCpCGhB2RUSK2RgjHVCldpPF8TcdaAsYE0bCVsSQpg6SlOa+vUGwlMh1NcDTu0Bp6Qg0oKCFdKyFEwJogCyreUcfY7iNCiFUzGaxTy0G6ephgeRSha6oqWf8u9Fb78kI6Pb7jF0aKf5pwzoqb3tA8lxeT7KExxgXuZ8pZwS74CLCkKGLJM5Ie0SAFmsQ7tV3skTZYJEEbrg2AxArMybjU2MyV3ZM5VT745Fe6o/HHR/v7x7r+B6e6ih6+7MHf5NJftxTnT5DnXZWFaqr3xBG3DsjO+Zw89Mtoj1uc+Dt7Q1/3BwgvbiK/+V3juNSR5ShXQC4VwyQQVsYETCc9TvhYyeIfefp3EySQxNXOTx7xSsC7GaYS+Fe2gQA5mo6+4/1tZt/mmr2d42Ol+PrAHkxwvVl3/8WONLb3W6aEhxNnYHtqrVcpNBwNXvy0b7DuCoBgS2ua9Zynvmo1olZa61Uq6kK87dkg8JYqpBJvAkKAAaJW6UybUwzui6ZrBAYJdVqmRIVV1De1Oi/7HpzSclJIQCgzhwJkFPUX492iuEqjkU/9/jJHnyVOx6qYS7FzH26nR3pqrg+jXhLdqs2W0qqm6F+LXGw7ALDi1hYehpr7MgArTQCdmLaGzvrK3b4pKa00jsDVR9zSOSgb6FcfROtrlZngGpjrl0XcGFR+1SvKjzxuOP9ccf94yPUaW7P+pE2yATRO22N902dpk3YGaNUYDrIHkdGchyxu+ZbAz8v4nxLxpghca0QNKvl99oGOT9ad3rgtffJGPQw+1x3gKwY4ucq94ypreSB7fx+onI45XtoGh1qDm1Ms9ZeUmvrY7RB57fU7S8DpPbJimqAWcS2C3yC5/RuLWmA0vo71C3ZWWQDw6OBoxuT4RsKBUgOoFihVhCpFx/YwxAsQLLKnPV7aUzPysmitqDqA6pmESwfMYu5SDjI/RzW8LMsXADSCfNyArWtTyFugXE7GZtYNkY/dYAeFCtI2gkHWw+Aj+dON7wV0Z9jCMbhLI7YQg94bjF6LpzLWSDBksB4LoWFjVLNAGp+wdoY3O0ag0fjWwY2eqhNVdfZ1XROlwTFuFeMch+X97r1cQ7K3ue+uCOJTVjjc2IOLy5cDLLvOjgSCLLwMG1BseSY3rbMi06cbb/q++PhgMgyZMEef763vOqPd/z6+IVf726H/Lg7UD4eD1enY972XQBlTJ9fXCvizkqC3u12OFsMkHyLkny00x+3WzLOZI+zKldx6wtuE8G5Qds+WbmZAyTrHiAYpLPGKg+4VwtvESFStkgBMpqNIGlSwjfXaGOQsJ2EECxjZC6krYjLd49vMMjn10aWyLYj2/7ySLouzcUPD7EkMzwGA1s9D/smw6P9p0SAawdHT8AHTpgwwNjLdZ33h8c+Pk4Pal0rvQVcOi5VyEDpSaWzZUGXQAMUx4iYQvh+JGW/CulO0deZarSTnbfZGkfEPc4Iyp4FXEK9gwIlJoNZMTjaIKcApy7UbgfOnGxY/T5YIzATiI9jxG6CJcmLRUYh0nPhPH0fmwfrQwajfDxYTLcya3TVpF4RLkNngi6FDoEOwToXmDnk9RMthWNtPxosjgH6gnSQJGhCnl83EZz51gAEBD3BkIlj3jBkxpYSB+Y8PA2xb8kaKr3kvrUU/pK13xkEzlCeX/eP3Iv6x6+f+BX51e+/fnlmTNgX//zxI8N4PqKCz/tHpQ+e5yOysHxeHSN0pRBog0WaR+yFdIRaHeo002TfgkVS5f7jjQAZWxVTOEQR3aoQxb5CMGbDsBFzXcKEI7leJOZ3BqUfx1br8aCNs7FJ2caNayepRb6Vjk1DkJ0Cx6vWZqFlAQ2T+sm+eXydQV5UbOSFrzvINGbFpvG+pamWiOKWsGSQrPLByH73tBFA4y9os4jg4mAwqXoGQC5WNln+2LZQJYDFfSR54QB0T3QaxYs1DK7uuJfnbpe8+0pbG2FzbAySKolQ7UUWNi2PNWUgcg8Qlo93FVLzBkQA37VlhQPMC4maiu/tEYjZQZJbrXq+tKR9capiPibm4YV1x5w4z7JPQgbk9AK1BtZbrEnrk8YyPtNLTwVIrhISpv7cwt44B+2Kwehmz3MmY6x+7s/LWdPV8LTObgCZ7LGB5Faticyxhfk4INdcc4D0cv/n8vzqHsbz6923Z/34eM/q37/C1vgzGORHbJHwETGOZ08bJHMco8yeCFNUsLPjOCKr7AqQLEgyEzyPOXy7EJbvy/qVpdFIA6+NQYoH7w/zshVDPBzH+2Ikuc4ki1m1C6qwc7FTznnaIXMYzXr9mI11bdtiAG1dxu/4vcYY+2k6y/zq8bdtkPn+tSFWeJhjKhWETVY1QMMunTMNJKWcNtv2kBwEhFrNncparnAWOTjP2Nag0qHYwd6OklShNOSdEExZaECANCOMIe6hzrjQJDD1evunxEWYsjYbzGaLoW55+S0Ht6vZXRh6PqpiiWAg0gzhudg2RrJP4yXIZHOzL0YAMFTFMHRARDFOYA0DxoIMZ4wyvKAE7bsrigtQp05pjzKBmGgAtcIGbVUAzMO4yEyGBZgFm5tU9Ya/X5qMXTu71HQuugZsEuzZbZAzKjNxn2qyS991sEBxNBtkZZCZVczpsop3LNvjI4vXlp3x3TffChtkZsU8HBgfZ6QOrrI9UgMYTrVbMRWOm9sUySC9OtOtAaKHxd1iTyX/3RHaStkeqxoRhbPszD26mIw9XDMBjrGmpBjksx2yZX1toMh5T01uH1prAFPOz/Zhc9hYDApXTE90SJX73wRIfY2PZZa4qNaXPAwAzyp2uvpT8vRHlDwLNjnDZkGVeJi63Ysh0Rlr5yrfeZ7OIpczyXTYoExbcmkhuxNm6bAYsloKWWBCSvTIwUo72dYBza5W1xwMBA+gAhd/YqP0UzTB48OstqvYXjFHA2TV9woZFtsMjPxuAWpI8WAQuSf1bL1riIoq6ptfLQPmgTFrHxvfn3rBsDAWnV696ES0UQGvpxLZDVHE1PvB4wdhhiO2HkEDyGMetcf3aGxddgHRh5GAlk5ACc8rGMIznsDRXx/BHMNzTXANu2ef47pV6HHHzP0RmTL3u4Mg94v59cuB8dcv/Ajv9Xt4tN8/XL2+fzhQnhHKs1q5MmfSXDeMc9y3/3h7e3M7ZAvlKa822VyFAlHNnVENigAGOgkDIJOBRxqlgDFXhjlHbEJXQ5A2yFZcOtk4NSbeDEkBZ/yTd6ZN/o4ssShol43FsTFJaocEcGvf+w44Av9wV8MrhbWnD4M9kT2C6nGxyClWHmtQzZYGjC1GMn9XaEXGmh7WAMjH/cT58Go+mS9qFbnpKqUv5tpZrzeeKVYjDOUIlXYj7QVsqe1JzZa0qTgwDE6+xhqlnS34F+hbpiRUY627ArucD1JCah98qQfRGShGxTYkQNPp0Zxu8OrhEODgDiRigCjOE9DpZl3PcSZIMkebUh0BLC6cRgy+DmAOCzBUZ7vqN9LZ25xHa1/Fg7oRf2cEZReuMlsVRN32iqGKLQ0o4/26dvXZPse92C33X+6s0Z0xFdf449cv/Pjl2yH8iue/Ir6ROdg9S8bjHDXvy5sQ7R9VE4Cl/o5j4nbc8Mfbm6vYwSDfjiNVXIIV5/icl9xy9hGFdPbhPoX8ZWk4FkHeZNXU9q4ZSBUVQHMHr4G6zheObIqk8QqwKAgch1oVblZYhiZ1p81zRPHvj2+o2BcUR5s4jf1a/yA+zBhEIOK4GP8Y6jVYcj3yQKXHRyJDefz3sShMk9WpAetkQHPswsdE/lXeQDbM93CuoPbRFuA1suQJdtrg0p7YAa86RwBjOG3/XWeaBYJmZf+jqlDsrzJw9uHtUp5OjR7w3O1KNVkhFYrjYNaFA89rKeWd+TqTcW9+2MWGbpOfKnpt0RCk3fZkL09D8/hHHUzLLMBjC7Y9h8ggzbY+KBHU+6Oe++te5Lizw+qzsk+GYyYnQIBksI+l5pvV64q9mKNCz8k0wj2VsIfzMJTnfr/jft7xCJX6VG5PGpsCmIVc8/ZUXGFsdBXe6mN6yb832iCjShODv8fwJAoWWhmhtXThiD4fCX4h8Ut4d2LAr8rW9wXmyDm2nRfti+3N32GkSHZFbFk7vLQZJOygkqDYfQQmAiGpELR59Uqv/f3xdYC8eoAIiJ3NBHtwCszv8Ev+PJ0ycAfNZluUi70RnXVa6y2/UVtc4OVNdSfCwsptSxV7LFlMvCHZ7tEkbAcy4BnYKp6rbC0leXv/jAg9crC5MkagdUsyxRAAYfymOp1ACdQABwiUx3EPfh6T4SttpzjmKyc4eq0Bb58/gMZKrV0jKhCJWtwvmRbzKbrA3IEdChAi69vxXS1772Zjyr6/CiACVs3D5AcbMLKbdqBEY4fShcg1pCdbIhly4mMUaYSLXn6mETYHTYTrbCB5r9qP98cDd5YqWyu31c01JsxK8TYdLODcikXfbr4H+u2Isn8EyFC7mY+d8xVl1knwJ0NuXUbQs/asjVa+ZW0s0J7Ktngug/3isE++IlJzht8ZkDT1OSgCLJfYARJA7gXuH/0f2CA33vIKHBmjpO21Fl73zbIIelSb02sttVFXqdNRC84GvMR7C0RelR73OC1sY5EBcsYEjsK1xoR6iXQ7SkBh7CU9ez2AVuL7zakxpH3u3+U52FPMwGkmG3hcBLxykNRspFngXMslvQlskkFGNRvd+1qGgFVnsvr5cO/kERWPjsF4uIh3OxjSE7sVK3CeBsPCUGCtEFv00HvxKHCbA77n9k4kKO85kQVwWRVIE9lhUAwZ0DUA83g+HQLTAbMJZMyf5frN0MMUCBf7Y796e1+4kFGLLRdjA0eySN9Jz500eQ6E3wlu6a4KPWSNpweD332/6p+xCyHTCLn74Md7Zcswx/rj4wP3R0Ra6NpY8cgq+jGmUSA62WMAJPdpf3t7iwDwERXjj5q/KGa3scAGiPWsUmnLnCENMK+/qXMZcCmoUtBnv3lccbbaVtqAm8N8LOaM9Y/CkXRcTmSml5hlxbV/Yof8OkDqpYOSHSKN8WmU33qhVGMBYx8FE5Z7UPT9KLbcTKCKRLDmXLTBc3xX7CXscXlnsEXffJ2ZH5olxwCqLQKugzRSczuHBo61VWaB4QzAGQw2PrqNBcmELMqBqXLiuZrSmSaN3GsFmCKqb8dNarCjZQQFSfVvBMiP6TF9x4waf8ebZ05kEQ+PR/P74VgCKuyzIBNjYYh7SpwFWIBFxH4mQBMQt+SwC3ssD6fBEiQFAoVCxohKSMNXlE5ATwdJKARl06SWy6W28ZlmdjCjSma5qNTj46EKTK4WrmayyGDbMmuHSlCVj7mnAfTulOkq9R0f90dtshXbISRI/giADBX71/u72xwDYJeuDN0CkBlPM+yMGex9eDYM4xZv8/BwnRj7t2CPY1Tw91a5KrSuzrAs5l+tzZqX0h64qKW9rgDHohNFfuL8yDVGSNkBOzPt+Wxd/SptS7IYN//1Emb+u8HrUZ229roB4VX7/XcA8qWK3dhBB8cNJGsJbUCZakSp1nxvA8Z8lM2R6uhaK9XpMxhjqj/htKE3uktGdz5Lvr7uZ7xnufTHaPFjbgAfcybTpCmE24h6AmCAi3Bq1DThd1UEsrxNQm8LpTL8XtM/zAnDQgCxwLl3Dg31t1HFhSfNByOWhVmUNotBEjI2i7zjsFtOXouTmTn3PShD6l6szZWwpxEwWZvPnV6l8jhzXKiqfUw+bCBJoEQFnoNAzJcwdCeawmLjeF+kOVWFY09h05xV0hh02iAsY22VGTOxt0xVB4+wHqYUvt+f7I/3jw983B94PE6c58PnLc0LiOYgbI4BdEeMn9c/DSEY43pkKE+o23PfXC6DsIV908YnFxI2ZKOmkDnfqeZ2cmRPz9j1NT84X8pERBboNkKOQRd4lIJSJhCghdNJwiTnntblXRBascXxfw+Q1J0bI+7pMx0cG0D21L6ral3bPLbKHlSrgFZeydVTjYFDLHBugHSeC4/7wmMxd5jVqKNpq2RddyigscfcC5tZLfMKlm1P7MkyUrPlsDqQKGP91APWle/Bw2JStbECUUOEUXjeYJY8RFMNubipHmabuKnYcXP1+u0t2EYtrgEKaIPZGfUpzbOFcpcjj29kmt2YwDy6jbNLaO/LXQZa/deKdvi8iZrOJv5cBzAMphNG5mgLgigqTI959I5wBdYVkbU3t7XuTiOTgS2FcoQC4nUWkHuEgywybJAxloBEmBTlcttXpjHIjwdZZIFh35qVKYX3plYzzpFmHwaD0yGTY8oCE+GE+ePtjyxEURvWRfmy2y3nb6nWXHHW7iUlWAqv5GfC9RYgFJHp7PUNDbFDWwccEWYEWmS7WpiKJNd/xhRvDBLb2sznozvM2sIIMmXt+tS44ha3z+q+v3d8PcyHV8zX2Nlit0EqPMEjvh/rujJmrNsekamFu+0xfmcebuMDLEDUWVzLcD4eriaeJ+73hXvmC2sUOW7QvBn7W/hBTiwCg2Tw7MiQCI8X3B7x3rwdqc74wvXMFV2+GwrC+eHbMlf4DhCLNkKIFrKZrv7TozOknqd07ZkmE8ctFtJxi5i4t1xIt3mkquRe/dgGl06GFYBpBhnl2Jk34PYW/TEW5jxAS0c+rLJ7itkrEsASYSqCgIp2LRVBiktbAJZPnm2C1ZHFMrJ2JRcG2XUEuIsAUSSXQCHi3x1kwaT9DSRpU/WYP7/2Vtvx8XBAjJCeDOcJe+NPqtY/3Ab589cvV6vDQZOJC6lZ+RgL3Pbogu2GP948fOftdsP/3N7w//zxR2oENwrF1GQOMF6SJiQuUdXIftGorg6qn3H9UJnJpBHq9rDSdQigXPivYIbsMEoAhHrt3mYTFzgCgbJ/jTGtcK8zJz84Fn1c+NkeuJ9GACktxt9wrSzbeWnuv8IgoZeLWXtwhl7AUWIySqQlDUSAcFOlaX9kzTj3bldoTwJzFBhUC9viWm6DzI2LFs4HVewIlwkVl+wAQNkcGbza7I2VAlgOGgnbXToN+L1gmZTaCZAaNpvuKU71OzosgCU7KcAliW2oOvRSbAHl5n1WIEn76MSk6tUY5DGnn18VJorTykFzv7uaqCF0GBg+D8NhDlzz8L5LydyBMbDPgVMTFD1cpR7Fg31MOLH9GZliY5BWLJKmFU5BC/brkQurANLCZhoFcPcCz963PBWrqpVdt/U3wSVMBMUcV+wiGDUeeyUePn9/Du25f7DordseCY5ilc8vQDJHH0dPCbzF4+3NBd8tnG4MBC+tZoI2RGa/IO8PngZKQbG4oMn3cwpGvLJPQM3eu0YOPjNIQfSnUK1G1EZQB0kDBkYApUVlqThPGj67sJIEwqzBSeHVAbJHG0hFpRjKUr63+vvH17d97cDIv4TtWOf9eX/4fvc+Ifx5f7B8WYt9BNkjQRKBKwFAWXGHe6msFjLh4T5h6QxQA8RGqq4EQrmCI6Vv0v12/+29jM8blZ41guH5xuYOqiwa288DoJwX0JyZDpjxXYuJHu7bkTVP0CS55MQpj2zZUrtziSFOCHuoKsJEoV50Iir4yBiYh+8yZ5ieUgjFhIIOpkzlzKIZ10cL2UmDfjFIodACbYu+FeuzjYbMU5ttE7GlRTnfmCqvZm2MnJ1SaAHM2unNClXyMqUJB/RaK2MeI2bxHkHdrlrfQ3WOx/3ugHi/435/ZAB4L1/GDa48zbYWewprmnAOfxy3o8CSxW5jn/YM5Yl9yFPsJEDyjgS4xLqSHdakCkEt0TdhK9x+0F4kA+W5hOeUmgvSXgvnPVJ9304ZQirZIoVHMkif48YwrTCF8LuNj78Ew/9TgGxsu67c5jWsWGQCIsrm2Jnj5sVuzDIT38JOgqbG5aZYCY4FkqwwAyDCNiqsJg3yIi2BniDaiyLw5gR71xZt7oRjUA1BAzlcgTGmZEweBgW7/VFr4pBFJmMc7o09PG50DGyAwQWRgeJCRhtVaciIQsI6e9QAR8X9HrvjmYPVXO7xVUxAFtQmjmCEZMG9+ji3F2BVcrLkAspikCQ3rgJH/wWDHAGU8gSOTMUJdXo5o1tnK+irEUMqFqyatSQFvs2slbDqo2j9YckYAVRl9dBQWGXno9kamSHzHs/f398vQeGPAFQ/B2MdnTHWJJEhEAq0ORwYOzi+HTjeWtGJw8N/tv3RRdCnm4a5SyAhcLmQn6c11eiuPAsq5Of52NXs0MwLDkQaONMhadtnDOjOE5SKtsX11msJDWG0SIux/z5azuv8bxxfBsgBZKdKe15RxfzEtg1/JtzmyH13fWtJAiIyz5rgyNTCYpLpb47rFUMhiyBQ0ramoeYOMCC8DybV6GaTHHsf06bmudiA2cotAVRn5OIuz0BRAGNUAr96qTQHCO5oWO21YCV8ECQXVRenzulJFjXf8RAemjTh3miJ/OcVNRlXsDqCoA3LMAtmGKlaguN5Ojje77FNgPr+NXP5trOGEyIH1E4vIwd3fJAl+qJfmTvMjKUVgovOGQfHKIcVADXDbDGnV2gakZc9AiDFFmC+Mb0uXwgGll2rMK77uXCeAZAARBj/NyOsx3eJBCspjSso1kPNYEujwlBzxoSt0VMIf+Hnz5/4888/I9f6J378+IEfsWXre1QHr60SeuFbKycJcSGjBcL5dxRznDd6sgfG4Z8N5lUfY5vDPd1uW6PdzEvB24HyN7TKwgRg9gUbZHIJ297KbxIU47xX8Kp1WH6BDNqPfHEySFDo53uNcX7KTnr7v3d8GSBnu8KTus3DDRE5HmSNxRJ7tXB6rStzJm2QTaBc5ELO6KrdeGFlVizgmpZWKpgUQxulenTPnqffCRas9blA9cRafm9rIvwJGtLQ2+aFdpniuBJQucOhIuoqRsiNQtMDVwBpySBz46MQQmMxVlAyrEmnZQyoTv87RbEEVcCjefnPFhp1RiaH255iW1dRjLlg0nKfhiXQZwRBbCLF/Z8tBBbSQRNV32M8p3RwFPeWp4BiNo8CWCFgBhh03sH98Vj4eITGEE6jMVeYGA4ch8SMU8hFve6sMavyaDh0lgu5jTl+3L0az3uUK8vHT/z66c/ff73j4/3dQfHhdQB6CuU1v7pHRrhqPZNB8iHx8OdkmtgiHZ6JUp/7u1bSFpMzxKB+r88hCZId+HaDRJ3PfxIkob9JVZ/12tLsEs1pqvUVHJNBNlMSHQLJINvz8oCjdQzBs7X1G8fXGWTWerowSDTAbG2gHXHGRRIkO0BewTGeS6jhpYbt9o6STQTLAkeq4GQd/KHkQGADxg6O/gMarqkSDvdA+89xnmGHBDCXbydgY2QOd9/2VYPVqhIkNcDQMvdWAyCX5R0FQMIXhRGeVhQHEK/DiOUA2UGvBcpP0QxA12WRm05gcwbG5+cZADkk2yljQR4LHp7kDHIMt5mqMg6wzBvFIlmqK7JihMBQuesExwTJmvegnYZ7jDv7kIhccGB8PPzv/f7IiudqEjGpB+b0SkZjHL5whiUYirkAcEeTx8upeSiZkxvBWn0XQg/j+YjQnfefv/DrpxfC/Rle6/efv/AeWTKP+z2KpHi0gGlTdTcBXcCY2VvNSZMgSU2CzrqYfBZPn2skWFN32zpJainFDBsy5lPD9Unbz75/1qgqGSTfsPb7rn+Hrt/xNgOSyIaTRUrG5GIDSEeXDAFq4Lht4MZQJ2n6Z/3z5ePLAHnw/tq91+X4rySYdYC8SUsnxJ5aeEip2cfooBnnlF345ZGOjmKJVP/2z+O4MMZM1t9OaaHC1wSw4XYsVxsPX/CmsMNtZmqsBBNzIcHRF/jZVM/VgNIrwrjtT1WxYBXHJQDGcgYxBmZkgoyo9u3SHWF3m7iPMyrTDHyMh3sM1WCHYYo/X6fi/vHA/bHwcT9xv5/4eCg+7tHG5deV6cDlvG9iKXAsw00BOj1UFeusfZofsZfKSkfE8vAsjh+BcQhuM8phRQX14wCO6VtpzAG3Qdry2pHqDFnhLPh+V9w/TtwfJ97vJ97fT1e5NQByhAf/uHlIGGYAZdSs1Io7TtU62LUvcgbRn1lU4tfPn/j54wf+/PNP/PnjB/7zn//gP//5D3798rCe//74M4rgfuDnz5/4eHfP9cn0VuMcDvfUEMiUrMZzHN0hw+cHRqjU4xDIMXweDjeduKYRITXYQ8cIRJWhU2FYXKnbetoY4nU98F1rH32mXrf3Ut1uHzTD+cbmpICQj3EwbIkMcvdkVzqshJ8hyqhJmR3yvJsQ+L6e/Q2AHJ2hbwczI/19Bp9aOmNu8de3UABuEByoGMhug9w93C/AsasMcVBaYp8qT67+Z/ZYbdbmHc89bOAhOqpuIzJTjMhGYWDzoUd6syGOWmaWALmUcYcrc3k1iqwudebIPU1S8ov5xFgGmWFDvBmGTcygDsxHF5w45MDAiYGBt3FiYsAOb8uU6YB9Ku6Phfv9DBYWwfWPUr0hgqFubzWcMIyojuQ2THcsUAV1VXKth1eloa1tncBaEPh2EBUAHWr1BI4b922OmNIA5TGccRqWg9dyoPZcdcP9Y4W3+MT7x4lf7w74awHLgDlvOI433G4LhgMyzgBNTfssBQudTCKCc7maDbgt8oxYR08P/JV2xj///BP/+c9/8N///Ac/fzl7/PPHz9pP5t0zZtZ5ZiV6F/ThtWd87ZBytnSHTIRpjaPU6loEAYrxADzWkybGMPAFPhYYqe0kotaC5Hc2EOmLKoHOGgDa5YttcW3UsDHHIW5uYXGC/DlJkCQgzrZ5mgPkLCZ4KTaCLHRMRj7buYJZsjgH5N8GyH5LrR+uN5sPwZBrvnV4soX7yuygSMC6Pp5tLckZt2HvKnW1AqlWbLYOgqNgk7SeZbLSful2yQF6Q8/TWXKlv8V+NaTMsQI9XpMhNPT4rkxZoyq8LECSWSG8q+FgHmY/3zrBBJCF8xxgQNuQhXOemGPgHMPtZjLSSabim2uxiDDthtzWlSrqil2/lGXgwgZJtmrABpCMP+V+zYumBHUHi6c+cwFYqNHuNDmmFEAGmxyDWKBgkDadPSudS672Pu4P3D8e+Pi44/5wT/YywZysQyk4bn6vx7Gqb7dHpFoG2DAMZani8aiyZB/Ms/71C++/fuLXD2eUVLHff/7E+/3D1f22fzVU2zrwfYwqhtYzYNIhw8pLLTOL4Mh0dQuA9OAwd3xxJ8kNIONvETfd1Wx5ZlIkCfv6ttrmoavMuD5v5xHZCtkWphqYBtoBXOI3TzbILNM3QlXeQ3rcpthCpJp6PTqjTPtlMPh/FSCNuskTEW/ckqoEGWSB4gG52CKBqhIeWiVaH+QJGULzN444cS8KC2APtozBUv4XDDI9zrBgj+4SdK1cYVhwe9mBqgnJ84VndVGlDkZpVAfLJshA5KV+xgyHGO7JHmEnM6kMEJGVU17kxGO4ej0xcB8PH4/wmh4yIjwmPM+PE+djlf2RNszT0znHcBudyQLGhNqJI+x3I0IZzDSA4OF7/sTeP6penBjh/QfZUwDkHBKbRVUh16xozoB8hAmDfWGCdRrWuSrU5h75z+8fuD+Ydy+YyRQFx+2B2+10++qy5iwhe0Qs2BXquAVArg0Y39/f3eYYqvaPHz/w88cP/PwZNshfvzwQPMD7XOWcESAYpE/oDC2bo5jjUdXByxZJu6ObeGhz1HBiiUUdTshmDkx6HOsURoBEfomFSFz1b1Bm9Xwr+mOv7GqNUcr+0cYcBRFeRqpxWcckRS2Wd9tALBhkfXnk3w6s9HCnmi5uj0a3aaYt8ntY8j0G2cCxqHjZHSmJqFQIdidNAWTER2J/AEjm8/lRnLVLlHqNGDBOzMvnqbJ0WVnOHTPPmugBz2oLYwyoMXJ/wXTGYl7JDAiQPUyIWSq2rBhkFvZdsZ9yvLaWWKfuqFE1DEyoKHLbl2A7vggH7nKCNq7bGBAF9FDYsbBkRuaR4Xws3MO5kqr1aQEwftllsZ2FKCAnVCfWMhzavZ0Lerp67V593//HwdHVa19vAY7Ty1R5mqbguNWuesx/Z7iVhwXAxwKC84Rva/BYzhzvH7h/PPD+/sD7rw/cH741rZr4BmMKmA3c3tyUcByst+h9LOoAcK6FYREpgB4ydm5xjT/+/BM//vwTf/73v/jvf/+LP//zH/z3//1P2B1/4eev9zQvuMlEczUwzIzzrjtfPMZxxrYXtEFWkHh30AAuwDP5QmhekbQ4ddZYhLGBZVs9XAohYZMhfr7sLuwx5qCA7Wlt6CendydB07Z2si3ch7sXd/Z797hHqtg+M8bTmi4CVCDrIUIBlh1M/1WAjOMa5pPFcdE6Dc58GLbjTAitUg/CjQgwltLPI5fBljrx745OnzuK9xkh1/fiElZzKncObPGL/RKqC+plKbGWBAl11lIAiQTcDKBm9oe1TBRmozCGMfrUGaSUyqQKWZ7XKuK2S1kOiOtcWGNhjeFq75weKwkPsvdiGRY7PdLrTE87jflIgBQgnSIecK9582SQMM2c7hICPedaQ6UpFlWxj25zPLbamjXnizk66CX71rWx1RVFHzJ7SgWQhZFe/dUC2vtD0ynD/GRXtx0cz/Ns7PHXFgz+0eIcPz7c5vi439u1tLEiKYAjALRQHu4Zs9cgnQkSrPbtS6C7IzWMEDE/+vqLiczX1yWT1ri+Vsxy7VjSvgtYvgDY58NqAJ8+CrDsbDdb5P2zg2TYEwfjmdp3Xy3gF4Sp113or//qLq7Ht8N8nsCxP4+OYKmyYRJZJk1itUcOrDZcTI9bdei2DWQef3GTrR+zkOcFOPtlwAlo9dfjKdUT61kRJSqTMH7OvdyC3LYgJmlmnLwESJQjSIEMZL8K6ugjU4t9ri0Djt1YX5tHraVYtEdGzUixWFwMEk+gqUIPFOrtsml52GJMiaAEyAAfrwq0pxb2aSyCZNdZNi5U6lKt/du06WZ38F71AsarmUHYDhvVN0pbZG9/PYTt5fwg+C4vRdYB8KMB4j0DwO8RzvNIh0x6jSMzi4Ih7WiDld5Ze7RvoFU1ATZTUAxi5i0jQpXaMipy0uaQ7euSJxOek8wxB72D2+c88jpHt/P1N61/ob2+tAcoYYIo39f3BKoiFV1H3fVVAWse7HiwFcDYyNP3WOS3VWzgApIWaUpdegENJEneXGXsbjeDeyopEU0IBhKT3UMbHCRf0sIXj8sAt48IlCSqeQ/bZ2UI766grHVnFmDp13JVkLF2cXXd2WNWn+FCNcvFbxQqrfVXryLle4KvemC6iqSHfIlgATiHZylBFaIOomaARe51Z491kat0jYnISyo9p4CgilJkALSV1MuUQkEDRaRtzb3W+2ZSBEezuk+yWrK+tCGmN/q6Cq/zoDCb32dIj2FlnxosWOkD5/nA43GPmEePbfQMGX/+HqXLHh8fOCPESaPYB2LcHBidBR3zSM+1zNG805E1M6XqOJJF5YNQwP7wOWcyQnDLDoJci/lanljkaF2VDpjsK8vA8ZcQeX3TLuf5C1zFy+/V/W4bfjV7Yh/TNH2abFvwSnvetXiLN0Va3vdXhEA7vpFJ029rv0lOaInnXtGnCk0IUBV/OFmHYQ2kXW0NL8qwBBE4zirWztA6nm0c5ckG2aghKlHRLo/EafQUcgtjeJ0+GSzQqqf9RgJpgUf3Ypty35G43sUWIwimxfM02xUN8ryjBAqJYPTTwfEB4IhJa3PC5sIMNyirm/uueZECKMAY7m9WOsOEIRNjM3DXYtzBm08k2iwm4ZRhxaOqsXm8qLHp9RdHno0TvNImgxVSs+AiGhNzKqYOuPd9tGpGVbaN+/BkKTaVVhVIobYijvMjPOQe2uOA+As//vsnfvz5A79i64R7gKM+HrAV3mprbDltiO54GTP22Z4COWbGQWbu9XgulpImPK4Vc0deTkgpwBD4GurkhLa/4livxMd1EOnfkWblShTFJ7AZ14ul94I5csps5ke7tobhOwMj9iXnVr2FfvWnY5w2VPSNuiKmNTav8er1WnO43dNXjq8DpI1sGReLhdQCKW7rhbSDUJVhHJdF5ZX4zANJ/LdDAJ2SToweu2bWfeUckQKOsjHGSEmNQwFjA0BUmA65okrVCKxagXGt5mIvewbbAGziMZmm7Zk1vB+QAUX7haZYudxHB8n2EfudKj6zdsS3aV0QVyNVfVuD6AzVsrEC8IUZKYvD+v2VJ7VK93dnXJtgsajTBiuRDdUWfAeBrMiehYYJYDF9aXJIkGToCi/Hc03MaTjMIOGYmccN8zgw57HZ89LGphale1cIr4W1HlG67N3Dez7eEyCTSX6QOd6xHmfWdHTtqULUxuw2xeMFQDJ0BcUsRzGoy8LyuWlwjWEEc9SaCCMmt3FOcGlYropMTw0YiqkVM9Y4/xuiia/lIhAdJrn6G1rFtNkYZc6Np9u5kIL4V/bHSNvhSHDNI+Y+TyU0hVl491WKdYmbUzaPd179a8e3crFTQGT/8ElE85MhaiE61CKGK6BoWLEn82DyZQJMn2hrRX72QKWDcQFJdW0OdwcSjlBHE6DUZ0GqzmSFgM+5/E7NonCU7IPXAaxiKWtCdttderBpRwvPbCOPcQtlTO6gyPs1sgo2LSaEGQCCr3iK3BLghERlBgJkgVBeO647xwjGUNfmZla1lUSpfbUS4mFua7NoKwGDlWacGb2ouTkKIBMcGzWofqy/LkwGJILOj8PnnTu+J0Zk0RwdJMMeaxTMwfA9JtTV6o9wyNzvDpTvsZc1AdKZ4z32rt5rOg4KuIzr9GtP7i54HKliZ+B3C/6lUOkuCFBLoKnBPILBVNzUEWOhQBS2bSKLAMT+avrW2K5BYIu1SyES/9I1Qn6zRzfK/vQFKOa5rp83MdvJBsfW91oa8ZoRxw39UQTD2EAN15VQiBsiv3O/xr8JkJWVQrGAfBhQNrUIozCiGwcbHlPou4Sqg2bEO/sG9K4ycP+UNQRrDegEFhencKCIYvU3jbsAus5jbeApFRdKblpITWWlFwFsxuLXQEyBG98nDelN9dzSmarj08GwyrHgABnfC6B1RoTMPWUNSCFwSZtopJqIaWZwAbSWR2bawCOZ5YCtgZX9IoDMlADOeNysUX0KVF4rUKAYHxGwzSezDZ/AqgEABoioh3K1rSuSVR1jC4z23SWDsShHKed82CArtE+E+0IP2DFceB4eA+kq9h84bm+43f7A7XZLkBQZYXN108JaHuv4ePhWCL9+/cTPnz9wz4IUPxwU73d8/PTgcKrXuk6P8wzhnnsVjYF5u+Ht7RbbX/jeQPM4AhidOVqEcWh0udFIzyEGbfoKppUKBpYt2GghKwj5nZAg7b9uhJKn1VJkNQgFKjebwFNVKzn/SrJu6nY1Hz3ipMm6FMDcjbBaJgmGLsg4NyO9MCKsuU4p5BjxYTAgKjARxAcFCqYLsMzX5hzqIuKvj28EiktKeCELit4rRohkkBIznNutMvrfxDBFoWIYUQ+NebdiHi/nqtnAbQIrdt3TIZhWA5jRk1XAMRd1Wm4TVEHemDWu8z463jfWmMAbQbnpYQxWwmKnchlEANk39PAqCzl0x0KyK2TJqzEr91RGk4VS7Ue0mWE87G+N18sUossBkp5TMJ/VMMbh9yWezZIFSJORj5xIHRy9T4samHqlH1BNjhhRQeRVt/CVfFw8tiOyJLTFWBZrjOuUdABA77elLdcXhACYkPHmDPL2Fuot4whpA/Ye9C066KmOVMKff+Lj3e2Ov37+xD1U7o/3X/j49Su91nqeoGp9RHwSq3rfjsP3A7r5wwFyghXhbTgDtABIrwNqKQgFaIWnxYUfophGBPBTqGrO+cuCF9vm5BZrHHbmZJdkkJHh4peNxGGLOSeldWTRisTHmg/VikYWpI8ff/IMULVfedggCZDRln69nVlz4XlhlNQSbESM64BoAaSAQPlvMMhtsnqPie3rphnY8kbIIhFFUL38ku9Yl2p57J43RHCeijkG1ujhGpRADXw2GRmMDDWBSu2VYJI1yOmVplQkUxOg71OyUfQekd9sGmwBgByknDdmTw+fEGQKZJCefsfiFLwWT8Vdk7scyiNBklsESEwS3y6WoT50nJix0nYQUuasbkbxBo59hubkcmZuQwAd4f0sRkCvdd9EaletgzGkaoVsT2coltIrFq0A3HtlTo+skGQmUcnnqB3+eJ+Mc2RVIAfIRxa/fX9/T6dMAuTdnTH3j4+sCK7rdKaF2KUv7oMbuB2H719NR9Etik7Q1qgDtRGFROaU0C7a8kw42MilluQkTTRN+NeocDVYKhtesLpikfN7UvM2NSf2Ffz7yg+lXagbBEkqk55xqkg9l1qzZKS9tTnf8GyL3OxKbfInk8y56fMwea8ZFAGSNLkBKE3v68e34iC9Pca+SSBE0F6YS2qw4AP1JHiYAswr4BgcIBXVefRU+8JSzOEpZEcELHshA9rRLnQ96LlLHq1qHx3ouKgBKNogx3ONz31mSDIPSdUzVOAGEhT7KchSzCIdDblnS86SGiw6MJiTnClWkSbFtrohuopZ9E2XvM9DCID1uGmwHjhH5AD7TmDgvi0j0v8wmu2nM0n2Di8qgjJSCGyMdBJRd/SFZ41BFnOcozlmmi0ScGOz9GyDbV5Uv0osdPcXCg4ZGMZyvBMibvObLLIa7V+LxYsRudaPto/Me+RV/8LHO2s8/sT5uGelovW4ezjPajnWCdYjwfF2HL5H9Y3b7x7OIGNerYGoB6pZIFkzfiLRpZgkBXiwOQlBrwhh1ydfCJo9MCbAEWSQVGg74+Oc4rk9dpnn1pjYAql1wzley6eOZI1BNrhOIBtIWlu3BZQooZn817J/kiQY6xnw9N53TtYcEMWiUIjo/w1ActBSYlnrIAMkbG7MAbbVwDJ3d2KmxUopZxqxkNPDUeZk6p7ibXkaHHN5eWheuzrfJwiDdDUcG2WY7YH8CTxN8nWnDUHSHR1R7WQUu+pHGrGburvHOKb4rL6M+UAb3XEM3FjmalY5J4LhqeppiFYB29spAzAdKAFb5nttDy44g0fDeEUiyYlXt7V55rdx74KxpL5gZP8gsu4dwBRTEJtL+X0x7GY2YMxdBK1P3LagyJg2uxWigAfb4du9ufAegBzJTIEI5LcVc2dleuf9fvf4xgBEVuvpKvZ6RJ754wE9H2UQjbull34O3uMRKrb/PW433A4HyEgyxxJnZ8sGxLzYSNQICaCUhMpNVUjsJJMvFMrYy+q9tjOoZEUt2iIJlLLZC32UNcaCYWgjmGNfL40ebeDYSW9rXhLKS/rAPpfyUfZxXOcEBYpZiycmsXRQTDNz0/iyQf3vN47v7UmTNBtZ6y7BMS3rDOyNx2JgcQdJBQupWq4zayq2Yk0PbD6msx0Z6uwHDH3YQcIdEWEvhDsQXlJ12cZ1UwW8PbFACY4XcMv7twhDEXgMoQGMret90y9DQcmFNVvhBgfIqCwdqKXw3GhEavRST+Fzr130PfrisHYvkg2lxxVtDFidyEIN4VasEpPtmRlwebSJHeeQDSA9AsEDwmdU64m0QoKjlNrnxTdsU/k4GKliExybd3OMKCBiw+1x4XyiUMFSqJ0x11aEjnlQ/cfH/Wk/Gd9Hxlnl4/FwYFwnNLzWrFKTjpm41150gsLgoModdlhGaItEiHoLtK8R9EVmWw9c/0oSFWlscJhARtka++6g9VwSNCV7OgCwDTezf32aWZVUI4vltE5wqvbt9vz6vGKRryp2zS0CYzePbSFFwGaXNsT6C10qK/pLnE+LOe7X+d7xDYBkqAXSDkOG1JlSj/0jSLLCdG3itLK9Zr5nDTBwile3nlMxowrLWhqM0rCEdLsM+RYsMm0YLR7vyW7IjpYmkwiMiBftu9swxvtlZo1QHmvfy/tnn6DOE+eszeFHA8kZhRzmBpALhmHmNCMk+AlpwIGtnVLNDAEk9bruPh8ETq/44lOSdtlkMa0PKtiWdl8HVJHwhSf4o4AxmOOcpVp3L/l+hf152kr7Vcl+pDygVBrL3K3u9V3eBxRmzN3++Pjw8J2Pj/BWf+B+/4jtErzOpa6Kd2RYlYD3R/PIaGAYjzCXHMMfWaxCJFDGmWJmjzXQSDUa1zHravH+36DAQAgnmm7Q3ruCY7J2CiJrmWoWXFZyTtNO3DXImt+2zUTJd1q2CwiOKcYTFDs49jvk+F9Mnpy5YAA9AZLZRVvIUl/z+HvH1wEyYi2y2jY5ddgZs3jBWrD464Vaa+OqZJLsrmAOOsOxAMHjoRhDMcbC/bEwxwRie4F+rObA6dLHFysSLbJ0ewNKSjgkreMPnIm6Bho5u1KTk84Cg4XG5Xu+UKpXjjK99wWOM7zDnn88NnvV7e3AH/F8RF1AAFknkt7uc8H7LnK/OTV94fI6bfuK+F1fo364LuJ2OXH/2WCohzPCgNeCJiFAEShnFUYaoc4NxRDDMZHsuFTs0Rw3Zfq4alMFnkAy2j6+8Z8ZnTzDHTWxOHyeCZbdAxiRBSp8m4gzANJDej4+3qMSOIHyjvN8RHUijUDjYrxZZCPsqrlPdajXb7Hz4AyQTIdbTErumz4hUES2mLhmoBXDleNEyKho1gLBGfPKyweOqNA/Yq8nr659bAySDLT3o2N2aKaZgUKNzgnABV6s/tAmaGiWKUieTyFZpi/vKNegoOs//bGlB6JieFPFtnB2aT13oNwhu1js34PILwNk9kZcyJaCOcfFFB0Uq7p0AWWpFRp2MKcpQzwGUpfBFFm2aMjAYyzMsaJzfTFLUBtOftfYvVOHTICB6EPC9hMhBJvjJliktHvbRPZAbuyulH2SckGjL5YV2/JmsY6kEbkzK8DA9DtnHbfbgT/ebjhuE29vB/744w3H7fDSX9MdKSvUwsd5QjAS7B5mWJSuxiQfiRRNX7yVxVJhQxJI7tED3nm6zugzjayWCd+W1SevO79cAHgXMvDbWkA4WZU7046JrNhzzFA9aYOUSqcTZDeBQb5t6cL3zC5g9IUTwKih4nMQLSoWRRbWuQyPs/bheTzOrHx+DxX748NB8f3nz8ikeeC8P7AeJ2ArGzeBBMXbMfJeXAAcuR2r713di+AWg+QinWHCMQ3nkvhrpTCSprZGJzko7mOcu4Fi4DZGvK6/VK27ui2GsNGi9Gpx4SihDZEQQBBbAQv5Ald+rpnMj4/2rlh2zWIQXy1AdEdpwX18sx65Dnf2Z/15Cj4WmrZWdLoiRtwJ3NTw1vyvHl8GSFaz8ReXHQWb3ZEgqSsqaEfRWO/MKCFG0i3uTDAT6PQJQBvkYyychxd1FVmxqFaqeYZWjQbIhTxgqGyaMHAOBkqT8lCa9bGoiUFS6exIQnUu1bryP1EA2Qwku3o9XN0VRG5ygcbtiEV1O/D2FnuSREks16wV5/K7ZYiQmUJDVAd5DgZJ+58vkLKTVXUU2sLcqeOV0yEWwmBGOBAzY7zPnN0QYCXljl8nrjsFxxCMYZjDPAQn957pxSlads42u6SPRr6VTjehWhhKosXYUmiFCp1McSk+7meAooPj/f7IcB3fyvUDj8cdD9od7/cU7KwDKilnaQ4RHNNB8AizAQFyzmLKVK05Jswy6kBD0FJh1OIolrN3Q/V7zP0nBpkPH4eJEkS5lYk1LYBaqEgDvpga2FVqyXe4YjiFrBGG0mTSNk4BLsWD05wgTOCIcbyo2x04O6ilit2uyz2dTla1YnEY62Dp0QN5qm+A5DcYpOWf9FY/gSMfzHNd+bqqsdR+LxWbJ7m16ZyKOZZvIbAUj3OB3lE6ASRGz+exBEj5RCPFjwjsGgDpA1DLsTnzkkkmjgaT6X1g4T1L84IhDfjC1UoBACG9q9jAVk06wfFW6rbH8PlVT5XwFQ2ojhjsgbXianG50RaQq36lYlX4TjBnBDjaAGw5KNLcMaaDOUaERIZyFwHGGSoi4kAoA3MYxvTNuMYwzGnNBilpk6OTpjyL2U0ND7rNVFKwCAVYsK1cRNXV6eFfa8WOh1553EN6fHsGB0ivSn5vrx8fH6FWrwjlaT7X6MO0FTdGzPCe45g4RtkgKQh6qmYtbgeKshPC+zvswE0dabgh4Ylu/Q8HQmoME6M2v5OR9schBcY1p6u/DeWh7nNetnUQiGqxYDbCUFoAD9XKt+BKqCjPWlHOJjsg9naRSl9igIMRZqWsBoxn7O+uCZSsfxBhcrmO8UR/YZAAAIZaSURBVOXjWyp2Z4xm5h7qyAVjKSluJVBbgWpmkVD9tCwPFewnsiMAgYyVtsTjwzfG6nFUDBXpex3DOHmccagI3Y2RwRB/5dVg9HFx6YpRqnV2aEoieD5vhDKpWnk4ObHj0qDUH4J5dI/nwB+3G/54u+Ht7cDb2w3/83ZrKnbc61o4YzuA8j4LdAqWSDLo9KyOWCBzxv7TPTSJsaPsN2alRzCZDMjyfaVN3MRg1ISyj8sz6tcBjumgeDtmsMcAyMPbQ8Agq+oLj12bpN8HoVtC2nu1wJI5giZwbgl74v7w7Rje3z/w89dHssVf7+++b8x5Vnzj8jqQjHHMWF0wHAZPjD+FWQLl8NzvyFri8xGhPRlyhLDNSTlKhgxM7sIZNurux072TKbO0CLxLJ5DYvMzhCCS2i20A3CaNEhQm3Aiuy0FOtorEQyf48IvF9PvDI1tJ4g7oLnpwKePayXYHin9Xq7LnTGW7XFRWzCv0n+qeqX8tXLLC9ckCnc0Joxd0fwvjq+r2Bl0FBciMpNBxh7Ann+8kkVuhU61QDKJtwDDRgzECjuVYsjC41iYk4AZsm0ahkrYdti6kH1NRWRqF6UUbTwpvayGur+fQyX7gBlXZOxvgnBKibqnWeDmAtoJDYJx0F4noYKNDAfhQksG+TabF9v3px4DkOU22jXdMbOUKjCSRG2LJxkMt8DkwsuYfbiDhlFZJcWHzPBoTo+vPZqDatDnWmoeHUIOlARLhJOmqfzJHovFsE3IZ4Janp3jtO9TTsSqoR2Ke5Cf60yQ/Hh/x/uv98yU+fnzF+7hoT7PB9bJPbxXGLOTt/iDAmdQqB2N9d/8OZ1QUTmoe+vLKRhzIaCPgVFThrMa4TsBjRc5kOAoxSBrD3lnjnOQSYaTpjHHSA8oJpindzjzsB7v/6wHk9PfEuB7iFcViiAk0G9d48SUgsGbKEr+rM0JVen6PQ9W/7o+VqjV51I8Ynvl++MRFea5MV1osKlu+8m/A5HfU7FTzbZkVPysh/xcH9fS97l3dfSXS24f/LUUa7BsvnfAGIoZ71P9A5BJ6jQsZ1J6DIC21xIMCm1A63C1IivSNLUhnyeLdGSRcFDJinAZIOI1w2PZQo9y8Wwl9udun8uHM056ZqfVgigPb1NMpKlHOfd29uGrlJQBbVwIkPGdkBQykNvdDgTztir7LsnYkYxm0i45IgB+hK0s3hutLYRCQbEajkPSBr4G8r0y80YEg9IxE2rV6YzwfDxyg6/Muf5wm6MzyDOyaxgXGhvPPTG2Vvm7qdgZ65gOmwJICippICDi3lzXNJoKGwyvgLPdNgESyPNlVowIRrMzEixLrWaIT4FUF/fMNuGlrF/WGqCKz/ntm2SLFxW7g3sfUsM2MZ8f/JG08zaE3FTqIGTcLrlX0ycgnue5PV/cNK/h0XeO73mx6w725/myOxMAKJIxsgp1vY5lKXAVOHKEJwPFl0ageAPM4eoPCyMwBoxgQGGVcS2D1F7aunPbJQZS3RROJhbDSHC0jTlnhaKl/jg1NoGycA4xtq2BOCX9FvPYsktY0KEtxjGBFTZHDTtkkOICxo4aQojbWW9nZnzNGLJk/wZkuXoxMOleBmA6oXAHGauY1yUYtN2cNgGGRwPFMeiBRVOTfW40c+02jwooy4xC9sgtK1SBtZpjJjzU5+NMcHzcfZsExjne6Yg5z9SA6FO1UUHo8wUwshrRER5rPuZR3vkZzDGzebjoyf4QaYLBx0aOZJg+2u0nbgg29rg9EFukbmA5Nltx2h7Ntv7POS5I80aR1w6ZfS7trxIk+elV2MWCLBv4bvLZQVIu5wrbYyNWaVvsNsf+OCNSYa38279f2PR1kPw6QGavXPrr+veT36VkMKThFAiAjJxeEXF6fEo6ac61MM4KzuWQjTF9kIUSr8XXDcT5nPQsti+KsZp4x5eDptQLZ5qIKs2RIbNiRZ4rH5YAGZVEAGAOdxIx2Pqy2PYMi88yTRoJRreDVhzpFoCPqGCC4XauUN1Ew/YKy763br9ZlMYl/rl/jYlBZgDudAEwox8FntlkM4DVwj0mFYJytZfR7PA0TRpLz7kVzNAiUd814Pi7DGt5DOxDgcepbnc6FfdHVei5x+ODwBjhPI/cQybQOWlSsEWRZIa3GBv3Ut/CBnmUup0MsupaulxmfGYcjakR1EK59djYiJelC4FzEswuit8XKyx7ewJi++u3JFsbOK93rvg9JtWHzC7PX54pNZ4AScaE5qOZIGQ/F73iaobTPBqGDphzua3xEWB4Pz1S4ePxwMfj4e8TLB9hj2R8MonON+73+yq2ldopRV5yEMre0QZmkxJd/aa66HTLkb5SwqhmazBInbF1ZyzkVGHQbG5DnP0M92h7mlSk2kV5KQ+dbA0HAUk8HAJIVU4MAJ1Rpz4DpLkN0uIcJoMENydIZs7MvRZihYJwHRmyAEaLG007SoRQLdp3g916TnMwIqH3NxaiFEBqU0lXODbC/OZCIUpxWQxwlt6KmEfvx4GBCZ2WJdVMEWo3A1YieyNZTZkBuv2Kc8n/+tyKTNRUn7PNC7GPt+Xj8Vi4r4XHqbjfH8EW7/k4I6xnRVaMR1MwDheQYF5HE1bHmLjdpgeAz9GKT1C1PlpF9LlXJuoPLpuuylqLK8SIohMUUM8AWXOoWCRZbuUul0oMfvZCl7gsZrYohmFnsBXUbU1Ey4aM1p6nDE/SXOywCi/vffRkh0Sp0mj7ors9MUAyQJHxrI8Xj/vjdFt0fCdDfzJms5kGv3B8X8XuvbNhTMmpBMqk1ylItz61XNQK6CiD+/a8aHVudiUXWwLV2Ox8TkxnCVmHMXQ6DyK1/Ly3nnDtBIMAaaFWL+B0hxTVbMY/CnxxMyMFQNmEhjQb1W92sUNMWUPLPLKnECptrzPtM6p5rOUbUtnwkKDMeTek8NHV1ZU+nBeAjEUhw3AKwcw50DoAndhCrRBMthIAy87mw9FpZAdK2/6SQXqkgMaWtYZ1amgVGqBIgFy4389Qqyt8x50xkbRAcORyp/o/RsYwHtOB8e1Wz2/NQbNtFZGOmE/AMYFRkrCVViAZKu1StRZTdZHlmtlCbrrKGq+LKNRq3Ca2ZW9fjn7tlkZL4LQOolszazm9OCW2to7sa0kCU+uUjxx+8/3hAaQzZmlFxmy2xrOY5GM18Ex1ezWA1Cacv46Qf8MGCcBkK+JAWxSHiRPIC6F6VkblSBQDtQZcKoqlvkufDsWay43ux8SaYWiNfVd0XNQWiYDZSRXbG7MuTKgYmVfgpkQBKKF9pGi3GQCyJluq2A6UziBXgKmHRCzx+L/sFylgpB0r1epmzOd1u6NLjRPjzJCptVY6IlYMvgWD9EiAAUYITB1erZoSOtTr1ZwaJxkkwRCSEQQqM1IQFTomzBQ4FkwnBAeO03AOwxwTttxG6Xp3zYlhkv0qsboknEJqZKQBvFkBCh4RsRwQV+zGeJ6KRwCiq9QL749VKlbEOr5/PPAR1cJ9W9ZHhfCAIVgxZ4LVv91ueLvdcMxIF7zVOHn64Mz0Qd+QK0ByBjiOV2p1A6hAjHTQxFdGQ5swhsTy6muryAUzpnK9dWBMVtYXrezgaPUhmVQBE4r7dA4Uz90S0235BQfbbbON1J6kgJH9lkCZ4O7n8ar4mumOHgC+Uq2+J0t84H6e+Hjc9/cigqHbIjcbZKwDpgF/5fiWDVKMnZyku8kr2STriCBiC1thSodVHQ8EUwK8oKgCSyVU7FGMacX2CzpCEpClcVEz1GGkhKLcMwb4hB1RbUHPhYXdWaRxQ2JSOauw2AskZshawCPCQoJBmkWXDKl2BeBQveghIG5v5K57owz15kVdWTXF92he8XiUE2K5E+JcjUFGAQyVAZt+fW3jwEVAUFwaALxq+1dnzAMqGkZ/D9ua4l5a0wWsBT0mYAu3qTiH4RiKdQKm4fqO80StjE4Sau5EdSIJddoZur9vfYuK5Z7p86E4H4rH/YH7Y+EejPHX3VWqx7nwcX84OEYc5D2CvxdzquFmAgwf22NSbfZ41D/e3rIiuMc5RsGJOfF2lIe624z3/HAuhN8rt7TVukmjqd8oIOvPY5Uk5hYZGe26Wy83tbjOTf0IYKosQWl/2OVvRoZluxqb/OT+AJRqPSQ2M5ulQc3Snnxa+DpVkyjJR5Jgm+p8Px+4P+64P063Od4LID/iPYb6ECAZQbN0JYP8V1Rs7+j410jKS50mS2R4xBiO1GMYhjpDTIUrKop39Q5qoRYybqnHTrYH1Ww6eVASi2qrrwQ/1zBJlsLya3oGUCZAVlEAoCK+MvQi4jvDQ+CqNUHSZwUEdD6hTU5J8NpVbC4uEDm8HSpRaT/AqzPH7aGZpcQCE6qae7yIBCsco1gJEOmfuzdQldf3VZt2yAAy3zsoAMbcubDEcJ4RsD4B1RkmgVF0Q5wtIvpeStdLlsLPCsGtxogJBpFs4OzZvdTuqfb0QWcNy5/fH3g8zmCOLfg7SuqOYFhDYn/u48DtxpjUG96Om+fI3265b7fbJyt9sxfb4ALvLLH+5gf5lOBmTeMaUjGD3Q7Y8k221VfXkKqAn8BItRhp+3QbvIRmEmdMNmhPzxm0ofk9bHO6hk6ucePJYiVjcEkQ+jpo/UdzWJ4zolyAKETRM2ScLKQqvcJB83jgce4e7LP9JWY4QCJ9H189vlmsAi1ujdBo2Ck1PXoOeDPU5yHD6yZKlfxP5h9ssAAwvLTWgXHPrSwveNhz0pPo7bAAcl/YwohTz34h+2KEPScfXDIDJXkFcHZDNZsgqeYgGRNDMFpIEKptG3iXI2mMNrEtVMuItyBAEhDPK0Cmk6ZskCNskCILsgAb7piyZJAVKsF9uivkiqDFtMJYqhJtMg8dOYFkYmsNZ/VrZEESH7dQ8w2ZshcGSO/TMM901bqvRGZnmV5B0gPBz9ODvR/3hzPKeyyY+wOPO1l35FTrAuMcpQHUGC3dk4VuQ832xxEZQAGOo49jy0xpNvYNIPOQ7aOMKKSjJn5bMZBW322xwv1s1Z/1Tmd0+dw6e2wHAdF2QCw1+jU4FvO6MORcKBc7bLO9l72dGualeAznJwCYZpGJs4NjrINHA8nzdLV7s0X2OMi1ElNy3iNv5EvH91Ts1uEDlHxU42pBHWPSAg0zxCZLBpsGXQOndqXEsoMAATeJ7yzK98qOhThHJp4n8xOGQJTqYzBoVHCGhTq34GB3qu8EGB1XSgjArSD6/h0s6caHBECCRl8Z7pOMkkzZX+kIkK3+45ACYb+LYNOiaX88V1vsMSn8vRM9S4CrYYbw4AKa5vGJPi6SNkhtDB1myXrZEldzFJ6DEWxSmUWjEDswxLBOwToE64SD5OljY0PdJimxKyPEQZfOCCv1WjRAcsHtj4tCLMZnnW4OOU+sx8J6nF5g4nHicb/j/hGs8TzxESzyPKNv1pmOGUFU0Ykx8co7Doa32w1//PGG//nj7QkgR+a1+zrucahuQ+S6uDpIrs/5rVJBLUBSG6hKiSZAaE/mGonV9xsVPsJ782/+qjF2Tuc0eyG5Qz7vn/Gh22u53GrdfTolw8vPSlJMgkj1OoRNFJUvIQ7fg4nmIKrLD6rX5yPsjVS3H00Fb+xROQ8sHbyZTfON4xsq9pVPhzcu1DkyGC7UMQ0TAKarXzomhlqyKiXTaw3eqwMh7RDptTUWxqiFTWgrT9moVD2prIJ0FKilzcs88jiltQtCBxntAEnxmhHKBlgEiYtARd3+Z3UPNW/Ks5nZE2nQ5/0CrGhnsCz00Rmkq8e9D8qGKuKbymc8ppWaXIvu9cxIMt9A0g8NphEcW31Rqywv1UWGp632ZzjYbA2/n+FbPGCMbEtSlM3wZWns6tt29HJ6bos+Azj58MK2K4DUdMFshcNIQ7th6icF1nMl91uo2sdt1l4yNIlIAST5Wgt8iYfgNWwVcFh29gsVXNp3IDkYwqe57KSeW42W1U92UMv524b1FThyCF58ltpFO8WrydMdL/kY5QhzFjnLuTUEzD+vaeFXyDxrCydNCwp/nNcYyP5YGdrTC1f0FOdiwl87vmWD7GLDWv8wN3iL+4tedrB0cBydYqeqvSsCXZqQTakx7KeMt9ZHEsCV3tPeWWldqIIIjclYO4c34AIl7NFND6lZlfYjgvtlGuXCSpC8nFvNN6GyMFSjCYQ+wGlesNas/Vovx33U2LlduPXVCBYHNN9bOR28LJbbOPvSM0eyFGZdJbYZbY77ZdZIzh1rfd5Xcuvb7s3H9aHt2tzjyOIkgSYUchmcPsKu3OyIrO/oefLdkVYgylxyjlsZRfa/3N/lGST3d9KQk3bCGrTWs/46kTHW2jbWYX/MuSrb71tmd528LsLY9L1bgacpfh2mfqoUDcT4BoqbSWk8P++efwqNdLCbUwX6IDIumo9r9sxa7bP2vWZOKva4+wi+cnx9V0PQeWFlN4DHKg0ZMNEoYdN70n+zVDHngGnZ4UYHyQ2g+AhAbKBIO+SWfO6jg4QBCRYJLu4WmUdVPNhkTu5kXsATYMf30pNt/lyMYNMALnv/eQi2Qghx4gQ7NZgx9KjFffV4x8YYrU6af7cJOnb7j1/OHTG55EL3YiB54gwCIuncQtnbUCajWD0eq1nscWGdHoalsU/KGrTb7XPDSF023c3qb/b3Lgj9x70/W+FeKWE9hgBxD7xvGQV+HtPYM516n3VnYwdIhkN1mzW7c2eRKRSuAjd+vzG+7ILi+l297mBJgkkHjZmbksZAxGuEBoRu25StjzeZn3PD13J6sC3acwHKNuHApSu5nscFHBtrbI904tCTnbLTElPqoVWYgmUUG6MkuyzbfAPJ+L1pqdf/GkBykliMUAKmeEaAzOkLJb87ICuGzACvogzMoZjzgJkDp+mAjh5MsE8aNcu9aTKzZjbQbL/sQbtAVL02qzhJ5q1CcMiMgFSFNp7Du90k9ou4B9r79sldk21jP03+JvMxD2tydkavutsGl2naHs+l24Qhhmf8JFBe8ii229WaGfmvbNtgjrdR7ag2Z6wTxENxssRZ1R0kEIH3Gl7n9VhYEJwAHgDQMm1kTswmrDjIG0jmcySdcQHmDh0GVk+RKFw7cJsDqmErVYNhYizBGgKREwbms7vyRu2F6vURTpgxsjJeqNNM8+O8QgpXBEjmZz5bnlYdIz1ST25AyamllzlMc0bMlJwvfh0Xah4+NbLghIjk1gMibvtVtM22wLnITqfcsWgD5dEzOD0DYy4PFwpj1DiJYKSN0YGRgfWsgTqZu94C7rOjG4ZnxR4j2K1Ute8rnDRb/nWUOgsgZXkzmuE259P1Xv7i+DqDlAp/odHfcS+kpKHZIMgOHbC6eriabWDGAoL6hOibfHNB+7rpLLKqenRAIoL38AHGNJYHufKilaEo4kZhS6rIq/MoUb91Lgtb1PyvPrEGmTHLkv2xpqOSbzgiePC61qRY5amuEnE8giWyaEcLXPaJeERla24A5r9J80RXO5qTxvoMUqm9S6Ki0BamxD4x5nafOMWDn6cBMg02fXxHeL3SORW2ZxbTKYQIQEQDR7TaI2H8n1qLzueN3x9O8cImurwiEdPLWE4v5mc5zFqqJ+d2515mUV9B8hppP95+I5eqRAhAlDZuwRz1xVxGFA1B2cI3FTvZazhhWhUrlUipTYavZDLZvkZGi0Gi1tdnhJ1Aav0cdMjwGuKozc3org8HQ9oe97+Zk02Ji7reZyXOdFWEx5lpiC1bRgMzLu1+CfRfOL7OICXUlaZHJJ4ESCrVMqF08yYdWvaqc0zMsaBjYo2q9Vh9vwNkDxp99ZqAZr1V3SlCR02GHwVQiqLqpWlO5ledaCEaNi9jtpTf2VWRbWAaOMJGVg6Dut2xx36W1HxOkXJBUAyZdgKCY+Z755YAHpybQoeA1kByY5DtYY1BUtBM2TOACCKsqLNkeZAAECaICUzDNInKSt5pDF5/VrGjXwmWSHKRhTCywo56QYlNcAhwDsFY3r6VFaRCgJMJ0qOaMXnRRZXiFQKzhG83NXS7MlAMMueDIXeH3FZKtDXBqUVs1A59FxU7TjpiLWko0GyPil9L4x601U1jOxst3eY4wZDzgGuqgLKREJ4wztPTY5G2xz2NdgPLOfc0TYJjp+odvEGQrpjo9btH68+KdZTa0rYRH9tW7u+PLwPk5KROqVaAwtzlAQfJAWAoC0u0mCsFjmPhiPikuWao15SeIalpuOWAZSyTgRVoSro1VUDQNuPigLmXdyZ79LQxhhURALjHro8TF0esDqo78QvL9/A0oAkwF0DPhadeNmwtBBIQHCst6oznq3muOxCMEWYNOCvyeD3frmHeDtyY1njMLKRwFTypfvQwn6ANCZYJkkjb7QDLepX6ZmoejqOGxwxP/2GwocCcGBOwCUzGTrUQsN18UWDJFjOfPTfJ0gGzmQtZBjBXLMBz4Qj2/VijihRoK7cPiY3TYu+YZIKWefU0gXi/DFK4fFRtzgJIAharEKV2oZYaEChgrRxxuemUVhRDsbVqtWWVJ8/YEhKBMXxnTcbVLn/fQJPA89GFoqGYIgM8ujlnC1NlsyT6JQtqIJxdI2NMa+OyYy/pN2cDyepDFcv1sztom5NmafNod9tk9GOgSFNI6sE1QKHxxePbNkhufUqKT9ggUjM3WQVuA6T9Kx7nWphr4TCD6sTSBQhC5dRtMffBTHC0XhOOYFmdu7WZjEEY0zYxRXGMCRseQK6Q2PulwNgQC0RisWTEG4GcPXK5INBYAGdWlShTbfcn1uIeK/ibhmWWkq9KJCQg7vyYuUCiRNc8MkTllluPzsgNbxMR2awEt0bEnwASLisiLpRFFrzeY2hX0KU4AUA8BhLDs4x0TNhcGAcgE8CcvpAn4kySRSnYCMkHQSeYnhEkZ943Ql1eqjhOxXGrLKN5lsCh6hXd5xoES8yFDTJv2Fo8K6VuUNpyhI3IymkRE5wKkf8uMZfASAHaIul4VPNMKYa0LF/eBURVrAKx7pJRj+EcUrwiuA5gWQEmuA5BpxJP4ic3JJkt8681GdWB8+Us9z5JW6wgigZXCi3Tabd6ms1GznYGqATxcI+Ag1ztVLgsyp6ZpoluqdsdF/OtbccCDl2/70+W7G+Pb9kgXXUo+CKjwgUEAVexJOwtZG46LDtK18qOTGRl30PyXihRUxVp9rNti0cjOHUKTdW9JH7aRqSK2k6gGCTLg3HGm8f0dau9QPbOb0ef4AlqnVFqxHsx/zTsj6UqOKNMY7MVQG42Vr+xmHB98tUe1Ecr5sqZXABp+99kkVa2SEWWISNAdocJR8lZktvBlgpkRIGQAIcDywHRBGNqgGM5uXaxbzlyr1mk00su4DE8d1+GQlbYIIOdMF/ds2osF03ZpSU91lsoU4JkM6iwy3us34jUziaAeijWGBJC0U9QDLGFoDAcZa2Ex42pUXUOWylMwq04koSo0ovtmpsXieYsZOM5n3dtZ7czRtuwe7GBNj+EUNbW1sBT5MRLz3UWq6iCwkZcsdY3eGF7bOujm596ZfFU1HJ2sc1tcX6HPuK7geKydXVdS1rmaDynsd3ggZ9zerEJOkkWF/VYqYLSN54Tyih0L6o2Oy07sQ1ydK6wTc2oXDZIqdS8AH2IS64hgAqN9JoTtKrrileIpvrd1aD+LGZgVxeySK/yej3e68ogX3ivAUBKcWLYyriC47FLb7KcXsrN6MRo5dA4UZmOwW6AGUSrArbXwOT4W3SVYvHzIRgqXnTXgCUTA+oZUdC0p+V1r44aEBijbjntyIMe9ZpnQwVDPS1HBgHSQWmI9+MYXiWqk4eemurnlXY/OZvzQfW2b6fA17BaH1QwzJDz+Im25JywrfjxpmJLyWQTFzpqkZihrpIKQsANA0OZwpoerqbn1ZqAx3lg1CqsGGMSjzav4148fbXx0rDJ9lTa1yBZYT3pOJQCcIoi9snukP3c9pgg2dufI8bu/6Ze3Y5vBoq/+qAAqAOlRscZAJ0Thxp0aFbV1rWwqDLBF6Ca16we0kGywJF2CF0VHb8HkloEpHepuN9BAaVPJb72jy0WsA9U/oZMaeM1BEfJydMPTjQuBl93ClUJ4DEsqwK4ZzwYokCgrEnLiRrZ4hFomwV4jyOf194pscNe27gLKDVKgg72xZAzLALIK2e6vY440KEsJeftTPanAhmxfaoCJxaGDfeGZx+PnMRZfKTbIEncpBw0rHnpIOXmCrKKsRRTRxbzcNskbVcCnc2xB7h9mk4FwRbq4+SmA+OmQKBS5drcz/ZKMpU98gC7JkEgaI64p61JOc1GyGpp7I/akrTzRYd5CJo84cIrcOzP0d/v38Xrg3G3VXxiVL51MsYCRfZ3LqG81RIYHt5TRbMZ61j7znQ1m7GRrl5v7LFp1hsEfHYznxzf8mL361w+zAcblDmhXa1tf6u69oJhpj3Lqz2XBzzBcTkDYaecJ4Op9+DQoQPQqEHZbRJPHdPVVZ+Nkv8ibSLbTN3uvIHkdQXFUZONC6KYYHqte+BrA8iKjzNotiXam+ERDMQt+4+z8pmhFHNONG7kY2MoNtvvyDiW/COpWhdAOpCNFZRPI9ou7k/Nw6bG9GBxhUKHYYliSJgUAnUFTc3vKjbBBmSSEfwtkVNNT5w4EA6Lxakr5pvbtc+xMNXjItfakwsqwqF5XUONred7NESFkPXIi5oxV0DsTIwgsEVKWM3x3c6BfTHX3iDfPq7T3toDl7/XdfISHK/ssWsNLVOmKh6N9ly2c7Anai20oO9XGTMbYPK9xiAZx5sySnIfpby3b5LJf84gr58QJAzY0vykVBRScarbFuo4tdo+bB1gliqmVg5weXpXA0jfj8WT/VuSOp4nQN2BpDmgX91fXO98Z4+Jie1vt3/kObvasuWZV5hMejURnk+2mQx1W6CXeLOrStMmKVVjzUbRtRZM+bIueV8zFsAAWW+YFsTaGUdSUmvn1vDeapRiY8zjtu8NmvBqlGYL8+HcQcXiMkXPou6k0j4gxfgIVkx55L12jSJV5kGgbMAoNWfzOS5/t8FvYAcrzQHI+8qwtAaU1+88TbXnp32qPX32fDzPRT55AslrU/r3OkYnzknrI0nge0r7vQgUAC1+uLHHZms8k/TsWTKvdhnwv1dwj4EKk0A/vkMiv5Fq+MnJN6rsDSpOVpOJkjn38J0Tc61w0hgY9bJQo2Pxr5qramsB5+nOnV26ROUPZZ3G8Ew2NebJptJvgQ3tHcx5/vKmLzffAIzv9Z9YAL+FlBNBxjwmOK5yzCzTSiUL9sR9C1yNmanKpOfwkkHTixdT7RuGBF5vVy1cqmWc5E+bQkEyLdP1abjdyxQY5hXWo88Y3O9scZTXVh0wCZDZFrKsF3bIyqOPiAhHPzBAmUHBEirLEMFYAHBAZGEtAl5z1MSg8fwMhSqVu2Ikt2SPi6KQ626zT3RhaGk+oKf+Gvb1xBzbzBJQeP/+vZdzctN4Xnz7QhWvKjVZ7+ujI3cDv/46gREpVZ5jHhtJWMUgz6j9mbVPU8VuSRRbKi7NJ9IHJdt3ZcXyotc+O763qyGecCQv2XSXy2dtUKPDGOhrc8JukdalC2sFeOiKsJ+YWAIvxQ5Ahu9y+DgPPM4Tx3lgPk68rYXjXDkvRqxUSqdFNvli0J/eaZK03hNQtZMArjDk1SS4yHkCfBqg1dL+aGYBitylzesdOoMMlkTJMicyElEmZOyAOOfhYRVjYkgBJp1SxO9uEzVlVSQtYSCAh1r5b3K/Z+73Q11FWb5+weApo75JmBcl9eLEsQBg0GFR8SiAf1lubAYQGDtYdBhj2GTNvAJNCfNDpJRa82LL6c6Z6QuKFX1S5TWymVK182/3bqPiHre87Ms073bm7pTT2FwtQ43iL1HoFYyR/PQHt67Iavd/xdK+jgGfHNb+/eRIgVGguJlo2pqQS6eVp/pS8/Fcvm3vWRXCvZTZZTvXCO85GQaXzQ73lHRfe7+j7x3/WMU2UixO4Ct1AgvXhleSMYn0agdQer96kUyPQYwYuVCzWVVmRSjHeZI5tpzMNWMMxIOSow1VD04TMF/L7Gc1xG9Jspgtez29+pk1JBmbl3pYJwkaEs69GWlzWVHAd50x2HTMCNzWCABRjIDLZVxsj/vDwyhYPIBl/ZOVBwMkOFL99S4QTPFanvQ45pa1w22ZboMc0IgHV4PvWyMMXW/Gcq2/GvGODG0p8YHGsLA9sqAIyPSKqXA+0fcrAdz8zDAhC7ENLwVvta3rjXQwVAB42c2KhVc7LpO/GBfn1iZ8DNyu1+t3lpC6giD630Di7jlPwB4NzNFMKVfADIGWhXq325YXk/0bx0VI1HX3m9p5A6lCsexUmVkxfrWq4CftjFGX4FxRZb+xR9qWARAYOc8H2eMFvL5zt99mkGjXs+05V/Xl8tZZJKuuNMfN9FiSFZWrNQoMDNYhNIJk1F5cGvm23atVu93JcF08VUY0IOAE7jagl3fXwHB7v01jqXlWrKKBY94/kmEkg0QBBfeoTrCMNlLgCJr9r7GdIRO0Q8oL+yO/i+yFQOoNqKJEGQFpxEZfVvdDR8+cM6sgYSnW9DhXmMFO3w9HhEEmDSRTSNgWnO4FFbqNuRhusUe3Dbsd1d/NTDph78Sii+lXAOktWW0hq9S4bwAJAVqhkyvYjBzbC3NMVYNT3faqS6YJlsXeEeNak+sJeNt82toDvGxffb6D42YjtfrzBIwXIfrbg/2QL3s7Lowyb27vs5oPl0wZ1Y30PFfR38N7ttRC3gB9AyQDl3797vENBrl3HMHxt0LoojINAeYQ6HAHik1PGyPAwNxuhVDZVFbZbOBVfWQsDBX3YkXxzHlO3OIvB0NDQsOwxZoxlIL85aWdpd9YSuJwbNDYhQaMo1ZOQVILnjfbQIL30isUnWcb/Cgy6yoVYxCjLTIhwmyFUao1qzejAnFpa0kybGjqnnoV8Fy8cDsfvCIMIFtxj9txJECaqNuEI37RRuyEiFH9SS0AwdzUVW3G8MEs85UJ3lk4w/YFGKH6mJA0P+SGT9HPA4CoF29YkYt9wgXyigXb0yqTSbbxIntEjHP3xlKwg2MuqHNhtzeWOt0EEeMcm+To7HjmnEQC8RUM+XemkGTa5ydA2ZEMTeWkNiGFmN+DEQoLqc5os58n6+wx760JTq/UVbt1emTK8n2HsjBumJ9YLZyOm9R+AiRFAGhkYBV1+2fw+DcZJC/8ChP5viSw+dAQOASIUA0vg2VWzNIje7RUkMVfGE0LkaKlCZBV8ujEfMwEpDlmSTEtSQ6CpNVi2RqfE2xAhoFeFaZGmbmNzhmquhGfQbCyl2/iTKCc8JJUSHXLQdISIEvF9nMLHGQOAO4QEQBdhZ7BfGbZG9n+PjUagyFr5MRMFduAcYQpIcwTZPq+L/RRYzQstkyIupjnhMoMM4QhXG2pYutwMB3Lw30EXj90BAV3h5UV0KRgbeFikFCXq38JwDFrAGHGCQtWACMiH85zRUB1Y7U57i6K/Cn72K8zRCCzq9+XOcPVTpFLTWWLcdQLO67+pYe1gyOAythptSl5/coGqwwjL+XXvtuRiZM7hJFEv0ssvq2uRteSPmGTG1PElUV2dOzn4x26Yy+TIbRrg74Z2+NcvvlagOSZ4NkcNFnFpzQCGXTIWIYov2j9y3v67PjbNsh+/ZLE1phlgCNBMjp1DF/oFuE9c3rHCQCbA8t84E0EawzPl7amtjUVmfvW9JLsuXisBhBaoAuqsFf+KADMvZYWwdxDkdsGGHxiElj9MgNVO/AyQVBTolhkpa/pxjDawyoTyBe4N3/21YNaxCK1SW1+RgCI2EV/vl+TDMcW+8XT4lLFBlLF7qFEPvEMdnr1cFsWlVl8gxGvTjjafSNZo4r5HiGRummiOWsMeyD3drfRpwkoIgAZpBRfMEOyTY6AwLciwmxhPi8AMrn/9ryYpAMVgsX3EY5nl7mZ8w312lkj14OkZ573kRLbGcPuHKLpRFAhXWSToyIN+sZ1tDn0e23FfqLPLAQEitVSvl9C3DjPc0yExAA5X/vzqzQxzh2rse5hO6t7qbeYyF2t3qIE0IiOCeRzZGzvfx0k/5GT5tqUAsd6p9hjMUXjRB+1l8oCYOaS0MaAji61Y8B5842RZamtsF1wAltmRYSkbKrNU4ZDgGMJvbB5japEtA2KGahmZ63CmCyQvshq4TBflqLjCRg3gARE3CTBnVQt6zZtvYk9TrOBo1EEtLY3YNTc3bEtaB05eG77rArRLJ1GgNSpGFOhQ0u9T3WLrChU7OhD1WCQQgbp39VLOzmZfKFyQQYY5u37tdglCmBG3ry0ylN9UY/G5ttlsi+FF6KOiwDIHi71tOivgBhzDJbMkXOgTzeug3xOqRtfkO546QHYnzDIK0B62/cQF5HKovK+KdGalxer3Id+o+zHjQy052Of/1dPegmxBo7x2OIbWcCDRSiaiSJNQ1wnWuPJc7/CxxrX7x//SMXuN47L614UrMDRG8qUMecOMwqdwQFzDsBYJXrCTD1GMs7eWZopPM1oKe6P06ucRIfOsSrwN9rmWSHPDAVCZhtA5LmGCTJolalhEiq6qwojPMw7g2QPRJ80gGBqmEbbNdRsb3fsBwb4LozMiU7np1weyH4waSytDYQBblZQwzoV63R7j3LHt9X4VrdHhrpGYGQKYyAe7DDfffAwr+s5pxeMGNb2S3H2v9RNBn5ehekCKz3RYmRSMZp9hAYsY0JFfI5QuPZB9KkVariSpVmo3FFhOwUcx4WzFCgPKOdxA0OhbZKzcD/IgLvHOjeC29hjw3ZDCJwSDmRdBJerWk022UHxaEDphgJJwVPXD0JhJN4GU8GiphLXlmDNgZ/eHgsi2kD3qur3eFvabONsez9Fx+/ea839388zyA7V6bNly3A3gRYy1c95RcUmQ5+R/hvH/5qK3RPz6jc1KSoLwmIjJNp9FCtzPQxqZa5ec+IGc6eMeuixDB8YoDr6jPJWMh5YtrCW16OjYX2S4cUVRVrhha4ysLVmSKs5sE18d/QseJVqhWBt5oOuanQ2x5L4QJRyaozRP6/iHBad674qAWsThgk11WRZ7v0fUkx9mSbb0uGsCdFPDo4eUsQJSFAUEbfRad13FvfIvG53qBEY9VTYNIzjwBinM8plYAK3WZXQWqQutsKWyIrznCwxP4LEMkUMMYYQlHrNH41ikRRJw0bGugrg8ZcDkEXzRmePffW4/diZyF58pc9rDeHRF3vGPVrZIDlnKoaKtxlzcJRzaVszBMmLWn0FyDGiyrtw98Uo7IGRtl007cViWJY5c9dhbr5JcESAYrDKMGt14rU5g7aqRjQDFLj35eT9Vr193ZSuvNMFiNyMS5t6XeyzSMfT0eVag2hhn6D//evjb3mxky63y12f19+9kweRIzq2K+DTRhaztWn+vKlJFpWpJdQJGMHC93DxrLcBnYbDNG0zTgo9ts2kgm2pjqdThv+1CeaHwmzEgJ6upkTeMTjB5Moio6/agKoigaMHLCd2JEt0sS3tPTVxm2icZ6h5FWmqy1EES9WwWONVea6Q2AGOLFuvqyQxkMQ9GaT3Rc9XTrECnbFb5dAMNfIMH829SshUitEaEFlCHsNnuaA4FgmC8ELHV9UOlwfV1AQ0IEKpoqSajSx8Uaou4TTGua1mzrMyT6D9ZYm2i+miP/BC5W68uM+7HuzD93mfMn1OVipkARCrumeFdc7pnNs1/xhzmsCi4pt8gcUsLH8hVK/ZlK32a3U7TVcdFDdVOxG3r4EiUBUDGYKbc7gBZqnWVtlIeR76DnaE/NRplGvye+wR+FaxCmSTNkDMNu6N7QyzN4vSTQLMJkrQyAw72JwQGOw4ADGsKGmlpDchUQ0+AbAUIidUDFMHxlpeTi1sNypeVNZL/wdTEL82cgJGdZyLDcdvpjuFmgQ0EgRLBtlrL+bCUosCDXEuWAZZayMZZlFgA4LR1GheJ4OztcBWAhyXiddhdHqc5bBGrHDuG7MilELXcgZoNS5ZuDbHPARHxqwGgxR3rulxQJdhzoUxD78+1WxdYHHlFeBiZlkMVURTJXQwqImcNj+fKQkoyEowks/JNvldQziBvHhipFdSwPaZ2ihOjtkAVf6+C95q4y9g/nowREJpahn1tyLli6VpE865MrbF3AGSbI2B6wiAHI1FEiD9/jNzKto/BG73JZOMa7pNXNwhCYTDyAPr2ZdbaB+ZbGeQvUBFK0rBtvL2urPK5/Nud0yWmGp0BIC3uGD+jvf1dOxU8SVY9s+/evyv2CCvr7t8BkKWh8RUqyrjAkS8n2HA7TE4ZqmrcHU8k9ZVnYGlBAlGtRQPGEQ1B+3IajaC25hYk6XWDLdjIreEFdp0whnB3GZpbDIl3sK5EIPrqZEMfqZTpcJsUCqOBdsMtsbKIxosVIMharOJ0c6oOjBcBnhJuAWsZYAoJtmeiqumwZATHDkOkfbGGDOGS3gWQo2DrlKxyap7Kf0xw06ngC1AT4UeCjkOV7OXRiiPgWXUgDKo5yZcprkIhwzIkjy/C6hg0GgTfMSKq3pkMfFavGnMMWFd0VlbM6S3mOpz34pCBB4yFeCRrNHt2xILGoq02eY1EzwLBPwvVet9Ndd+PtKcGuUpT/vefAU6BKnaGyg92ah5t809DfurGlYWq9TIXbfynovbJHM3xFynlmaGvud1j3DgFroEco5L7yeNiWZ22eeaazsSPdLpqt3uSGdiMXp2a7SsWC/nC7AJFbbpuxzyH+ViS3vDLp9L+4FrVwKm5VkoK1481oLlMQfDg71lAbDDB8Kq1p8GR1jBRsjQvGiCZgfpjCK9awBHcYYhBlWfVNnJXJyxXw0dExUu4edfy5nvWKcDlriDherkaAPEIyeImYtneBYJWaBV9lms8BH9NWDmEKcqzoSs2KPwuqaQKchqv0BUyvGQFyG7CYBcj7LtsIisMzCLRVUkUgTB5qowBqMJanc6wxgrnheLZEEKQ7vBoMyVQigY4rtbjmnuTJs+LjNGLD25GzhywgcgJpOsv6w8pFQbrWalq/GjsVO/R9IeNTLIUPPXyvEcZtAMJ+FibeD4pFpHkw3ZdjLzMTzUIuNaE4AAmeU975WZZEg6ZXaATJjIKvBORhg5EAAFA8JcxP3cEQ7KAWFwRkSYaIwBsp+2+o75KBZJjaOOZnYAQfvqnb6+d1Gr4/VFBcg7zrGR8PJTwci+RmooxIGvHt9WsfvhC/lyxS4wAy0TSOE3ENMpJnft3+t7beymc4ir1yvAzwOpxVlRk96+v0tcSOA2Q4t9mWHVeQoczV7EdlJKc9+THmuGUK8YppN2VVMHtFClOChsNyVdST5Olhb6Ys5qWP+JDCcFTnxOe3/mModAUUScXwgbiR9VTGoBpHZP4KLKhQKYNLiV/TH/Nm8lzGIBu82xbJD1MGGmtKQQQ4RpSEg2gS9u8/RvHCIwHTmuJIpsAwiUIMi1iRYvHZSLvQjcs02AZOk4B0Qyt5lgCRmB4xqsUHuxc9dSdpL0dPDq7FsyhryHEYDYtiBITzAdHgxOHw2YpIL3d4BECB2qy6HV9Dke8alDPS3LMCCqqc15nQQJ4drMG7whgnSq1Ttwb1XDL0zNzCprKk0QBYTpmc7tRSoUqPZMspyfqTz0ddKY48vnOV++xyK/7cW+4uH1ePpcGGYTnrQRgBB02+bIvWvIzlXES+nHBMjCDmPEtgSua2rkXEOjWg98IERcNT3Uw4ZEsE2qNYO1WMJ0TrzjmLgdO4uE0Zjs9/YQOCrZCoD0AUzp1ReylaGcIYtFpqhie59o2BxN3KudoUcNHMVcxSbwQT2/uyaAhV2X98a+N08tzPjHVqSC4ZR9L2yEqirtEQvbzJzxTcWYM0J8JjBW/HV7gEXIEZ1a3P2Q1H8AOEVwwHBgpjAcOsDYrM3gThRsLF0T8MjoJIFJu5Mh5iZZIqTAScYE2SqD3FnKH6LQ8wQdM2OMAEnJ9vGckqcPZ2BfyU3QiIQ9d7TSdLE1Rm5Fm6yMKm1FSQwZCWisMJR7BsUES++11xAMMFfYnEDucTEStKAu6Khee9rpKACKyV1qdS/K3IriNiClKaFMEGiZRi0G0ii0r1lIF491I1+cD9XHu9Qqc0RXOihIvw6R/1jFZn2K3s4waexsEvVeLwDriy06MRasmmQg7ByjNutZC0vFK/4g4gjRqLuxtgsyQwdmOHmeIZjMZrGSrU4canLO2BaUecgItWoqnLlG2THYDKkHeMiPtQVD1rirrWmTZCUcJc6V99T/l8hAES8KG3NaFjCGB9anFd7PnF5JxDJ3ZlBqnyk8c4aOodQECxi5kPm3FxZJlmNy8VrPfO3g6ABEFZsbK6VaH3Y9sQrZIqodOjbDfJ93/rfYIfAiTAaN7UkB5g6OwRob8Lta6Gq2mmszosz08d/aWp66OOhahAvJCBpLOySD4SP2k60elzqeYx4b4JTqKmGDbDa/IeU9FpodkHG+NFtwwpkZbLgNFQBWAyuE8EuQ1Givx5UFeFUAP9kvGW/ZHcs2nXbqBpSbKhjrLYE7tIkKQ7IkDVtSQy6iGuGytlL2+JiOF32TLJL3ge8d/0jF7kcHzXxDasLmJ9LuNWaxq8gAueYgiKBNCKubBJxVThlhX5E8H9V3N05bVXBplN0LByAnEk9bKkyp2wTIYcCCM08NtToLGAiDJfxMu3rhfzMPG2SNF092quJhciDTjs+H1Xc07J9q4kHkoIZuOZfIp3pfW/tQuPDRplxOLnr1W+GLfET4TL7P/PMRzGlk+p8Z4yCtdp9b6h4nKy97Mg+tCtEeZ0qQLB7Y/+2flOpd87AiEDl3YsU2dbb+jryHbDwc+rzWpGGaQed0X1ubczl/LJi7iJt82mQXSAKjhDNwHHNnZI0xZnphxkOiZddEu1CLf7Q2p0nFrMY9Pvb5DMjgDHFQhBqmozw83KcSPQBkpgzNKZ1Bcu+Zvqvh1Rbfx6uHvm3hO/m8Xbj3L4W31Fyl8EogbJ/VfK6h/x5//AdhPtfnL38DpJK3v9dOaizg4MGrgHfcACKDRoJFSUlJCKYpxgjnTQVP5Ml7x/fUJL0OSmsYO7l7tmcYTWknHMPV/ylelUhDnWLH9wXMge5gVVIUTb2+gGTch1oBoxrci30FS6BCWbDPK+nvGJr63G+7q7ARanyZZFQNOTEt+mmziCPAEZKppBkkbsjA+KUKWytA2rcEdrU6APJVzJtdZ5nhOs1LeKLa2O4xF3qslivoJ9ATQMzidwqdM8eOzHZRlYv+06UQW54LLhpCgtlDxSCLRbY9pBNoZqnNtE+2GEgGjYt4ZSMC0IDlNhWcGAZLZ82Mm58G6GA6qWtCkxNVOImiEIlVlCYzaTqDLNMA/7ZYzWtsJEetrTlqek/AyMnb/+bYFRCi/eVzCo3Rrsv+Kt/A91jk31Kx+/Ok4MYYNAphSdEllx9uk7tR/2GAjZmTcIoEqIUNUoZXaVkLFiq3joFpA0O9UMJ2rU0SlTctWcqgg6UCZGlDPISZCsEQFcAYOMaARcDYiu9CGMHjS4qT1YxqV9xzSE1t6q4WoYJqqDXi6jaLCcEd9BEYHgwy7mnMaFugcwEBYaErI4ohExDNDAqEB3XKjNJpB8Y4wmNdBXiR0jvOyHGjMBhSedKQtHItODieWjvV6UkGaVXYGIAM30pjqoMmnWlkRNKLQTam2J/0+cmvEyytqX5dAAAOSkjHTQgksQR7hJCgyuuhXjMrI+lS6Dy9Nmakwunp3m+P4JAMuJfYVO04qGJz07XmEZ6lWvcwH1pUqkRJzK8K6XDhKursHDWv/acTS1zIixpG9LVvWzFKe7PSxsA+y9CjgXHQLMDwr24jbW3nYDSwazj+DIpXwZeCu8rQDRi4EynbJ2GnfQWMfxccge/sSTPq1Hb90AC6swd8jCqkZ29ZhjvEe+7cslRHknoDXrRiubq1RhTEdY4Dg3ncVAzoOZanKUYppd5ZQJs7TWJl3T7r3j6rdETxqBMAsUVAMMd4THGQ5GjQ4dP7xQhsrfah18EzLCtVeyWjjAyHQVVaYmMqP5+oYSrS9huO9JjMAYopWRn+4ejvC13BUBL/ipTKR6cBC/IGaJRNaQQ7C2kiHNca58i6draFUq+XWRQ39nAjMOiaXmERjDVwquJQxVTvozmp8ikEE1TNiyJygFBt6oe8fNqAtbMdxKIrkDxkQCKv/1Rze9tawYhpVz0dKOeEzdPn1TLYXAkAAq/WTnsnt8pgnyfYhGknbZDppCn2SNCjkPQ1RRXEG67i1Zn4vdWEWWplA7kTKIvG5G6aZmGDlOxXmc0EMMOx1E0BDZDceRS/jfVcf/Hi2Edn+y8FmsfIjhEhYeZJFeyTCjmSTShwmiTz/AZM/i/aIJsi0yauoNRs3ghDNKh6VFAKMnDVAnQVbqMU3W9rmccsLlUcg7bB0jUZCL7bQSxZXKrXFks5gNMdLT4ATtfzlxlqmNoldipP3Mgf5B9fvV1qKu1Eyvak8K9J1JtGIDTs35W6FtpkyQcZn1VaX8a/AaA3d4yJKb6nTXp3eym16McOhATlV++RgSgQ+xyX13JlAKh3KHej3CpFa89pbjar6GgKWmFnSRuoy5DncQFLqbtr/VfvDJ+sMReHCxcmFIRmo6dizQFdy+egsGycRjUozgvxvg0W5gkJBZBuz2sOsQQj2iALwAVIG2HZXySksTXVnjULAINicvARYBHB2zIst6zIqAOgOWkkBGkwtSGYx/DohU39lwSiFDg5ny3Psx/S5u1upsq1xeft4SFJ4WdP4SEvvtv4mNQVv3p8HSAZhGyXC8SE7PzJLq7FVDLjJlJLEknVPOvlAzDu6GTlZNHh6rXLQkDtwDFXhgkcFqp5NtiaLaI6no4abIuvgaNZ2l8EGW1Cf188gvRLfC7sl45WEu95vxCQaGtk2mDu1xLznJoS2+uB7XATAJ2OhsrTpnodM6DfK02EmxRtQO3NjAX/Ym+b7qjpo55aQbznjFGKNYLAiGT4yzRU7SpbJXEySv2l81JSX3O8fI+icCggWHaCfAhnf/OCkSV8a4LVU8lPquckzglQpRUPe5kTMwuW+LxZ04ujmCp0nFhjRBEPt7Uy1z0Bsnuxr/uXSwPIo+yPVFfZLAJkDuLySeThcxoKXaRcgnnxWYHA2bFEeJhKqtQM83kJkAGOBMp5jARLmh6qfX0th49B0Cbdq6PPX1rDJeffgMZ89L8GcxMZZ+PgmiR7vKra7TK/acX1+L4NUp5flxrxmj2zw2qAitnlv9uJJWkUJbCq4kRJDzXDH3Olevyw2IdmCJZ6wGu6/OWZK+Q2DNZcybov/c6NGDPpAGmRpUI7lw9OryKT17H9eQXB+rxeCZaWAOntc3AjOFJV17BHWut3hI2LQfslZSsMhIMjYyRACgDEgjyOids8cJBJUtUeNVbskUqftIs6TbZSDqhlxSBPVSxDbMwWQmoIZk87Y5oZzx8quGcvzhQ0ku7qDn5X+oj9vetEDZATLt78aTEQZupEgk/dP0K4RnttLeg8sM4zw5n0PKtDAAfIzEYhgxwlkJqNs6caVlwhb9nqfhiRIRG+pjEfVX37CSwIpmfUpJY2opqPO8loCtFQWWtMnZ3x/DIFiIyfMSVCpYjYfT0jlrDfu+me/HHFiCdOuTHFFmKmklElGBGWRMRrwes7WP895sjjf03FblCXDJHHpmJbQbo3/ql7glk4K+Bi7uosDDiYWx0OnGMejm+60k4mARA9ul8am8tFas2j1hZvMUlrd1GD/fRoEqImg2RJe54lVexqQoFLfFeqIE4xSGPMWH0XKunUIU10CYy0BWUamvmk9fPGmKQNabbQjem58dJRAWGnakBPkIjXdNIR6PvnXqzO2n3T5oX2aOdqaWdjVDSCkAqTmUT/Jg7+BUnJTpemORj7xFLI5FyM+VN0pGOeQYdihrNQo9CJl1gLT7ZWgwa6Z9pTM0f3DMvFxhfhRyMLV3B8AxgbqJuEPT1GQTEgYZfpcxjBHocZZJRDxsemtAGO30a/ImJBRDCOAEuRUrNQ/c/MGYHH8tpvB6avoYuqzhlNwaEaAE9yYvnd8vIzZvQCjt9kj8C3APIrp/5MNlzpd2OSrcmEWIkv56JWeAiQOq1WMy8UGl7lc87ImolNuxQJkNI7D9XhT00NNLINfdpffmfTT/eTyFP3F6TmL5oZoE4XQeIECLRL8zfWgZHtlIvW0u4w73tsk4+aqK8tyeyIVLGD5XDzKqAF1pujB9MdnXw3NtnaRiBu+JpMs0I9Lu8D7Xe7IKj7DpSLfiu8DGRLZxhHR+Ks8ZxsMdY+z5t7KAV4ivVF2gByeG/yDoaU3VEDrBXBKIEXABmgNzzZIAGyq9ctHjLtkI0ZZeM10mvVPMRqeOylJ1rA0zYFrnK0/kiSQAJgfkceLiZpaydY0saX9y8jHP6zkK3N6b4uHMQFl3dxeWObv9KfbWwytEeRKNd2MdeltrjjVaGQIQXMF49/zCDb/XsTLq/7v1y0POHVq9T/JnSaSyNRQGSG+maZM32ah4Pc7MgQIxnbkgjqXWEIvW0bjePCJbNUi31TgCxh1W/8t/3crhODxjVcFJsTh9eNR3xHrdRp8O9A2h0NaLEsEmrnPqHc9lXPeR1+L22Qk2E+xWaqfRHoHgylb1nLOn4Zc2rVpdm92R/dvsdpsIv1BE2rd54CikPQbuNxOboIq+d8FsiowaKUoGj5PkblMmesZBh12W9m/j0Vye1zhwEqCzrEnTQNIH0sqEZXGNWTen0BSN8amWAd51Ovq2kK329HuPmahfbRbJBaPeH3EqYFIM0k3alm8Pz17LtkGmDBgVC3wwzBpZTCESHASgptBIHCrgnB6iP2VNkSaSqiTXIMg6mz2TRrkeRy/gPtUZPxX2OQnx253vlaagH2byV7jC99Bo68wX6GBDV1Q/I0L6Z76MQtVL0FB89TvfDuQmVryBDMgQqhYCFSDkNnKD18IyaKoAWaaxnpgZ0NbZ0AIFWQ+DwlIyO9wzPqV2D2NJIBQV3N8ZAfX8yZgiXFBOvinBgVFuLe6QaQSbnK9EAv6u04otRbGPSjPqWtypMlo+TWnIwBXAmUu/eZ41eq4wwQ8vEZQzCOqADfKtjQ9rVhFkrNh0XRV1j2a9LjtrgNVkyDQi0CTDNNdggMC1SxhWUn21C6R3sUq4n7MoTgtdBgYg6rODxh1Pyg2UKSSc4GjBXOU46ZsdnVunnBxijTjUZpshHxs3D7nKdPaszDCJfSqPuIckhSxrqKPXJuKZClzgAXDhmBEvbI5BkW/Ww+D8jqtuyuq6D7VBPb+1+kHDBjCIw6fTj62MOb1/rTgyvxa8ff3hc7Lye7YplCAw0Q0ZljrdHiNJKveZkOkm5Ir5sfQzAsUgHnwMTEQQapgmULI713SM2g7+fxKgTIF6KFR1KhKlgLCV2rAST32ebA+xlQ4J8Sr0laQ26VKmYBlGQjBDuLVDb3YMMcHIf5QqitS/tk6/1W91ZhIzG5MKKfwyqZWRplg0wWKeIMKu55nQtjjlyQ3Ms4C/Dqyn4x1dYeJAjbHJiHF0OGDoh5vc/N9jmYuliChX2sQa9HjJNrmhIFSmLibSq2bM6slNuZohQxmE4gCyDVPITFmnEkCVT0y6bKSYyXt1uHF5NgPcpaQzUnxmWc+sZgvS4k+6+HzQAIdltzSyWEnng9UOrIY0RAbYt7Nd4n4l7AiJ9QvyXlDDIOMq5ltMfSyBffMpFKirgwZ07u3VRSz3ctrt1je/A+PbzHH2NUJbAcI9l/W2yjzYFvHF9nkBsP3oGlfZCt3TC6s8UikTt3FKTdZzv6pGiTJZPkMXBgQG2GNFdMFbcDRfsoiXJ/jyuDjNuwBoCLQeniH3KBLuYLW3mj+9jmAFEta4DpBMxBRxirw4XNNnDSIIBRZGONPRUvvRuzuqri0kYKg0m7V7wHVNtoE5tztApGZJAxmZc7I3rq5joXdNsAzItQsP9IbQkqcwxgTkAt0tscRA8BjqPqcHZ1M0GSABftyUyiNl2u06aAFcHLpWJGpS+rlWYKCeAcIwLS52TXBgMERD20REaQAzICQRS4MIwRW9/G/OAZRhTD8HEqttjBb3NScJk3FpeJAIHsXlu45hgGTS3eYFEyMAeuAqpYfaFiCwCmYRgaQHIMOIdrMSWDBjyCQxgFckkNTXX6hbmk29erp+sooULhVEIlSjnl+HJSeK+FAG1nbJQLXz3+sYr9l8cm9vp7hfHbbP+07ZZzRYaziDkHbph5ujGHx9vZ3AajmKRllD0LUQyf6aXKqeGxfDOuXGGpYrdNztX3415N3QZisg6qiiNVpX4bHswLDPVQC7pO/aPyqELCPgaNcBdPEbMVHtLcbKUmEhACgKlrUZWIdsViz6OxxyiYcAwcMzbnonq0DAuLyn/2w+OxcL+feNwXHvcH1vnAOs/Is/bOJGAfY7gaTaajlREzEQA5Bm4tu4SMyqrLYoDgmp1FAsGWEOBzpNTCYEf8rLEui+Bqg3gskk+s8NIuH7c5oceBaRPTDIzPtWNimPeZoeYIULGEFA7Iz5sxiUIKlUr4tFb6evmL4yqER4TAGOAgKdyV0yOVOcfotHDVWjJkjZXZve/qOVlj4t+osK/hEiJoiccqo5lZCggZQxrmGN3XKcGt9puv9N8BZOqvL3g3yLti8degV6vsix2LbwGkbc/qElc2OfLtTn1fAWWdZ//M8lMEQ955twDBHo2iDxjw9DSQ4RXjIQN0HPZJco2ZIjiu5cV5TwNswNMWk0F6uqMHO6/MDmFArUBSzYJIBgFntRggyvd76t2Y6urc0K0L0tanHnAxCFah8lqwXTfSU0WJ7skSXhIG/hkeaonsjZHgWMHgkgDJjA4BnDkiSlItTaGjqrg/Fh6PE+djYT1OrMfZmKQDqsDjB734K3y73+lOg9x+QeCl5cSZ7jyOtEXGUvGFmnMh5EmE/ATBB+tBUthmST1EuEpMHMuW9UwgzYIjEAHrNMpamKpuvjHFtBmgijB1WM6fHo45ZMAGZ3fN3WJWzbR0Wawcyv7L7uzgB0xk2NhRrDNnuCOcTppl8waD6CmgUeyR7VGCoVB9LU8xBYpRCAk5hM9pkdLW6n7avM3Af60q4bg8mrBply0TmZWtlbYomlGKuDasUmyHXT7/q+MbKvYn72/I7VSYHVxvy+u/7dQ+2EXRrzfixvb6soBBo/Fjt+Dntp96cRZwjxCeefOMxUhTyurygGaEKkIbpJrhDFtbgqOVETonTNyjDNmqRvuNGEwUYzETgeXaapry3sWqXWV/pLQFckJdxob3NKjmDykb42yAyCwFqtmjnicDIBOJXkgTRALjikK8rmJzEQgnOtWhsH9ijCjuGi/lsksfAboJmrxVzoXw2EjMlfRWsN+lVLZmnt1skXSIWbCnDqoyLJm/AZnFAiCzSXgMqXFj31MIF2pl03IVd/KQe76wnaBd2TZc7IjYLV7szKxzkFIk5vagd/06T6o/+om63ZGXZN+wSlP3XvPeMgSrn5fzt5lnnp01vMi1JQRIqceIyI4QZrwOZ2mSrzbYX4fD5+Nvqdi7w+aZGZbJRPqLXVheJlY/pW2dbPszStCQKMwtBaTCUAiQ2gdGcwDIIQiSfI/fW1rAxBxwA6sKLc/BNdojo2WkM0IWEupay701SHhPxStyj5WqeIW77JMMRpZoNYGyEhGBgBDGAeCkqlQwFmHtYSVHqNIVXlITkdfdWGuw6LW0vNiPhfM8AyQXTFc4aZp3eYzyDJt4TnDc6eT1hWA5SniNsnE5u0EyRiin0GUFbNPKivGFJOUMyOI3xl0Xkd8dTwA5au2GoDM6YEZUmqmur7EYsr3HJuWfXNM7HaBIFkgbX7l8Z58mjr3R42FTNZgL5rhRev2Lo9U5fdY9A2RiF4GHwCRk9cFGJb5j1d8bKMKaat0AMthkNzRfYeKqaoNOoCFpjmImW3YJCQ771rKzvwWY/+thPm2G7M/73y+dqB8dJus8uT0rJgYMczagsx0c6Z1GLHJimTvlfHS5YM6lqG0P4vpGdXrPxa1BMV/4zQbJiidMJ4PAdyBcI7crmBqqdgLltgR6F6SUTRtlZxNmrZfaRIf0G02b42QVme49ZacgJpS27Bc1LFsZ6vQIYFynhrOmUuw6mLvZKkAZHq5xjApeHwimDdT3qxmbqpzjz+nCPulzLcwTxSSeuxDgVq4MdG95xyFoKURMADzEFzY0nBPRP3OGj81Az3RCWRPke8O5Qu2FLNwXcw+TotnlaqZ8uXA2RhpAizI1yKzwmyoJt4dRZ96Y1GcUUgwiH0Ds9/PEGzfTFk003QHKfHufumW+4aBx9rL+ge/W6aTIuB2JWtpNtQHr1hbbDHQ78/7C8e09afY3ZX/OxdWZY8z2jd3sYv5ZwkpNjj6tOzjmgoKEx62YHtC9zB73tcLuAjN0c9V1banSVqkRuuBXpidOoTv4cqI3aZu4dAn+DQqFCQvwHBhrZ3VmFntZSzLBDAlpzKu6JiZBPsj0LHLNgwGKZRWfYlR9+BoI8VwRC6DGPYrb/sXnwjpjE7DVbEoNHAXV/tnUeu49ToD0+dG5ovfTdVr87vXvaEFXBS3vrxxiOZ4g66rvShbOEMjygPAlLPjrYEdbror6JmTg2PkddYy3a6Mao+kL/Hdcoh/5G2mzwzoy856kwNAKp/dHVlTNfx1Y63mfg3Zl75sQvyQUkKjE69JOqrr/dRC7RU6ArMEwoo+zorhZE0SW5+qxuC/jlb9w/L1Mmr+yL3Z1sYEl7TLFF+TFRLCaIRfJdGWqeZaLPcRikkh4uDTST6iW5ixBsZXqXy4aAMKOj/dczhVzTNbWba/tvoGKaQsPoES7niozN4CcHOMAyD2Nin9bd20PTkAv2qGqWFRjVT1GTzXtXnQAmJW9K+4ynV3lwV9po82YRz63fUKm8JEKfGbhAXrJ3Y+LbXL3ZX2NvX0qLP67w+rE1aLOlrqqiU+el5BlTriq248VmuPgmSeaXm1yOs731+B+FVHVxr+6ze1zqtVWgtpP/wIkvwDBcvm37odMtHqzx3iSMRIcYVxLtRHX1SfQvde9Ny7WqixmXWsg4oKl7ju3u6gG5Rrv85J3/9Xj6wVzX4EiuuRCBnDKdncFnGW4RqbFgf8adle9FRu0bcpejuhBZyP1+ZTukQPQQh/KLvPijMki7bIggy2SV3XqfmUHhQ4gkle5Kg/YnrHR0Yr4w+OYoFdUcgtbhHbcQnFSPe0gGSrwUpzncrsefMJ46FtNRACwMX39GNwpYpb2Mt4HN0DTKEC8Vu0+l1vHMj6Tj6b2g21s4D4YFC7Ne74tWsv72SHrelzGpp+Cb5T0bFggz9gQQCq4uDv7VxIkvXr4lukVApW7PWbsK0OAXp4PNRb9gy74no5mBkFjcgRCiUSE1h87KPK1f2EDutYprK9pdZJrK59uplha9FWyxNqUK1XrtXZmyaIbFwZ55Vvhg42Ijh1EN7X5SaO6MMnP7+Tl8c8AUppaOSQkDKPtO7NswMjlfTXOCBpFsPzTwVGfbq/1jmzvbutgsB2j7IzWv3i5Hq+SxViTLTqIb0pqSK++tuv8VuKPfcTBHQRGwEygyxcXVdaeiOAhDgRJeQrm9skHDFE84B5kr3I9sNYZ8YUT63ZgreWFhiPG75itBFcTaBneQ1V7Vbol0w9NL5NzGxlJodjjQQXMQ66KQ7mq2XGtjxslaCeX+uwKlN2004Ey3d7+UxGyEMT41lxOYGf8aix8Z4/qcaHDoEpbbtksMSyzkCrVtIBtm2596lKQ5RwLlZZg/IScss/fvAA7ZZNUOzJcVeNsWTHr3rmW3+B3OFTNrNNU6bT7h0Dhg9lWGo7Oq/qbzaNAlah83vogmWTMF+mtbOCIxlp5Deud/sXjG1sutGDnC4MkUDibHBltn1H3lE75u27J6AOLoNv5tXibqi2foxBuw9QnKkEOmYZezpWtmwjEl060Nl2e2tALszaJnd+8DkTLmBEC3hyYUTD3uB3BshQ6LG2fbHvZM5+zL5LdnDFpTKFL4jGxphf20LWgt4U1D+gxHeimZlsyhjPvtu5l2/CsEmViXnI0a9FeTQMdLCuwm5/xXiVIHuGhFvKukNZ3OU/2vpZ9YIS9WBia9RsQhv4+71BtHqPaZyGM2Od0ZNkY+cCcXuk6gH5EPGctmbKb1d34h7SpJcbl7bR+bPewTUAjx4hFkzafZwLR33+a73YBwHhOOySdWRV+W6BEm2LuybMxRSvhr/ujj24pnjXHqVZL3FtXu7fD2qx9CvN7vt+vHH8PIJtkEUEGjkrcIUFT4v3fAWQHrE2tJWhdFmreZH92RaoX32Gnbqw3JhKf+2SoydI7nO+VGaAYZPbHxZZSAGu56GkvZeA2jmK1numi0OnpdB0gatJUyqQAkS/t7Pp0+gHThSnAGsBaM/f4XrcbdC3cbguqRwCk50HPdUnxk1oggPUymQGOHFdB7akdfdFi8Mpu1MKZ+GkCJ/Kadc9EWgJmTYx6tcNmHgn0zUEiY/8K//aFDk6lEhTsd35IgJQAA4yZaqVNPxnjVdHWTAoCa6ysZKa/92k2SH2p25/z3Yg/fCIWV75gl2u09bQJCAuhS0aZfdTCfqx9ZruNkaaYbq++PkxrB8veyC7bas5bpBi2dQwKiSap45422+Y/VLO/oWJXhkwdTEtCMkgLppDR9vE92QBss6A5cIR0YoxTxl1xywUroNkOu/y9ACWvtr1Om42fW4D03m0dmtKIYGj7JIuJ2DcksFf/XcQdN0CbVirYkAmdJWk58Upf97N3x4W09mlkvdBOOsT3OR5DHCDnxOP2wOO44XYceDsm1tsbHlHB57jkQUvUPUwBkv3DdSUNJIGwEnkbQ2p6wHmW620DlLOfA7KNnUiHK0Llft/J36+zvZ03QUl6O8nW4vnTKbqQtWRmde8G1RV2sXLO+D41Mxi596WNiRGJAJlJJT6KeTUufISjDNLMOLwlyXNsd0GAyHu+CJfrkTjfRTecbcVXyrPv3+tV4w2+TnxK1lYNS52xZSX1pU2tVmiq2Z6mu22nYbsNMtVrcwxINds3pk/BWEFsJbXzPMlgm40z7/U78PgNgGRNut7TiU3iqUwUkD3MJ9kBf5mTtTsZfFKI1sRmCEFJ9/Ic53A1umm8TLPeC4GZRWNTraufR38+J9ijMaa+XFulZK713i3t9p6Ozha497HCbY9DnIFobL/QnR41ibwXsrgrGF6j4VRRwHzHQMBie0xgTrdbPo4D5+0Nb7cD53FgLcXbze2Q5zz2YhGZT07hNvZ76nQFvlcII5R9zXagGjDhZhXBPpIFtLAYno5zYzxdrF5bM3G86uimknJgUkg/sdYO0HW5rjZuzCTuUQGM4aYKFvOATWeQw/8yc0kYQ5nXqrnO/4bFXivVOW3S1O/69EqVut3nSxXSXj0vGDYCoTa12hgjSrDcGSSrkJsiIhzCvtjsjmtFvGwDSS+RV/257SXFW72aFOrtWmPX+7wyRo4bSU7d7HP/fHJ8n0HyGmxj3EHGJG13EcdLgCyITEktkpOjWEMxx5R7BEljS/r412QhYHqO6Ci1VLoc0mxzplCVTvF01svt7HaSbjf5bTdIzONYMPF9eoMJkjngOppNRVxCQkHbn09SZg+5MRwBpEOAMT2dj2XILG0/AMywpuKYhuPQLbB9DGRJNEQwNEfHthkQY9pwhou+S4zgCNn3fC9nQj9NQwHjWHIkmF7YjgYReU2Rer6DYrG2DUgbQFr0jTMQsqzGUpLhw/vGXAgrKanVKWUUCG4mJ+yHmaVte7+rONcG+hTUfg/c7XHLx31xjr3PLK+bJiZQbUaCSqnZ/h2N6ve5l42iyuJ1VXpF9EOwyFKxK9ONdu3rgmvDlmo1b/HVnRRp2tlkCbQAyH+LQe42yP12RMKIy4UuEXC6ManGEuL1VfV16hFCMH/7pBDsIMnf8efJQAxkPbxSOTdGTXCJEt0ETSGrQ6PjHeDb39FBsQqeXg3qu1O/HBTOnCVLZxVzbGXNMoxmla0HAaCoVKvuNVQ9YaZAqIJzOEBmQPdiSTLvs2Mq1lSoHZgaKuL0zKRh7nyQ2AWP1zRUdkfvI9kGWXC1/ZlJruEkO0REjn30E7pVpwEkVdF+bK9ydbHfm+2zsctyxJQDh5jPhSvifa0cwBBGBEc1r7XIauQDgr4IBcgSXcL+EB8zCuvr2orLkKo9fbbfcRNQaB2ba6P3zGfnKpW+e6ULLG2rOK6hAXSAJDskKObma2SNZJf5QM7nuHCasjYc7Ld2eUkRexU0eb68n4YT/xZAXhvBm6ANcsRfqtptPr08x6vnT53z8rhQuxffziUrlbo2W/XsWsQ7Y+UgzTF84MaItERJO6IEoHEBjymZ55wFfNsjN4CXUKvJXCBe2cbE93yH+M55UT3cYisAE0vLoldqtqjMwiyfmgQ7SC63HcCwJMpwhb7EbRK42NdxeNiPGuZUfxw+8V3tNt84qgkgrl2za693oVJT2NpUjjTa/Ps0Jzr13oSsvyHyetzzt9v3SqJ1wVW21qrL2G9hhIq2zIBwuozQMprtxftBNdusfB7l6bJMHQurmCYwUxAw/nWLV/3mQv70eIWPf7HINkrSgEuNDwdO7jy5CJDnwpmq9InVVGtWn08g7Q6akAb1shOi/3+Pv71pV+cO+Z70vg/gMWAfkVKscz5uE+KZbm/tiH9SwrbTE3wESNWwah5Gyt6Iytrt2gLBgkBUc0tNgUQ5+hafL4DMADmqnx0go2L2nCMdI6w7ObsDxHkloJEyNRqzQoFH9tMLW0wn0KVddMZpUU6K7FixlmDJwoDgBDKWUtWgh7OE4zBMNRzmTEHN69zWvsQc1PK+c0+TNkIpBHI0GwPpDAoiuZOgmqRAM37WsHIz1eSodMZ6aYO0170PCYg9FbQDMlk5qGVo2tXdMVMTMONac0LxZssW1s02ZM2899J+GjD29+IvnZV1d98D0RJsxb7TMUTzgzGERnCNlUxmSUapEX9rLtDP88QZ9sXz9EImZwNGTftjqdk7QPqTUu07o/0NWAqHdx/zqya32XW/0W9/AyDr5DnQIGCSIofDBq2Tn6TWCzFm+6CgTYi8Th/A/NSPkXNcchFkxoY0kAynjcU1dj+tQ7cHEiv6rhcQZ4wIBjkmkiGOWd7iMTtIFkBKGum7PQoXNaiET2/jtZ+uZhu7jEtfP2kvElZKDy/seQZAcuKH6mQIxjeS+U0ojMw5293VsNaD25gXSHL3Ha3uDNBwoKlMqMYCUefqtsI00FwEdzvz3pAOrm3x1Dwpppm/FQDimgRjHoeX6G5DJjnfnshuW+Qp1Mhs+4Jt391/2xxDto/xJh1fAcgmTJDmpx0UuU7bGjMgy7W1NnWAZAjPYnyj2ualvgaH9+IUq4f45H16K9hOtYriSBsiPjvkAoYXE1cTjNltr6bMJ8ffUrHrGpZqAjoYboPXfyP1m5BlddImPcNBsYFENELo8cY+Abrk4GTPvViiIGvtzzLbZT0kRkQyT9lT/cKQzNYLsggtnRdykD36+8ecUXTWq/gcR6uac0S5fRHQwFpgZBsYOvtDKyyqOysMlpKMkYIlQaov1YB/9corJwzc7sBUQo1WrHXgXIZjHTiW+SZcJpjqrFIVmMcRANmTOi2LkvZ1mm4JodzjArCsTThGFB+AO5O4YyML9rI8QjFJyT5M1bwthj6Vsm1PK6vORZs0N4TqC8tV44Fhnh0jalhexj42s+oMjm2q9MkEmw6EauAmXq+Bvd9DjbOPNcvXXjLUrzcYTFH6+0FxfQxGRkGMVgpNVbMYhMCTDWRbgNLMOIbzVK/er4rzjELS54lzRSHl8ywmeZ7pvV5r7c6ZAMhdTtBkZNWFjVXyPntMqN9mFaxWsLqS9507GA2prnzx+N/ZciGoeRl6ed/W15EDWwuUlf59SsJYZX2f4ifJHEeFoFhJ8ZCM2z7D4rUPc0/irI7jv1U1TBspGY+hrRCuFQEJZ4eEWi0TGEek6Y3h4TRHlTqbx/TvDxrii2GpWlVzjte2KpVvrZaNkIVoQ/rydRoh/cG+9Y2NBiBcWDlM0BXV0tWwlsewzaEYU3EcitvhNsjbWwDk4QzhuAmm0XZnUQexzmtsB+8JbFPYp2M+EAwltln1MmjqggZe9YfzaRREgrp2lWdDhSFtC8wboQksnYkg2GfFrpI95n49oZz4tgUGHdO3VYW3Z5ir2CmY+txM0PXnHFthJ6V6V9dJtTZALbeeDVA0RTjwPN6SZcb6+nlSJ7ZWsY1x18GAfcsKrfyiITBTj21ndIQ2kET1I8N5zmCNj6UOimsFQD4CJBceAZSP8GJzF8xsrZHsWGkcFP7Rhj2WsYSS/6YxeAq8MTDja2oKE9ZO/YSt/+b41q6GtQheXCD1unwj2H9NCN58QVP/PcD4vdINi3WKSGxQFO+VttUWSf3tFWTI4nKXPxlRZsuvMSQkmsDT/JQhHcgZzuDpMYsxjmPUcxakTZV7hH2yUgTZdSkRpXITMmiW2QfLKtl/VVDtU1WUYJR13wOQAR0RdWh0RMVzhAdckCEsaxjGqpi2eQAmvjdLiqfh6vawSBCxUl+smQhKZSv1zbHBQl5q5te6Yy82uIoBJzkdjJVsknHfCbBVP09BiRi3uFYWSwgBl+StmCO1D0YabKw3QBhkvTOEmWCzCfZVkiw3/6v5LV11zQBxfnZdDOy4mAdRbMXZUfv+pQ35dq4P2T6QgESDM0gNUBZFveZ6Ey9DW5y1pw+G8yUKpBAEywZ5bo4ZXSu82ppFY/Yokfac5MQYI9n+6xDDsbQ+92OblBzjYJEbWfsXAHJvlj39Sa8T2WB+tVgi+UA3FG/fS8YYkxwc16hlt/HpWoRdZeIknYOsILIc5kgnDesR9ksPgwf4Mrc2lyzSIePMMABylmOmHDaj2OUYoYoPaj0JkBqZQunXdcNO2maUZcQCAFOCBosky+yyhL2RzgeF139UrbHhcCjg0KdwZ32U8jKBHYIDAozTg6BlwESBpQCifJwJxmApuFo+IcoAkDVy9JpjgIxJABUvMlxWYPV4wo1FSN7bDo6jgrBTNS7mYcrEgmhBY9KcVwTEUVcAt+Ewbr01RuRbB+ObcW8EyH0pIFGvqX+dUddXLujfSAGJRfVBMDe4d31D07bYLzCT3+P8E4iPpSHZbMYXjGD0EhWvxoAw/C3WFdl4r8zjjhiq11SpV6rb57mwdDXnzM4IW2uRvLixRQ9na7bYhi1p9xXxedM2cGPpOWqhatYu868AJI/PTs6byFetHR0k+3uy/b7AEa2oLUUf69HVzwRSezsjPKAS4NQZpMxQsUeG+cye1YBiOgXo1VgZY2OPyRSnBEAWq3Qvt1TxmEG2HBCwImgn7JupClJ1Xgo9WdGHZaMQKp1nzPQFBJRw8GwVNyWYxHdHAawx6DknKB8OeCMKZywLBjmWAyQEkAmDYkJisycN9oa2EGtBrsvopjoZE2HbMC1YpIhgaSR9So81qKODIzN/NgapBBNnPs6WFYzbLI3D21eqNlKt5/UhBlseF6rBpVzz0AzD4uwheFAQWvZE6wdr+Hl5ZB+xNBRZFijovN1Vx5Pt7TP4+ejaFiJhguUBvTq4RAiCP3SI2x+V40tSw8wa799Fm+PpNsf74+HqdDDKx+NRXu0ASm5TwhjLnQru7a4g9dIKUvhtNxg1xcWjLLJD1CBDsr0SAtJwPcHvj+9v2mWv3kQxwGjcs2gFXrLHEHu9f2hj4QB6SuDYZhPZC0FxsE7ixfbo+5zM5qQhaJZKzuZnc5qU7gVvR9gYqT7LMdLGyA26IKg4SVjllCNsixBnbssS2AgeziJXq4iCVKNzUoH9FUwNvmhsuBNoxIqicdpUPQ1ODSoKYEFjxWWoRaglvomXAqIYc2FO9U3Fhlc5F6ePMDEMK5ApJtkhsrMCCp8aOzOvosN8T4nF50rdzoZyqkiNhVxCp0YYOi3MJZKUTQGTes8KGBMcgfSi9zUWycAecjVGtG0A4kH7NjiuIQDNausGo1C8rvz+17Cp2gQNzj+rcbfB+2oxm/5zAALmr+fVJOiEcC7y/Ta3KbSiDB87vmq17iPAudjBker043Hisc72WYT9qL9ezI0Ok5HfrT3zpEtX9X1vGn9pc4JzcNcwPFoifBno9sfX1/rs+F9QsS9AmEB5BchSwEj/Zfuc36k/tV8GYDWLYtCDQTaA7J7ImQDZ9mDZ7JIFqLWL4oX1hHEqbYyjAFLmwDhkCwRPgGw6FQPpzcgc/bMhmoHHG4/lLODktW6WbqCTAD5ycZbKR3bp6rGqBji613qIxg53mtfMywYTUmuv87NujepgiH7T0dI2XnkDseijD/y8LjI/WwDb7Gjjuy8I2UCApe1octm2I2gMjq3PitXteRktaStFqtkWdkn2lVfvaWo5gWub+/uzYo+cE23mGXkTHPisBNkVU/jTq+ZdY9Kf1pjsy9LPWKp/fadWsoV5pSUkNFWbgeKZPfNUwcfqt+3+fgdYee0OkP39NBMUDuyhYMg+u9piv3p8g0Gym+o5tudtRXEU8+8FFNEmSKpZJbMIiEWp+0mQUjG9mZQacX5W3v6MQeaeKFLqOU98BcgK7Rkbg5QpzYNdj643lWHZQWBBU4NaIwzgvId+e036c10wTEEcXZPlbmlUAJwxsdcs2aiqK4mmlSpGJncZkRJjUu9JBxnQGTFCUrXCsGiT1Pqg2dMqtrzGXwj2HG+O0R6W45EJPr9MtTQPFMMYra+yn1tz6j1/k/t2A5yL0cYhxRij/7hrn0XYjKHY3qv7IhDnhCdItbEwtABzsMK8tDGT+j16t36GAJIXtQTF/v1XTiek1OoVwDPn+hLz2NXpnmK4rgCpCbl/ySDt8vr5tkpqbPGP1ChiPL4LjDy+ZYP8HBzR6ccTOPJgG3M/6g3xuVAKiPfnSJ2oB4F2BtELySYgxiZRaYNM0GyAKgKgdtrjuZGq9UgGSdY4xoAcZYMMFCt9DayAHjabSFlcEMAEUxbm8A3mNQDdi67CHTARZjKyeO51INjthtwJ0fyq1ZM7QJ5LMWRWUO/j3CqpzHngmAdmPmbY+eLBij+TxSyqfqSwwnkyEcn2lQwtEHByXgJtEzR9ossOuk/CBM9z/5mlPR+dMRaLwgsmt5/PgAQ+CqlMs7VKR2QR3i70n9rbWGEXdiyn123M/VwFnLI19ztHDUkJcaDsjFdAzPCeVsbsTECsuMeNVSozZ8L2SGFC0Oqy/TOQvAD/FTTz0+iILDqMEoo93vK7x5cBsoKmkZTX77GpGsESrBmvmX+YNrPG2uhVTmbZJK5JA8hn2pmsqtulWMK/PNXFII8ETEkWuYd7VDu2c3cVm6E7ZJC0RSYwtgfKHmXmzHFxQQxEdo0lQNaeMwCG+bakPI8hsofYDZ2FOd/gq52zhGMnJvlxKh5juiq0FuaYldEARHD7gXlM3N7ecLu94XY7cNxuuN0OzKOVRJvcU5smhlFt43g2Wdm1CxJJgWAOtHGpCICuIbya19buk5khCXKWMiqnzZWwScxjwMJMGaFBoqXVWH2v/7bOK8EcCYqN2bfb9vl//XW8jrUkkfsPABYOEhsESoXILAAFAYGfo84tLekg3y1tzOOU+pCUczVTAJVFbxtjXI058vViAYoLs+wPsw1gewA4J8hujtg62G2HnEyc3RLaR6fPTZhQA1BrwuTfBsg+OAmQKIbAMIrubYo3QHovVI9QjM89T2QIBQJbId7428XvHt6BJxaZ+zDLwDFaJo1UmE+p2VXAIpktr5GLtgOkZH43GQ+Br9ofjodUvbyPzDxsxAFRC7BHOFoGwChX9vmwBooB6HnEQinm+xlAGtZYkDGx5oKeC3MsrOZQkyicOw+C4g3H7XCQPG4BkKwXOTdwTIbXAZLDb+1JzBf27xzYwVHkaWz3aV2snCpUro+mUnV1Onitq608X9kY/LSqNU7XNFaeK++FnxXsjli0DMUhaPo3bStzludqv7d2nRg55E6F9kIISJzZSHpLODBWuE5nxOL9e2SL1j3UWqXILizy6dHmVu5H099P8K1tXrWPUWvO0yFbd1S/SPzTilbDikF2GKw1UQz8u8fXAVK7m7zAsrPHZ3Wqfl/qLBnDvt0pkZ8zuDtlrL3PO39SsxtwsqgCnTQOkOW4eWWD7GwSKMDdvdge80iVW45QJ0dvuyX75STP4PghgClkAsdS6DBgwjNoImODbRnS+hLVRhA8UPfvf5NccwByHDoLeLDCylnFA9inQwbG4Wr17Y8b3t7ecARzvP3x1thj5LPnuI3Wlm6DbHOgqdc93GZEtxAknVE2wdSZZL8zQ4Y8QSMGLuapg6du139JRK2AyWqloZL6OuOUYpTY12cVr6CK50AwBQmShavS3iFLjTQMCx7aVUMFvMxTsSVrtCsXfwBH1ZlgdltdrgCp7KRkdLVbZVWEWrqaynxiz7FuJc3adsDMy+72RjM0sPQ+ZwD4FSAT4AytxmX0W2WK7NhCfi79s5aO+vfII4BvOmn06a1gj6ibr5lQUpiTk4wxN5EfA8dxlEo1xrbSa9uTHTyTaDaGsTk7pFLHyBjnGB7D11TsZLKjQKfAshhkfqfHOw5+jq1tFjEiEYYdExgZhyUGnCaYw4vU+gIAdFr00fBg3Ta5ad9jH13tcpXeVoKoZlnLfliKc1VerJ7OICnu3M7oAHjcDtze3lLlPt5umMcMs8iIHf9oXijA5kzc5nAySP4tdpdhWgTKZJT1WReICPBzvA2njLjtD30R+qp8TUPanOah6kxP0FMcaV8MMEIPZarjee3F76wKqJghIgcu/SIlQD3dugK10+5oYTqROn9nn2SSXBcdZMjEypanuW4Zl1iFJzTTB7dQnuaAKdtieKpT9Y6dCrsazXl3GZfMjrHWDzkLO/btyEazggVgbv1O+ZbgUP2RGPs3UPLbKvblzfiLlNavvpcsoAPUYDmwuTGStmp8UIsaPgNkPJHt48YG43E01kgHzegAKYwtYyAwQYcAifRkV1YNmSOqAeGg8UE2gFsMcHEF25pDoWNA00ljmHPGHfG72XuodLrI82655LtKGm3Ln/qYpO1neVmqyoldKeFNPPVMwgnjqnWA4py4vR3+WYxferDJjIHatqLPwy4wbXsjwIjyjzGt1hhlfdanXGotVIvFs3IkvpCxoyhVvFJlm6rFza5s9zhr448qTcQLEjpzTm4MNWyP4llJ/JjnHa0N1sanBCHvkW0iwtUfzpHnTr7QqgDFVMFzbRZ7ZGVvqtdXBnkN5ckNuBbjdMtmmX1uOyCyf/cH+6Vuvlkl886qXyj80NZGI2CCZqMvpn3FiPg2vnP882IVFzyUmor5mpNnKyDRQNJVV4KkJNhYABQ4eQlG4GTq0sXqWg30nEEKpsx8nup9AGQPGk/m2ACyMnQIjN4A4kMCJLsk29ayISS2Ah2+VULFYxp0Go45IWAKoNQ5xBnklHCKDLK43VaXLHIUY+eaMS6C5dfZF0EsUjHv/7AxHrcDx3FgBEAe8bdsrqPuWWr6vVRnlIu6FmnNC9tkoFdWapktQpBsp3UdzZf60FS7CD5cTLlWruaeLsRDuHcb+xbPkOMgsQhLndvOg9L+YOXZ5klFUPUl476pfYFrJsHW6j6kQK0v7lfdvLWl/ZNELW1/DazSIWMFiF3NDlV7/6xvvKVPDLED8TUDJj9pKnbHi3wmF/IvRPvqbB8Hy/HgDxMY27N/nUH+7iAA1nD787KnVdYDQ27m9AV4HEeFzYh7h31lvGCTvNj1+iE5NkaFZoNEeI2b6k0v9oiUtlesjHsil7rt1+ff64NTnyJiZqK8V06ZI6pKD/FwHm4TqgAOMmyFymgTwJnjHDMcQzPvi+rnlk0iqEIceYRkPy9exnOFFmrgnjMyHCTnHDhut8g/n1GliCDdBqPdN3CZ1DyiyEOnS5mHnuPm4Ojj6Yxv5Pt9AdQF1LyGm6vXMecIjmzMX6nY9vxc2SYw2NyFxxNBlq2T20Ksd+igoRqdgmK7lwBJI1g2FmnVuw22v7jUk54lawRauuDF6bJUI9h7NVU7KoWfUSH8fBUE3uIcX4Ilnl5zLrwCxwT3WNf8yeZsuRCkPga5aHGFy38RIEVeNEYqgZ3ZC5dCxJAeoD0ijCS8pXxwoyjmPPuKGKl/GVfQp7PDto+SQaIxyGaDnMy6QQsvSTD3rhy0McoOklXdxy/Wt3S1NoMNrvbVsHgGMyILY4in9tkw2ARswW2RIm6rImVOxkvmOKP4bnM0tXzkslHW+s3JOa224lSFHZVuaCkEJEq3Da//2ACznDKVndLH+lMsMo6RbLOfDLLGrWxtPjpV2OI6BfpFCTaCYmhkhnsm0mftI2P75HOylzzLNsP9nY1w9u/szGcLWX35xPDdmoWfHR16rkC1xTamKt0K3PYMmW1fmWZ/pK2xg19e59qG57ZtgHgRGXXs/V0ihrz7aots9vD+HP8yQLI6Rj9o39hodJtn9GrS1sgQEarVGYg8yY5KxbagDqHVJF68vkeazwvI02aHrm63IhYbuy2nB4OXS4VFOx+yPU9tyefWpBdyfEWihBcuXlsdbrMaI9eRL/TGZluQO3OQaZesKAB+d+Q99PaZGWzVPiluQ/LNkzYVLgCSe+xIAOOcblsg+GbRBGxQ8DnGVEtQU9UyaiBBML4siJzoDSAFZVtCZr3kouTvn1Tq120yTtgXbZXrF/Pm9mvyB3WN0B/kFZR+3j8X6lkCoc2//IzC8PL6r46N2cXrZH0Z0rNHPejSl7GNaQOmTbGNgV1Acu8ofuCddrV2vOqYa4CO5Cetyy5rUVAd9E9A8hv7Ys+XAOl/bZuTzwwytiAYRzHI9pf2xxHlq7j/h7mmWbYdv8OXDLIYSTE/Msi+3UIHyPSqvwBIXAESDpoGqqNUD3za98HqKoGDiRd3MBkevsPN5sVgw0kltx33DbkcIHmOkapt2W4LIGeq19x/h/cioxrkLHFA1oIOgarAVMqZwR7M7SSabTiuSbRKh4Zcx707QPokiD/dhoQGgigwSJVIemUd2z7roPl0bOBY7PTl8QliSfv7PNVycJu6eD0BJfqncPjchN+s22tIG4kHCJRSfVbYvSNPqe2vWGSPeSx7Y3fmXcuV7XGOBYhb91+Z98aMLWTNb/rxk+Nb/Fo+ffGl48sAeRy3lxfMDmezDelgSNDh5lVz4jY9+NjV7LBBMmyk6VMmbYOnzVLxV0fZIMkiIZWtwYDx9GqjgUtMNLLLFEJNkj8fnTJjG71af/5sBLvwdLTwBhs8NChiIE0MKuYMUtiHzUbbYkjLVhqskepxliHbm6kskKFe4UfXciFkVuDW4z6D0dMz/nTOaz/gc0gom2P1DV+3+HoHzXQ0WQMAqd+Jfwb8pjlfOHxspVQhLtr++atr2MUxsK1Ye7GCX/fKE8OU0nauKZeZltlAMed4nKzYJlLnz5XThEZ5nNHYIdMHdU8f3Go6NvtjY589XbXfnY9fhLmxyHL0deoQGyHfWfn2/NLV/e/1669P8veOLwPk7XZr160LdxbJRhVAIhc3w3luxy3zfB0gZ4KjM7Suou/QmEvwaQ424iz1zu7hDZW7pxw2G2R9n6yGE7F1QtPVdtXASjDQCEuKk9oZ/+VEH2G/9MK6GT+pYc/lrBCAtlJmrWzguN1fe54l12p8RgaaCKCSsXbW+5NqbHjvqdLudQcvi44A+0qFAPtgBzQu6kwPbYu+4u6vY9BMKajMGF7vuhza3b94r16UNVXyfjluz4Ixrhp0yShFrQO5pRZ5nb390pwmdWmpNslo83W3NacwfwGS1+tcgSdZnhk8s6tUa8Y1ZlphLzixqdr+m5dGZ2ljQwE/am54iZaoSxlzqdbyHgHztKj5lGBZ3bVrcL05WxOf1fW/Or7BIPlV2dr7bEOoGd0BcoRzwVXrVghhHlu6Xs88yHAHkOX0G30+CAiVv9kn0BVExvYevzPa7V0nXsODeG35bn+WIpG6k0h4I4sZscCtDc2Nobw8mWU/pgFaIiUz2xrggWIY230kuGxL31l53D9GeNgjSiYlOcEpzlPqbQ3MVXrDKs6QNQYv8xLIqZmoVGXGhPO+QqoyxHRIgmsXhPk3L/oSH7bv5usL89vWYatzGV1/EQRd3H3OmDdZIdd395YRV3OupmbQnIVPaZhj/83lRim0i50VMFKgbbGLl1zqnVmuJ/V6sz02GidWc8da/0qTBoLnMfkM4FovVUf1e93uud7yaeEdYE+ffP34MkC+vf3RWiwvG5SvEiBrMJmmdsy94EF6r6XKMFWhSz8xx8DXHgd4X1gGRMyY7J1PSZvMamdeI0pldZBEOzf6nVrdq7epgWM3OBNt+g5LvUkBBBrXNlHInBBhBWxOaq4c9ufY1N28//4cdS8jf0e2EhNmeNuMjL3Pzjhhqmv5USsPUMiIRMwYqM8cH3nvXDTWrmEj+yQL2QZAAMj7aL0foByvP6l2cF2E/T32GyD5l4CS7exHDS7M0LLKahVYIoK0Tr92R0wi7MAP4DJHS1D1Yh5klMi1FWfI+brHVPYW9tdmtX2rLouCtnvFnixftlZlYKXtsUhLiae6dxkxxiQGo61bCDJTXQSsnuEmau+4p9HcVbnss1dgmkQlO/8z6P3r4+sqdgDkVVJtL9poSXSWs4Dywh5zZjAyS2YV4FoGAAuKwic46sbTNqD0SirVvqeOy8m35xF7+9rz6+1dDoI0TQpV0ow/bFcW2UQjwZzfGgMAJgCPk/S6HZZpxHW2WhB5L62dT3+lDUVvi7h9T9WZiS2EDbItYs5zACwwUjffe7X9KNW1z77Xe/Ay0cUXS/D3zWTg9uG6l745mL8molmNPb62DPpYUJXneNH2iRCCu1Bo98V/4zOpH4GduK3Pl61gY0pAd3NQajrNDJUMn2fp0pyCI/usjxHZorWUQBaneFHbseXrXwPEvbrXZWwp+Or2E/D8PcEgAZI28XLDH2/fE0j2+3taoH3U9xyjXXn/e8e3VewdIFur2+oscCxQYmDzbKxxK1KBAhuJAGBX3LgAGJ5g22IubdZ21anBHduDnGxXdaWBT7+9xhxfLbpikZ84kayfq+Q70HN1Y5GnPbCxOrTr2g6S1xF/BZZc8ylXo7OykvmQCB4uJnI9YbKcBCM0dh+iixWBkupfAHK7EekN8/Nb2NZQ4D4CzHuQeBV6rYXwMrW1tzkpTtnB+vR1WJYmFGK+WIkmTTJCULQdPLd7HWxYXuPZHtnaKkwIoBAnYL/WegR4nq9tjD8TDiW7gskFg+w2yEofrL9PpcuSPVb3ppZmrX6Cofq09znbvAmOXYo4QWqv4/5SiF0na76O8cmur7D8Pv2+c3w9zOeYT4u9VlFTU3KAq8JLB8gNHEd9zwQhXbgxhiQwcc1FyF4Djbw8noyvvROlgUtK5xfg2DpaEmTRNLgGmNYXDGpS9OcX9rj3WTXRbYJhDwJaCf/4drPpfQZk/VbJL7nospFtcg1YBHv7NGoKa533ApJ0xLB4gm2gGCrTRkfZWdcGt37O1jJ7hvbHniPfBaE/yVAj6zzhs6Ma0K+eQMRXnLv5KwoPwxWHX4FjGiESveOKV3DMMeKwdFW5CUE04Hz5WWeReGojgLQVwup52h8T8HpZs6ZqbzUhG0lpGtS1X9lOA7biOzmPpcYbhpyD1AY61MnTBa5rur77+fE0cN86vhEHeaSEreMCLhvYjKfn6X3t1Xu2tkemidq2mJMEXO7veWHEUFwm1Gdg+PQ6zyd4Sgn6q2NbFO35hmx9oValGBXDHBLblFqqkl1K+wK43K3UvOmCtV7viwkwZ42kRMM3WO8T3lPjWse2v36GdlOdSqA/51c+6UCr2qC8ZgGlPxhMvxEGKVaQKWet+79z9Gt5/+zgyDmUhSzoUfrLC/UJwNe/ge9t3DgnL+P59duqVrQxvQLbyzqP8dnupGkbbV0KU/Q7jcZXg1OAsQix9IYlYdlft+fXufONDui9zbH7J8fXM2nm3CZPPgUZmb9iHB3BMx0fCZJt9C+dCkgEhnuwuCE6t/P2LioDHaoNkp3fbTgdyF9ee1uFV4S7HI1Z4rpEc3Ta5y/QhkBDWxFrCA7P1yzcEWKOpo2tw0Jv/ue31tRn85JaI9R5V7XieQBfgrMAuYfJ1i+82/qNv3EFyq3D2hFag7XceWPOsaWTZttxsLGtPZaBzIRvNF7XpQsXXKfHr9ZNjJ3w9zmWsdDksugas9/O97s1efkeseK36/jC2kQkx89ft1t9AkPdmOLLOMawSVbxicv2CA1oX94PG9ARnXaFb4sutH6/vvebLnrxSkSg/ZO/0ZRv5WLvAMnF3phYY5FXkCSQ8XOCILvw+gACKNEya7APRBmnu/os6Yi5tqVTkbxWp/Wvxhc5D1EA3kDU2vNtEDswIp9nmGReqL5GZfdF7794Lz6h5z7/8v0Cx3QetDYKEBW0Nc0JLvFLZa2qRICD5+u7q8Waq3QDyKuI6E4Qab/ddhe8XKOu1XrDWps6GH4KjNWm7HzDhk55PUEIi22A4v0CyU149P56JUOfLvJ8zcsKAEKrkA5USoZGM0yx9vQsb8yv2RYTFOv9DqBbtg1nY7/2020022B8oUggW2fbMGyhQXgxPJ/1V3XSp79NSI5//gYmbsfXAXIwfBob0BD0kAty4AkcGzBCKmSYA9z81q+BMp8XyNWkkrxule6PAFtpqnyC+vOMTYizAsnL3QeYcmAJ0L0l11PLvvg6uPAc/aeSkYTFCMwyqFazl+J028hfwG8DSX4k+RVikmAENnHy04vI15097mzAtkG6srZasP03JQ7Day3I3QO5L3W/scq6iNZYE16FjPkZX26fvwDJfjfS2U+Mv4Cwzn4tQMeQssOKtKD+fv59bDclYn/6LAnaWcwk5oB6qTVVQDyWVaTix/qmbRt7tBeq9GcPfredAzEefQTzQkl6/DupCQiiLmj1cuJhbx9e2zK3V58BY5Cr6w+tPWlircvqbx/fKHf2SiRKdtT1QQb39D1gq5Zd68wrlkd5wgRL7Szy2qKNMe6ZBj317qmd23WjHRxk7J3L10ZGsU1mAxiu8FKW8UblxWhVX6bIkHR9QAQejgPUpvfYgTELql6YyJWCbT0XC1wIkjJRLEVDgF1UVbQYv6ebKHFXrCH+WmM31eithBdi7558wJrKjdqnOq6UjDO63AzIbR+7ip9t+AQcrRw9XOQbA+xNRnhnfavCANVWeuvaK9dTyCfPf/MezSqmnkSguhoZEIBbBrfrZuiZGXIvIvMKTsu0KoK3ajwrw3wihmRjjs/tTI3EPFQn00GBpmW+Eqh7WuIOjkSALxyvOc6GJ/29v4GJ2/F1gJQXo/wSAAUspprsMr4bsn4b0ATHmMvK5+iSuZ27t2I09jj633q/M1cuV7aB60ryVmrhbh0uiRPxlzQjVFSq/8+dVud6RU3JiNhXDZqtgaI0hxGn0fZZsJxCkw5mfeikMMAIehZAKbsUJmhmWyUb/GSK8hn/G2CqBUNIqpH17xL4BHUbuW91gFffNItgTCBNNf+pDVZNqB7J+3CnVOt9o4DiXA6g77Q82/Nsn92hoU2wdunehicB1s8SYKWqkZrq410hPm08QXND3+ZgL4Cr+royz3Mtx97eum9eO803sAr4byy825F8GlgzwyCvQaCsnton1hO4vVpi7Xs7ONa42/WL3zi+zSCJD3nNZsPzr7Wp3xgbP2tzPDoOwRgtALKtN/R72meSg1oP12nB38Eed5C+gGRWobZ2Ruyrf6MuDRwbANGr+nrspH7/iplI9adAco7VUeBVXuZX06i3gABq+2K6mCaKGrfzJxvev8IqPLH+sguegCCbdAUm20+4dUATmbsulpXAUw3eJnsDYbu+9uddTawmeCMsRy6+InIpNsMFT2H4wiO6d3mdK/+Vp+9SIG6/vxy0OfJs1v+1ACq0XHQKxQCjAryWLki1+0XK4AaO/C8ZNcGQBMXyPYJh+iE4N6QESwF3AWI6BV+o2HnHO6g8P+9Ly1BaaV5zF1p/9/j6njR9gFtba4AkHwU5O7NKwONAxuscvJAsmkBZdspncNzVa9ZBZDkzDhoy1rK1w+p+2KbBdjUcvs7fbRPzVNHG9j3jBbbWYh+ltrCuoTgFSLKxyOfFFAuwU67PjgtToUij86aM5gJwp8C9mXnPT6fexLbt32ngSLbh7YhFRQA0pC3ySTJ2gO2s0Com8/fM8QrWDbqkYin7FqsASvDnvN8FvZ+HbS7nWsJvv+QFWJ+UsXxdK6dqD3hcsMacKPF8BdkAyP+vvavdkRwHgbDv/8q73I8OUHwlnUzudFpR0ijpJMbYxgXYme6jHK4r/haIHoEQ8WcTLLqjGeEVJIKjOo9MauCY8vuT5X1K1h7tKtb7nC98ZDUZlplPdl432fL2Ty64X8SojGwhOtxL5VQ3I0b6dJRuQAgR/dYBlmRc4sZjnaUptO5aa9RoESW+3qM6cBAnpIGMmGhtY4oB4CpcgejOntCIBxvfdmJlNazXY5y8BhnYw88xN4UJFEajVKne/0ip+dfhrP4cgRO0zQi1qJ0IqWlzaqMew7nE9UhNuUPkAqPYvmqUP+cJcpBz+9oMUc2IUOnYraaNRtS6W9vaQG587oCkpZJkvHxQMnsY4g7KiQdf39H1xRxBxn819F8a1Kgu+HOdU6gzpjusJEng9IGoVDboWIZGZNggbfrv4jkkyey/7+LBJk2nEITg6FpKcdy1dm+nnYULuB+52EgdDI9+8D3LsIBt33x9aGXrl2xyQ3SUmLDQIONVthZb9GHsFcn1tO/Eu6vUaimv2iKngmcAj4zsQ2Re/sy+1O75iAA8wm0mLBbCo45amLzabx9ZnlYz+as+EUl9rwKNBm/gjLDzSpJV/Zg+ozkgXcYgoJZX6wo74Lkx+ZycZ7JMakk2tkfSPXzNR0ScHMO38DiJGiE2xGjKpQDA1iBF02t9zokbdbOj6F+fWl9i6kMYeq9TwmeiSzNo8cqPdhXNcc2vG33yxuQ1R7wXSYrjOSxUExIlwaYMU6sDyu4ixUygbZyXvWgo1Jfqge3Ik1O8iiejm6sx0kxRErbD6vTo+5Exu3A4dr36UPaZsfy0rzqwjxFWAb0GV2QynBtwed/3fyI7wY2X4Q+qqpRyAguCtK1MvgjZaKYkBm35ct/6NipZxnt38Ov6kQkxKvPFifT5BJ2Nt+TY1ctc6oxRJUaO13g2VBP5P50ZsLvLoXffwSSz6T/9vPgCaGdd2PsjaDaVY8X6HJLetN539qdy/x3a+m/xBjkSPUqx5xSuL4Yu1wchY1K+hvzU2CKSdVSKuVcwdyB/eV50aXavpv+HqYgpmCehki5Kn219W0XwVezRkCFGEkUE8426LY8CeblwaqOuOdI0irUKO+ZQofWyqgsPAm8C+k+IYIPnaNcbET8R6Zqjj50EuZkyAyHqUdNoonKtyLjLjhaIEGRNJwI0UhVYx37RB9fg6ueDcOM9yHLSfvxcaha6DwPK/rCz75PK55rCjErTa9J9wFUyaKSrEwNvFp2l3g9qcVVPJ/NR6JUUm7D1kZj1W7TFvn6l0fkxJneorff7nFgYeeFSbGH4zpoSUeLgnkHH25Z1rp1paEAz9uX8DLMJ1UfLerB2jZRr/bxzQm2B8wvHC1PsYUlLQhFxvegy2XwFT8z5Byl2RUzfmmTuUc54owDyIrBOJKDppe4X0Io9r+s7TR7q23opOOXozHTBvdvHvTbgYwp8YYUhSpTr1oX8YJQ/udlBp7uzhRvzbe25jRhqWHwaIl/jHd+lpPlM2i2uNweWndj/Gyw/W4FfLBaLvxavRpCLxWLxN2EJcrFYLAYsQS4Wi8WAJcjFYrEYsAS5WCwWA5YgF4vFYsAS5GKxWAxYglwsFosBS5CLxWIx4B8AxQCDuGkvqAAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[6, 1, 5, 3, 8, 9, 7, 8, 5, 7, 0, 7, 0, 5, 0, 0, 4, 6, 9, 0, 9, 5, 6, 6,\n",
+            "         6, 2, 9, 0, 1, 7, 6, 7],\n",
+            "        [5, 9, 7, 2, 0, 1, 5, 1, 7, 1, 2, 5, 2, 7, 2, 2, 7, 3, 1, 2, 1, 3, 1, 2,\n",
+            "         2, 6, 1, 2, 9, 5, 2, 5],\n",
+            "        [4, 2, 3, 5, 2, 8, 4, 0, 2, 5, 8, 4, 1, 3, 8, 8, 5, 5, 8, 7, 5, 2, 2, 3,\n",
+            "         5, 4, 7, 8, 5, 1, 5, 3],\n",
+            "        [3, 5, 2, 6, 1, 2, 2, 9, 3, 2, 6, 2, 8, 4, 1, 6, 2, 2, 7, 4, 2, 6, 8, 5,\n",
+            "         3, 0, 8, 1, 2, 2, 3, 2],\n",
+            "        [8, 4, 6, 4, 7, 7, 3, 2, 4, 9, 1, 3, 9, 2, 9, 3, 8, 4, 0, 8, 7, 7, 0, 4,\n",
+            "         4, 7, 0, 9, 8, 9, 7, 4]], device='cuda:0')\n",
+            "tensor([5, 9, 1, 6, 2, 5, 5, 5, 8, 5, 9, 4, 6, 4, 3, 2, 0, 7, 6, 2, 2, 3, 9, 7,\n",
+            "        9, 2, 6, 7, 1, 3, 6, 6], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[20.8125, 19.9375, 22.9219, 22.7344, 21.8125, 28.2500, 22.4375, 22.1562,\n",
+            "         19.8281, 19.1250],\n",
+            "        [17.5938, 23.2031, 20.4688, 19.0312, 18.3281, 18.9844, 19.4375, 19.5312,\n",
+            "         20.0000, 27.1875],\n",
+            "        [20.1250, 25.9531, 20.7031, 20.2344, 20.1875, 20.0469, 20.3438, 19.2031,\n",
+            "         20.6094, 22.4688],\n",
+            "        [19.3281, 20.5156, 21.3438, 22.0938, 20.4062, 22.0000, 19.9688, 21.7031,\n",
+            "         20.4375, 19.4219],\n",
+            "        [19.0781, 19.0469, 27.4844, 19.5938, 20.6875, 20.8594, 22.2656, 19.7500,\n",
+            "         18.1875, 19.3281],\n",
+            "        [19.7188, 20.6562, 22.4219, 22.3594, 18.8906, 23.2344, 20.8125, 20.9531,\n",
+            "         20.7812, 20.7344],\n",
+            "        [19.2812, 18.7500, 20.9688, 20.6719, 21.4219, 26.5781, 19.1406, 22.3906,\n",
+            "         18.8750, 19.8125],\n",
+            "        [20.4062, 20.0781, 22.8906, 26.1250, 23.9844, 24.6875, 22.1719, 24.4219,\n",
+            "         22.0469, 19.6406],\n",
+            "        [20.6562, 20.7344, 21.7188, 19.1875, 19.9375, 20.3906, 19.1406, 19.7031,\n",
+            "         24.9062, 20.4219],\n",
+            "        [20.1562, 19.7031, 22.0625, 23.1250, 23.2188, 26.6250, 21.9219, 25.0938,\n",
+            "         20.2031, 19.6719],\n",
+            "        [18.1719, 23.0000, 18.9062, 17.0938, 17.9219, 17.9688, 17.0625, 17.9844,\n",
+            "         19.6406, 25.9531],\n",
+            "        [20.8594, 21.5312, 23.5625, 21.2344, 30.7812, 22.7500, 21.2031, 24.3125,\n",
+            "         19.2656, 20.2031],\n",
+            "        [20.8594, 21.4062, 22.7969, 21.2500, 21.1875, 21.6562, 24.4531, 21.2031,\n",
+            "         22.1719, 21.2656],\n",
+            "        [19.6406, 19.9688, 21.0469, 19.4219, 27.1094, 20.2344, 18.4844, 20.6250,\n",
+            "         19.0938, 18.5469],\n",
+            "        [20.2031, 20.6250, 21.2969, 28.0625, 20.6094, 23.8125, 21.7812, 21.1406,\n",
+            "         20.7031, 19.3906],\n",
+            "        [19.4688, 19.2031, 27.6562, 19.6875, 18.8125, 19.6719, 20.7344, 18.8438,\n",
+            "         17.9688, 18.6719],\n",
+            "        [26.2500, 17.7500, 22.0000, 17.5000, 17.5312, 17.6094, 16.8281, 17.7344,\n",
+            "         20.6719, 17.0938],\n",
+            "        [19.3438, 20.9844, 22.1562, 21.0781, 22.8906, 22.8125, 17.8594, 29.5938,\n",
+            "         19.4375, 20.1875],\n",
+            "        [19.5312, 22.1250, 21.8125, 21.4688, 21.5625, 22.7344, 27.1250, 21.1719,\n",
+            "         21.0000, 20.0938],\n",
+            "        [20.6250, 20.9844, 23.7812, 25.1406, 24.6719, 23.7969, 20.7031, 25.6719,\n",
+            "         20.5625, 20.5625],\n",
+            "        [22.1562, 21.6875, 28.1875, 22.3594, 24.8281, 23.0781, 24.2969, 22.4531,\n",
+            "         21.3750, 20.8125],\n",
+            "        [18.1094, 19.9375, 20.9375, 26.8438, 19.0938, 22.9531, 20.4375, 21.1562,\n",
+            "         19.1719, 18.6250],\n",
+            "        [19.8125, 22.6406, 19.8906, 19.3125, 18.7344, 19.2031, 17.4219, 20.3281,\n",
+            "         21.7812, 27.0469],\n",
+            "        [15.5234, 17.8281, 19.1406, 20.1094, 20.0781, 21.0625, 14.4688, 28.3281,\n",
+            "         17.1562, 17.5469],\n",
+            "        [17.7969, 22.1875, 19.2344, 17.6875, 18.6562, 19.0625, 18.6562, 18.8906,\n",
+            "         19.2344, 26.2344],\n",
+            "        [19.3281, 19.0781, 27.7188, 20.1250, 19.5938, 20.0781, 21.2188, 19.0625,\n",
+            "         18.3438, 17.8125],\n",
+            "        [18.0781, 21.1562, 21.0625, 19.9062, 19.2812, 21.5625, 22.8438, 20.1719,\n",
+            "         19.9688, 20.2812],\n",
+            "        [19.0938, 19.9688, 20.1406, 20.4531, 19.7969, 21.1250, 16.8594, 27.9688,\n",
+            "         18.2812, 18.4688],\n",
+            "        [19.7344, 26.9062, 21.1562, 19.1094, 19.4531, 21.5312, 21.0938, 20.0781,\n",
+            "         20.5469, 27.8906],\n",
+            "        [19.4219, 21.1875, 22.4219, 26.9219, 19.8281, 23.5781, 21.1719, 21.6094,\n",
+            "         20.4844, 20.1719],\n",
+            "        [19.5781, 20.8594, 23.7500, 22.4062, 21.3438, 22.7812, 27.6719, 21.5469,\n",
+            "         20.3594, 19.4688],\n",
+            "        [20.5000, 19.9531, 21.6406, 21.2969, 22.5781, 20.8125, 22.3750, 20.6250,\n",
+            "         19.8750, 18.8281]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[5],\n",
+            "        [9],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [5],\n",
+            "        [5],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [4],\n",
+            "        [6],\n",
+            "        [4],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [0],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [3],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [2],\n",
+            "        [6],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [6],\n",
+            "        [4]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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f9/vwnOc8B9/2bd+Gj/mYj2me+8Iv/MLm/G/+zb+JD//wD8dv/+2/HT/3cz/nv5e//OV4znOe4/tSf+d3fifOz8/xxV/8xY3o+yVf8iWXpu2HfuiH8N73vhdf8iVfgo/4iI9o7h2ypcFtpfF973vfQS4673rXu/Bbf+tvxSd90if5tSeeeMJVFBbs+1/yJV+ClGr3+fzP/3z8J//Jf+Ibff3AD/wAfv7nfx6f//mfj2mqgtrv//2//24fm2dRuBOxn0XhbW97G37Db/gNmKYJz3/+8/FxH/dxTScFgGmaFuLbe97zHvzyL/8yPvIjP3IY78/8zM8AqJtT9RtePfHEE5d2ahP3r7v518NI477w/ve/H5/8yZ+8uD7a6Gt0fbvd4sUvfvGlG31N04QXvvCF107nXXi44Q4gn0Xhkz7pk9yKvRZOTk4WoFlKwUd+5EfiqaeeGr7zxBNPHC2N1w3PhjTehQ+9cAeQHwLhJS95Cb7zO78Tn/IpnzI0Oliwzane8573NJtt/ezP/uylm1O95CUvAQD86I/+KF71qletPrcmbj+MNO4LTz755MEbfdn1+P3z83O8973v9bzHjb5sX3JAjGjve9/78LKXvezaab0LDy/c6SA/BMLnfM7nYJ5nfM3XfM3i3m63c/eUV73qVdhsNvizf/bPNhtr9ZtajcJv+S2/BS960Yvw9V//9Qt3lxjX2uZft5XGQ918Xvva1+Jf/at/he/7vu/zaz/7sz+7YLSvetWrsN1u8Q3f8A3N97/lW74Fv/zLv+wbfX3CJ3wCnve85+Ev/IW/gN1u58899dRTdzshPovCHYP8EAiveMUr8AVf8AX42q/9WvzwD/8wXv3qV2Oz2eA973kP/ubf/Jv4M3/mz+CzP/uz8cQTT+DLv/zL8bVf+7X4rM/6LLz2ta/FD/3QD+Ef/IN/gF/7a3/t3m+klPD2t78dr3vd6/Cbf/Nvxud93ufhoz/6o/Fv/+2/xY/92I/5joIvf/nLAQB/9I/+UbzmNa9Bzhm/7/f9vltL46FuPn/sj/0x/LW/9tfwGZ/xGXjTm97kbj5PPvkk/vW//tf+3BNPPIGv/MqvxFve8hZ8xmd8Bn7H7/gd+PEf/3F80zd9Ez7xEz8R//1//98DEJ3km9/8ZnzxF38xPv3TPx2f8zmfg/e97314xzvegZe85CV3e3E/W8Iza0S/C4cEc/P5/u///r3PveENb+DHH3989f6f//N/nl/+8pfz/fv3+cM+7MP4v/gv/gv+Y3/sj/FP/dRP+TPzPPNb3vIW/uiP/mi+f/8+f9qnfRr/6I/+KD/55JN73XwsfM/3fA//9t/+2/nDPuzD+PHHH+eXvexl/Gf/7J/1+7vdjr/4i7+Yn3jiCSaihcvPMdPIfLibDzPzv/7X/5pf8YpX8L179/hjPuZj+Gu+5mv4W77lWxo3Hwvf+I3fyL/xN/5G3mw2/PznP5+/8Au/cOHexMz8Dd/wDb5J2Sd90ifxP//n/5xf/vKX82d8xmcclKa78MyGuwVz78JdeIihlIInnngCv+t3/S78hb/wF57p5NyFS8KdDvIu3IVbCqenp+j5x1/9q38Vv/ALv3A31fBZEu4Y5F24C7cUvvu7vxtf+qVfit/ze34Pnve85+EHf/AH8S3f8i146Utfiv/r//q/rrwS0l14+OHOSHMX7sIthRe+8IX42I/9WHzDN3wDfuEXfgG/5tf8GvyBP/AH8Kf+1J+6A8dnSbhjkHfhLtyFu7AS7nSQd+Eu3IW7sBLuAPIu3IW7cBdWwh1A3oW7cBfuwko42Ejzuv/fCwEAhMEMAAISJbmnt4kIhLpidFw9ermatB2Tx2eBQQBYr+kzREiIcbXfRIi3TS6tHAMgqp/wSzGekPNuJezRrAgG28ElIcYzSJfns08u7TmncVw8OBykj8NlCg/0j3oeqT5rX0wk54kYCUAGkMDIxFaL/g+DUIhQIL+Zkh8zgMK1Hqw9mOacxdU8uNMUucgAuEgaWX40yGxf2qkv6BTaaNM+RzNhaBjn+DF7KpSvZMbzwkWO43W/x6W5Z2XAXDyutXvL9CTvfyklEMlP2nkKfbXtp7Xv1Rwzs7eLmF6E64cG7tqrRwWujZTlvH6nLN4dhf/zO75v/wMarsQgh+C45/qzO3ww5umZCRR+/Xn/k/vcgG0TDPyAOm76uzezN97V+LM4UHtwrJmch7v5+OD5cJvRgWPy0b+4PP7QCmv8aA2C1p5vfiSsIw1e7EFSrjGsxSkn8SeI+ytt4oyt3LwGP3TbwKMWYk2wXQhCDFN74xggeTBAfnCyxLtw1bCvFSwYIi2Pc7hmLwRCiASg6C1ma+oMcjHbGGSvwoii6kCAJQrvtulsE3/Xzp8Noa83r/Nwfoxw5yh+F44aRmJ0Ivll+4uKQ6ZKIj0xkIRfF3BkY5Pc6ncFSI0tip6L6hMd693HgWPq78JdkHAHkHfhKGGhY6QlOGYCpiQAGIlaAUCBEKpNxcGROm09Ve18AFf2dwxYh1B4xxA/6IIxx9uY8nIUEXu/RbU9P8zCt/Y07Tm7Qbhhp3kUJyM1urlbCtEw0hpYOnBMFSDtmNQrgFkAsijmFQaSWyZbdlk/VwHSQzDeGJO0J/vWF6X1qxl2DMWViS7k84cV9qQ56uWOChqjFjVuZZGnHzUNsdjDZ6UaSQZOIjAzSP/eNNyYQbrrDoQyxPOFa48cLGEvUo96tTskf7hX5t8wA8NjWrn+oRAuE0RHz8vfAI6o4DglYJOAiQhTZmwoiNgMzCDMBaCC2psKKZNkoBDUecP8ehrlfDyPziSWj9655ar5u7Vw6SjGKz9gmAMSJs4UdLhXBqj+G2NlxWU9MI5BRPqXr1/u/nVCtMa0d7vMHgMkr2TF3uvm098fPVodFlsfQ9AeQKLmT3//RtDVA2Lv6xi/0n3osqYSBbyHYeDiLkGNru6SdBzGC4ZfbeJYMEcCJgI2GdgqUG4zYZOqCF5YwHFOhPMZ4F21WDPUrY25dq6gbyS0eWTLM8F9KAHxbYxxep7jwLvWkbh5I9BZag1EiwI7oAQvBcfLbnUWJ14c+gUHrFG0drHpd5e1gni/llGDUQHM5DC0wC4ho2Q1qSFyNYwckCfBB0IdEWpe+cYrtx8uYpsT9ujeQvQ9bHSp7DLcofGzy8qqDXb5Nau8rnH7YQTCet8Ybn12LR/PVkY5AO2+jaPvYMuzCmF2rTJHAjt7jCBp4LidgI3eIxKA3M3ATv27ZwLUvRszS19IKnpTEK9FP2nisqZHz5mFwTLJk6VL/RJBGHxoR2pk/tA6OzxxeF6NNwB7ZGwrBLFlQpEl1bz35MrAqu8J7cAZ0seSiRYvB32oC9RFYYzRxF5eFviiXGiA3NwTobbTLtMRE3CkcCWAlEQsP94zx/Fsm5ahLeCuYYjtQTs7Zi2B/YXaYttXQwoiOHr+6nN9vhZ1GHzxrpa22w+Hs8C+8dm7A+S05ztwqSXKDUBmIhext5mwzcDJJAwyG0AWwgUBuQg4zAkoRbpwZgFJLtKBeACQFaxElGZIx2IwCidAwbiZR9OQv9FgephYtnf43Ff4/b2uPE2NYDNR6uwZHjTCGiUTxB0qgmZot+OctS2lxZeeJYbesZI/+Z58icJbDIo1MGpVi1NaPDRmT8YUk16r7eTmHe9KOsirgmOvf7xacnsgO+DRm4ZuauFdsFC71nAcA7x+k1bXBBWvE3AyAfcmAcd7G2Cbq6GmzMBFAs5nNc7MlRXOAHIR0YkVNMElMFZlXCQ0KSn7Ych0RQKLAahpo3XQFDCFiPCBOV0FJI8ZrvbVfU/Gmqn1xsOP9L2a98TdMs99XcTBkbAA6mEybhBM19h/6xgGoqMwyOY6teem20NgaBTPG0OOPrGg0strkenVNB6SkfBnBIjNnNPu1cBWjlG516/A44F3Lc1ejF5Cog9y/gA31yNICoNkZCIHyk0WJnkvA0nIHYpVLQMlsYjjpODIjMTyHSFPBTanOhknVIADKRAGfWOhWtnCKvVHlV+QMjbD2ZpzqofNQcuElmWJK1dPW94x9GL1ivytkbjKIbDARuRVejUmOvr3YG6gcsbiWQIRB4CK5/Vh5m4qKQ1y1T0w6vM1LRh/44Zd5ThuPt29umiFlLZ3noVOIYJbrBlq4+quHc1IE+Ky9LSLUDQPqqhzXHbRV+DeUXmsh7jyx6qye6V8V+KXK9rBuAUMB0f9GVCa76NZsg0oM0nnmgFwYnACduYOBAHXxCKuC4YJi4RqFQ0ka2CQXhEADIo0IgVLskx3ubJneU/V2nPUlN+inDqCcEg17W9NPaMLA1mIm5WuWR1VUbnTKXIomz6BXkaXtcP955YWAWp2kXv0Uh1rV+be7/lO8z3U/uETB44gBV6ZQa7eX2OQV0jMot3GuJ8RkfeZ+OZDCNpxqO/My4c0VEZFgCjUvTMZI2EHxgiU0UizIWCbGZtEmJJ0iAL5hxOw02cNJBPgDLJYB+ICYkZSwZmIxVDBwkaZkvhUyk39yeo0hZIo/okq7Lge2cAxdGQ+4Hi1fB9uIEO/8PGmmlB1dYO3laDU8xunR/+JQN2EK4tOcVTa9+5xVSPHn0kT6miNm1wlfJBC1OHhOLpmDwZzh4CjwV9/JKfS8s2yHOs6DX4ZQAYjg7DRvwnCIHdgvV9/8V0LzAzSXzIx2/wi9UEuAKXkiTEVTskZpADJVLnnwmxgDCss02V/4/JhCyZm4PqMNlgFj1VXlzRwota2EFRLVdu0X894UIocJFufxOvLYaMOsUf1cMNwfICkttNJhwp6RrRsVFeNbCNo/h27Fx2/KA4JV6/WVZ3tYFRdju51VD9YvN7boqP4JI1/LApKuQ+xOWKAiTYBPEkZg19jIDG5uJxYjCmJWazLDDlmNAx0+HOALEhcqh4SYtGmROAkbDPmA5SAlMCUhUGmZG+1GfOjCoKmn4SuvyhTfxhMoVM68ySA7PmxWHx8BI11Gj7nI1YClzCAJRowSWtnKbSN0EcbwKzfW7dk134iOsgle+3LqO1ZfR+j5vJ4EqnJBITm4RuGm8+kCQfNgrm1Z9fKWdU5dkAZS76+PP4uAK+MtUSO2HkdYoGQ7n2h6nSq4+v4WyElNAK+Nr722gAkF/cuS2wfZ/3rAw91LLIDyCo4r8VvBhPIfGl7iwzIFCwRjhUoKYChAWMa6DETiVaR9ZwhcSdjkWzu4FzrxsrJ69kAMgtAJvFSr6K2LoARJ4ODK0jaorVIIC6qBxXPSmKbt8PetlhTwasWuL4BXi+0dRaAxvVwdlwALQ4i0jmdbUz1D3kcVeRWkhJE8B48+zYr2Q7AzWNfyAZI64sxEr8n/W2RXckbo7L78NZNLdjALS9WYQ3+2g8ehluHh4YSWWsOLRt2flmQd9YB5HrhOOC4Enfksp0Y1cTZn3epIQNHqBkaxZ18BSz1WQZSsR/L31nOaWbBLO25icPzqM2BNH11AQoF1MJIpSBBmKR9PLGuSu4VkwBKoJyR0oSSJwHICJI9g7YBjm0mzwwwo5QCLgVMkikxGoke1Fb3Fju6GUWUSh+jl14aAhjpqRUBIYmhBKoiEKfQTtwNZIWWANkUU/N3beDvpvhZHQZjzdWmAXLbdRffjeBovqMHRn1JuBWAjB1R/vRZW+nkfT+No9cgtAT9kBBGJZuwitiYDhvfhaCQM6j2Zvf2Xva4/NhlINnkuWerdr3r87AOEEjBctuL8JweL0RsFiQjTqLsc9it/0bRGsygIsdkK1LMmhRjOAVyrZCu3FPbiAAkyzYIirymdyRmpDJXPaTWCVECJTUYEaFQll+ekKYJnLKwyWTzHQd6NhWlWYERpaBwEZCcZ3CZBRRTQilF013ABWBnlxqVx9lX6aClLRhSfyEyhlo7pB7ztc8IOPt00yT5ocC0hgMuhTjCcX2UmvMlSI4G9KWLDxABekQz4lIj8R17vsRHfUCq6uIKljcNRwfIpizDwVKnse5gEq/WaLoCRijaqwwXTb1YNYRGEEXuPWhJOEzEXh9oY8dcllHzZIikKZVh5Ms4Yl2QXxgAZMMeyUYCz1a1/bLeFiZJpKBl/0UR2yTSwgqSqCvialv3604ELG0aJwUdmgKogyTXOTIuarHUKVseUgJyBudJwDGLI6btuxLVQBYRQxhiKkVBcgZREQcjIgdJACp2A0g6hqB4uxx1/2UF2Xet3NG16QVyogXE2nbG+yPF3iZW/2WXoTA+tgAp8cakLPvyMFuEoIOscUQ3nHUWGRlhHAgwfKe/thS5rxeOA5C9ftAqC4PuG+4hHDXPev+ntbJvWV/EiyuxydDwmkgGrEwS37w+dBgeNJihY+6gcffHHiH1ebssj0t0j7oiX5gj/I2AH6CucaqGGUnCHDAqBZQSqOjUPxASVSYJKEgqo8QMr2wmIaJc5wh6aZGm09KdQrtJCoyJUQGSCUjs3IIpgVMCpQxKGSmLiI2cAQVKJELdnCqUnLoVif5xxrybQSWhlFlY6UzgROAiyF4wG7kGFdFXwjfIGnfSFvJifZp+O1Sj/h2DETX12j4jNVksDQSdsjlyoO76WsOsY29dSnTDuIb32jbZW9SXw0DLBCPo9Ri5BM2VDcquGG4EkJWNWMFFAGwrLv6tlRE7fHsc+eUqs0JsYDcdLQ4Rrq8RRmmn8e1hNheN9Crpix0n1AtUFCUSkIhATSYo2/uxs0kHs85vlmMqqtYrRXWICrEKqCZaC7oBPLPMGzYkUDFb7QmI9S+aAfLCyZqXDBJ3IBPJqb6XKKEggdIEzhPKtEHZbJGnjbDISQCSVMymlEwDITlVEOHCKGVGyjPKvEMpM+bdDintUEpGKeKkREkYZZmlHEqZgUTgWVFtqApZq63K+MTYY68u4QPoXXK6tqGnyUCI6zurzI2w0hCX1xf66yYXy1ftkw3PcPGbLykmbt5p7gzZ43HClTft6vlQ239pcWNE/Re6jWVsiysjEduf4SoA3magxYkxrkveGbaZfrSP16DxVta0iGg0O6FJZQeOxhTjeUoBFAETNw3iQDY9T2N0tiY7xxCKMEhE9x3SqYasonFdT0ewVa2qJF2CoSzSqCrDreKJJC4kkvspgYoAIKWExARK8k3p2AmcMjhlUJZfyRmUJ6Q8gScFx7wBsgFkuhQgS9qhpIR53gkjIwKVGVS0rMrO2XcpoiflApQEsFral52fFkfL2oyCcZR40LSdhXTQxdy42BBaeXcU1m5RY+pbpKOPQO5Vxids0a6t5PogrtM/cHs9/2gi9kiM/FAJl+V8XDaxsQ+HnAiPyxhWkXc8ypMd92BpLEoeBijYkVXMcqA2Rmgcx+NRCzQVAS0GKBVQYoiVRtPEok/iIsyoKJPhQp0OEiH+Pt0JRMXTnkoAiFQBUvSMAo481R/yBpgmUJ5cD0kpMuwKkKUUpEKYvaxIiDBBjEqzgLrM4Jk9AybSCtumRsd9SFeOGNG2jQosUfzdyyKXr3ZQuycRi2tWRlfp63367Vp/vD5MXD08AwwyisdrDPIwmLxu4R4x3EK0+6IcQlwDcPsTdNBTKyArtwxslCk5MFRwINLZJ8qqPA4zZABVB8lqmi7CIFNJoJKQWYwaGQkJM1IqoKRgy0UZ5Cw2GmVss0RTdZH6ZdJBlxKQOIEThBVymEVDAsRisU7AtAGmDXizATZblM0WvLHzDXiz9WdMxPYfahtnmFuP6CDTxQ4lZ8zzDokS5l1GKTvMaQeipOK3AZV0Tt9nx6SboEvc14ErXFRR+CDjHe0DyL7tDb7P3ckeFlkdEm8axADXztUeAeozF67IIFfAcfkYot5o+UaQZ+y5qJ/0396vhGhpqZhYS+WV6vagsXZ/uJEOMpb3QSU/+HxgSERIBgiU6rGL2p1lV+RbmNgtTt8MoIDmCpBUZqQyg0pB5oLMMzJmAUkqAO10NR1gLkXWeBRTBmYG5sKY1TDCutWC9dFMhJLUZ7Ew0pTFCJQSEs+ug6REoM0WsN/JCdK9E2B7D3xyDzi5J9enCZg2oDQFgCRn0dJVWdakZBGx57xDmWfMZUa5OMc8TZjnHfK8wy7vUHYJZU6YK5UDZgLzTtQRplow40j0B8SSS1l7NgB0kGxWw63toBLsMTiKFMDxyhJ7ImhqXY1tlWt9+pAQWWIogxBFa+2WZ6Q4Ykk9vHDF1XyGXKgCot+kioENAsR3R8DRPtvE199oBhr2Bj4Kw3VC+sa0t55H2qGDTpZlEO7E/hSuDr+0LHlaSbOVvXWeDhwpssfKolKqAEmUgoXXRHCzTLN03tQCpICkHGdOSCAQEkQole5ZmGWLBXUzKWCx3zAJk2StSpuZo99NZhTOWVciJ6Si8TMcMDFtBCS3J6DtCXhzAmxPgO1WfpMAJE0bUJ58YKCcmpIXFx9G4QIuGSkllHlGmmcRt3c7pJwwz1I2hQhzUvYZpiSKn6RMazRfRVafTf8WA3WBNgU5fcY2l6nAuWxf1n4MTGnQFisLDQC1IGpr/WGAkiMAuDQsh4IW+CrY26pZQXBpOFB9xs5pcT5mpFcPNxaxYxqoqaC+S+977xJ+1D+weDY2sbVn7GAPyJDFQbVWwN5QESpo9Rt70rj+1mBgGD29bxDtRWwFSWdHBpQBJFMDkiTHCp7Q+6aXFOswVK+mIKmyMc0zqOxkdkvJSLzT+dcF5stjrjMzEmbV8SnWCjgCzhzNUCPuQjLJkHQGCBEhpaJzu20FIQE5moQ90nYL2gZw3GwFOKctyAGyDg5Q1YP1QjEclaqHTAlzFoBMiRQwBTgJVUeJwuofaaBU1LDEACUHxyhv922DUddrZCvvNb1iV/VLK/a4Dx6kB6X15/hSEduAeRxvvTcARyAw5RbF3drNbV4X87y5f/f64QoMsv47uDGCvwPiO05oCfv1vkRdZSze21Pme8GxGbmbGwel67L4h09TYI4w3aMAYEoJSX0DKWWkKSuDzAqa1bqbcmCbQQQlmTIiDJILMM8griwyzTvkIr9U1ChT1NunzLhgMeagCBzsGNhB14aEwQYBSMgUVt+h5G5EbkrSfKaUQdsTkAHkyX3Q/fty7eQe6N49kAIkckbKGx0UIksGjO1yFLHnScTrUjDvMuZZ3H7m3YSUMnZZyko6b9hhxvwpbYxV1h34ItbXoNR56EROKPc1ipF6xv7tl8gldB4fzbuRiR0ChtcLxvrsG76oLiD1gbrVRKunDCy4ycDtiN9XYJBpz80lQEYFMprD1mJY6WFgU+G1dVwK42K7guneZ7sENvevJqS34gy1t7r07UlbiGQfx+wveJNoVAtkqFHBUUXlxufPxOppckBMKSPl7IwyUQJNWf4aAJGAk01bI9Wrocwy08QAsuyQ5wvQnMXSOyvYANhBkNKmHzIzZhZHIFvPURFe5mqnyhNNHWrAKFoBTWPOSMoUabNBuncf6d49AcjtPaTtCWijorW6/YAqc47lKqK+zrfmgt08IytAlilh3u0wzxlzzjBdbSJStyDZcoyZQXNGSkX2+gZQ5rlhOu7grqDs+smuPcvlFhioxgATV9daj09FDDGsw15kYj3w9iB0nTnPPcBFQ41eGTZ6zak7l4e73cygVUv+NcLRFswdgeTwodGxY+MYJIHB+NAQvp7fjrh9vDNIxzDph8Bze7Yopn3lRvXb+2Eylm08ilFZXKTxRjecaMUO+sdGF5l1UQcDSAPMJP6IKYehTGfN2BYIJQNlBypZ9JGz+UIqV+EdmCYwMXSJCZhFnFX/OJMsgSv+jiGtlCsLNyu8AihlQiIVlfNUwXGzRdpu5a9f2yCZaK0g6cw45aYeiorINt2QKKGwzLueCaEsSRmi/PI8I+8mEcvnIg7lKSGx6DOJEmxdm2prYd0ONawCzsZlw1hLqy3ugKbWtpibwIek0QBpxOauElrm514ECx1kBUXTP0Y9o01nvI1wPIA85Pkel1bw8sphjWb155cB0XVBsot3yZ77zI4/ehmDXBwv4u3AMcW/0Wqdw99JxG5d8cZAMSujtHeyM0j9OUAW0bGVpEabWRlpAMiy03UaC1AyZp4VVIQ7zSAUBoobhaqO1NZylI8rYzOVwaQAqmyYNicChJsN0skJ8nZbz6dJ9I+6aEWje7V4tRRFH2oMkkEpobDMnKFEoF0CpRm0sxkqkpdpLpinnTDNXJDyhDQX3bJWBxVbLi1gg010sNBOnV2v76tq2q7LrJZszVyQrpqKDuS7vXJbNjrWQdZnenH7dhDyVpc7uwsPKRggm+oiMEa3TJuxptM1pqTiaRZwTHlyPWWe7Fh/1LJXE7dRClAmEM8CkrsLpF0CpQuZQ20+kAB4LpiTrghEs4jeJJZgTgTQFFxwsovC5p9JWRklJXH5MfY7TcoYhSmm7Yn8NhsFym0FRhWxKVEwWtUOZsokVuaXchbQ44K0E/VDSrMAOGC6DpQyI++2KCpqz/OEPNn8yap5FGf5uSNQl7CwAbjV11smNn796gBiIFjB8KaMcfVLTbwjJmlgXO+1zx9zemEMt8Yg78JDCiZ9+nmwWMNAsuohbQaKz6LxVW2qf+TCR7IBSAPhAJBJFpOlkkQfCdUPkjKnshN9pYqsonrbASWpKKuZSAQkY3cCfuwAafdCukxcNn3qtAVtxEqdpklBf6oDAbV5qYNHC5C2Bg8Dss+0MfJuPUMAKHlGmQpKYeRpQp4mmZ5YZuR5Ums4gGwdnnUdBR1YyCy3QdRs6pfav8NwOYO6Tv8d6SCPC5Q1nnWrdM8y1x3nbyMcFSAjqu+P7NKYmge5OefwdxTVwwby4+gJDn/T1fKDjmSoJDHWOdgGknbfwGGgo0wUgDLcayzZVD8jcrIAJBUBMxCSGhqIGTTvgEkNOln9BGcVmXVdSUsXpUnnSVe9qDNIm2NNCchJ1QEKqFMWcMwbF6UpVzBFl1f0+aag8xKNoLQybXqFCWAS0TuLzjIzywybnJFzRs4Tsp8bQM8qhheUlEX3SrolLemam8HyvbdtdH1QesLh4uVVgKV12K5fk3iOsSBtD4DrILmug1w+e+xwVBF7oXsD0ADmpVhSqzy+S/4vo7s1gMm25ladRi+VSlyd7t1lkeShUYm6v/vD4fDKXWnU8zhsSKizYdCAQXAU73WVSfOgyFenIFIFRj1O+m6K2WZWFlmALFsUOjPzOYQhTXkCz3Ob+SQ6xZQySkrIeWrXbkzZF5lwBunuSrIQhakKmmXNfLMu0pKLg4Ulza7ZJTGo2KIdwoQJNEWhlpHLhEkX1Z13E/K0wTQX8FQwzRtROQCYYc7xcEO1/C2VQaO3xvYHz1yISWh1kH0Yd6olgHUkaAgTtVXzXh3k6IPrt64SHiEdZGSGl+XuKkrZmyhwO3rvZ3S0CjhGaKydgY1FYDRfP/+b9Nc/i5q1iGe96J5SZV0G0b7aTykCXrudMD9A/u5sHcbsrkHuNK2gzFRFYl+VJyVhmw6KwijNLQlqxU65ruJD06SLVwgwkmoDwLYkP/tiEp5pJ99Wy+J+lFICs7JjzsjZ8iwidp4mMDOmecY879wX0qYrWrmZn18ByYJIUF8/vXdVUnbpGH/LoWWYN4+LA2YS6so/MshWkKzffDboIDWh68mjwVGHL3vBpqGK9eHGVyx+/QrMbfHogGn2AGuNIjBF6t/pk7RSOFKkg7QvP7dg1c5o+4wYaAXREc05OlGbsPhgYFbjdLdWcl/MloWfIZUgxkKco5UhOjjPYtCpMgCpe08WUdT8E02kplSXKFM1QAOQqeojSVfysbwxdBcdtThz+MFA3eQ3Z9aS1wRbejUhQWbGyN4uDOaEnDN4LuAkxpycJ5RcUKaCPE8o8+zfzFnVDMwoKckqRv7Ntp4J49a42jJJ2+SRcGLJENfhuIUGOggwWx1jZaWqoG4yWueim1zX3789kn0FBrmSgs4J9fIa2tf5Wv1ivdo3C/ujzCW2kg5wogdF+9l9zHKNOdbPNqDY1M6+OPvQDR5XIaY0YI6oDK+ywrQERCNNDqYVJ80M06ejfoIc5OoMnWC8AVQfqUBIEBE7vktixXVHc0BYHpHoGW0+eDbdoxmSssybNoBMlU2aLyRMrA8zZKrRRcFJV0EvLC7ozj60p7mqyLZ88CrUvDKBWJ3rk8yiSaXqIMskvpA5TyiTzg9iRslFmCjLCkhMCSCdWUMR3Gz1x7YWxoP6sg1dK4Q+RBixwsORdwxW/cUW/hu8pD16xgCSlTPdRErcHx6yiH1IJm4iPt9eQbWosYZkl3x7JXnXTrGniaq4DKqg4vOMzbWnXawChEbUNrbZsMiKDvWThAqMBk5kC1Tos+ryY0yOcgbPG/D5uSwYUea6wg0sUgG06v9IAorqjgOYiB2+G6cKOkOt/pQ2uJp4TZDtGkopOvuljPcWCiGRLvmbZP1Jtn1rwcIgdSplzjPytK2O5upIn3Que3UqB2guoDSDWUASscM/Y6EDZKrsrFqvbzmV+rEqNgfjWW+4AQWwfIZF7PWwZG17C5G6kfEgdFiLT5nB8PaAXR6Fi1P9G5jb+sg+1D4PL12asuZhqiOsA5y6sQRjTHKjhoii5tMY518vrdnKMkn4U7T6OjMNcST1TSRKFV85C0imJOtVpATMkhbeTVX/aLsjEhSozGczqU7S2GP9prFXA1JyXWrYzjWuaWk1oewDqn8supiGs0iqLdNYpHW8RCqqG4Br3bqPaM6YdBk05kkY41zAG1tNnZGLDBrMyi7nHahEF6OrAdBV4eqmvpDxq3KtgtVlKV1+epAWi4fqPO142TPcwMfg4hHDEdx8rsHiejzpHz84r4eLybdTiFeCt8NjDFFxe8fFiTUXnsZP0H3/glN4c325JuTCer3318aX7LqlV7ckcBE7GIW45LC/TRhsghGJmvwZOIZ0pgqera4Awh47JV6rKqmKm337szgnDsaWvgwEJAUsszqVp1L0eEIuukOibjdrqoGq/kht2q+Aeg1u3Zb0NPiqAOdISzp4el/a4v64pGCn4NsbYkQ9Ge67uq3GTYuDm4WHb8W+Nhh+KIVQ4f1IEhSG1T8xgJSv1BMXoEjueF2dsCOTrCwoeTwtYNq5LfeV7Fpwwq4iPAT4ZB8GMNTvL4jDNo+7cZB25gcHe4QBIK5TWVfiaZltM6BwwJp97Gmt73Yj+QgkU0oy3zqxz0TKthLQLAtcgAtKEV9Jzln2844DU6kDi+sj96HkLWqSrhMuI6b771P9EzwaKoOMLJW6AU7vjQS4I4Vn1s3nEarkRzcoH1PRUdpMZE9x2qDOrc4Z4k+YdOpgdqtvmupUQlIHZwfMXIEzJZ2P7QyJ5FkSlpRy7p6fKnARAN36lIsAXkoENsNEEgdx09FZnjy/vTjv6oQkzt8NWybUTmYgo/OFAZ0uaNFWcbZRHxxSC1H3pQMFkMBqrOEMpHkWEFRDTJkmOYYB5k7WiywFc7oQ9yEt39kGABQHg1WQfET6zUIE3vPMleNGb8aJWnAX9K8X+RXCw9dBhlHAstpuSUH945eEqINsizV6/EdFrgzQVxh2HmKDbEqSWtFzObNFwCbOq66AmJVBTg6A2Xf4s3nOLcjljkHmIDr34JlSf546gBR3DQaBc9F1c3WPQ2WQtvhuK4Gp8SiqDWL+G31rAFNjHoDPTDHDTKoFCgL5Em6NzvXAUIHSWJ8udaZsMifdOCybiJ2Ri61MnpFTQsm1zEoi3amRUBJULbEmuu73hHgYIU68iHrIcYh9cXlrkRuR2/2YVMQ2I1pc8Uh8SsOMpxvkaV94+DpIovausyI7jydXzXinZYqK5EDJte822ud9U7Zikg5ujqNxY5ji0BmqhBFSZEARRM1Ov5hsmTKqAOm/aQmQLn5PWVxU7L4bbqiyTCJf7iyKhQtwNJGckkjJgk6gBBS25cRUNQCAOFWDSVNGyY+TGVoUKJPqJqPI7UwzlrG77QSwDGUZgVaSZd/o62nZJsy66p20H7Q6dUVOCZwT0pyQs4LjXMsrpSTbyDrIazutI3vbXrhtVP1aqLUsrwecFZ84YpXHPJ72txYbrRwjtPPu44Gpe8dr1DD1MavTRS2NwPea4aGK2BQaclOdXQnHRYyHo0MsgNHN8LYMQtG5VKGy9V+Qa9GfqvOt2lPVy2CJ7vJxWWD9LGtaqwEiGlGMNeqqMsYQjeHlrQNmspVrDACnSdd81E48GZtUkDRgDPpE0z3m0KErg4wrlMc9b7ScWdxfErIwSCb3d/TZLE2dhVGS4A7orR6yZY0R6BZVUHyyYL3e6A/1fQywMTiQNEkEN43S4/KBRAAxI8tWszyDoQxyykg7Y/YComXeuR6TiqykbQgQ1iZvk9C2mJA3YNjSelLSx9IYQ+waheN4v18JfE/El+hRrw5iUvCxe60pI47Fq59BHeQYHO3O0Sizj0qxE95Ay/3QJBoFAKrAZGsfUtAjpiQOymZ8ydMmLFk26ZJl4mCd9bqD5DS50YUC6FLo7Ma6TA9ZrbZLQ09c9EGMkgJ0hVim/JWCogtaCLNTbheqhoLqg4w92hlVo5Exy4ZBwmrX0CssThsGvGh0qjs+tqXfvRKuVmJDpJoaggwQOSFzgi7dg5ITchGXH0wFu6ygWWbMOWNWMOVSAZs5ic9lKaDj9oRxuEH0USJeeeJmH1iN08KzRgcJ9CxgbTCLo1nfuEdhBGWHK2fbt6PUsipij6ji2t/+uWHQRtIX0+C0ge6GJVVwtOl1xhJzMsPLpAA5BTG7AqQAYhDH7dyALxhkCFTBUPVssq1AzyKDSBnAKiUd51lYmMxl1lpTfR3bFL8OIMFV5dJoqykCWcsko/qHvS57cbQFyOq3udRBMhjE5C4lfYjG98g/q9gvM3VyyuAszuAiYme1eJsYLtftnDnJdhSqSqiW28AgY54OEaMvYY/W5C/fVrU24N5gtR5qmS+AlLDYnpvjN9D3DXmhtV+N0nxc0LwVBrlWZE0Dt3O50Ty30GoOhikKB4fM/Ww6DowdrFfu4g6115ci2crzjPbJ0abt+kIER4/E9XpBrLXFbZ1BbmAuPXJv4yuE5ymIwsGKTS7mKWsMRhnBHt3OQDuYAaQBocRZgTOlCBIB7CmJFVkrKblOkL021oqE3LxCWhR1QG3E7Vg5YW4pNRFXFJbXUpPePvTLna2yoSiyU0KhAlvcgoM3ALsKpHOpCoMMM6EgyTpCRRN1mUV7UWY1XU0a97wjPEF7yEB31ZZPXRgiGmz2BYP5/mJPdPw7PMAA4xnU6yD1fPDNY4QjAqTkIAJXczcgygHj3uLdfuA8BBSPFdaI45UjWL3YNmbpuNZ5ImOU5bQoV2aYs4rVASCzMkrbQqFaoHNljdnE7taH0S28QGPtBSGAY2WZBpKVPbVZYQCJxPWFSabbkTPKUIldhVIzBAUQM0CkAJrxLQLa7tEekz8XWKklth+oeXFpESeFV5ngAwWQddZMBjMjlYwpZxSdqy2DVUYuWZ4pWctj1jIi2ZfbtoO8FXH1toNhQueporfaAei6sd9uOOKCuZ3CGFgAg4sjFEaUS3SQXsSBDXDc+e22SmiY9uGtg+Kpo2N9m+MDIDSsKBhJbGVsAbwJado4M8x5agDSF2oNTDOHfWZoimBZjTBubAnpSEG0NablIKlgaKJ41Q3GKlUncTDAMg0RbpwJ8Bh7SRwIu1InQyO75+xvjT/ENtm1z2bEvkQM7RM2CL3zOJhdnBbXnxRW+5llWmKeRAQvRZZNY1sZUueKqygvfp2lCtsPkx0M89qc7XkyrKm6oIQmYbTnB30fY9XUbYRnaLGKKibpQfNU36yHko1fri4Xz4rQUew4pFRWE2fHBEDL2QEvxz1kTNxuHLiDWJ2jI3k0sOTO8FJ1ctGRup4rEIRlzkb+hP2YZ+/VFVlU92jhKgAZr3f6xytVQ6PGOPStw0CSQWFKZCtGU3Cr8vorpa48Hr5TdJ1hJAZKEPuXXz40A3fhiuGh6iDlZsfF1mTyJvTsNHg1Bp3E4q1HFDRdfIx5Dzqxqpsyw4wywWmqjHGqjDGnCpJ5kk2vDACzG1PMONMaWa4LkCmcp5Rc3I2SqjHkhLowrC0M0bjfXBEg/V7QR7YR1OF1L+OKLLILprMev8qDI4uylltKsl1snMqZk4jZVpclF2GQkyzeQZ5PAmgW53ouysJb/ZuVxFBcG6WYF1fqWXjA3Uj3dqDLO9fVx65RnHyNe8fr+A9RB9npeIhW67QXsQ0Em3cXS3wM4qGodXoUgzJGBci4T3NWQ0vWbUqnzVbBcYu82YqIrQxkMudvnWLo0wfJWEoVgeOMmOju4n/DdLxWB2kbcUUAqOcyAaYHMVTZyqqJlU0GM02sz2BiaeKp7SoaZQYMUju6gXOda+E31+uiC71U6NcZ7se5jIYAjmVWVz+39SFLYeR5h2kjG5xV5i1+kWVOAGSlHy4z5nmWhXtLkYV6nVoOrBPXDscFx4PCatfl8Ou/t+/eEdOm4WgA2euCRoNa28jbcwCeN+4v9Q6p7kjbxTsKoZWvc4V496BYsd59+nglrqZktHAWInUwzLj4NU2YdLe8PG2QpwnTZtPoF3OeghgXrKNmfDHrdL9SjwGyLhDRu86kzkGbCLLmo7PFeGx5akuByFbkhncIdqZklVxpUVTm96XbiPEWeVO2cR8hhcmorwat1orVyXp3q0qyxsC06mFh9VpqHSczohXkeYO88ZKBbScwJ0LRgZ1mQpltRXTz6bTdFq39hQEolkScBhNnKw3COhbynmcuUzf07wzAYA+jvSw9Dysc2UhzUEwH3KLucicORKX6uqmx3jpklKUDjiXWcGOhLdVGHF7tiTIF8bABpNSCXNQn+g55kzNKW73an4kr+uTkTMtBMfWApscIorMDUH3Hj13niPreAiAHVUG0XvzNoLcs60MAcikfhIiYdcq9GTdC5ax+pQ911G5mtth5QIFG7FXAQ1O3OYDkjMm2Y0AHaABy1DqwrF1JgDiQeyFbXuMQ0SdfycQlYMSDshm9M3IDGgdjxZc81n5xEHd/bd+9ev1YxPrIIvb6aXux/7v6gjS1rpQb9zZa5eld1MYAetZwwKt7k7ufcS46eWCOkVnUVXmirlF8GvO0FRF7mjBNW9k5bwpGmpRaNkhwRgrzVQyO3hXk0IIf4GJ0Sh3ABtGaUveuZXRJZFpOZ0TR8OuS8qbuoSqKVtCtqhthi9wwF3IgM2H7sNEypC8AQgsiPaOMOeCaTmPqtn9NmQAGysTgwuinkaa0g/lo2uhemJG4uLdPSpCV0BcDw+KPJfAABtnnr42XV8thGapBDs10RFpP7ihVaMq6aQg2MEWj1e2wy4dmxb7aSNK8qX8HogxVEXzvfYtnZWbEZZ8e4eLl0L4nQhj4GIjpcmE+BTAHkXqDabPBtNlimjbImw02m606fFcfR+lLBAqr4QCovoqKiHXN2pYBSnnVawAW7DOhxuNxeCH1q/LUnDpIejV19eUANyCSzsADW/XX2o+JNE0gkiXO6Ojdp4rVxvgqboyYqQAgCMoeGTnbewqiZjzTwc3mxGsG4fpaFakLzeCZFSUB31J3KbNeLWfDV5dgeRk49nGaqH39/j9KUweYtyh6344VeyhuBetc+DM4WZwvxKgAfKNOaaEuCGINm5qFMGJ81B/vIcSXgeT+tqC5SYFdxCl7qldM3S+bY7GJ2DYfO08V1CJgAd7BGtZILcg1YBfqLS1E89SwRn8PtFLfNegSDCFIZ+8fj+A4ahEustr9Rny3fazr2ci/uj2NbXFQ4Y2Ya3+rWL0w1CyEiVbnm8wfkjNyVgapuZF4DfRlewaLP82zT9ckdbTX6UmoOxl2mW2tmuCDQCSoCxaIuRRnL9tqYamW6yhk5D49vg/xfg9rPfjJq4Vb0EEaGxnGsvK3PV62144pLK5cnqZo1xxGS2upuVpYwV//bgtStqlW8tVd6vTB+Av6R3Pt0f2fnXF4X4+7vQH9njMVEFsG2KQLEVTbd+M9ec/A+YByu6TijG2M4mnAOZwvS75+hExiiCIf0CyIsQ6Syy7WYGEHji18tOmqOmZZdVyek83DrCBtYV2QxJ12MxIX+eUMKlmmaRaGLazrhWZfXhWQrgaODyUQIPPuLc81Kw3eXzN51+2/fXhmVxQ/KBya1eWQY7oQubu++MBDCxFsELYsaBy9q2hdfxOmzeQW7BwYpOmvIj5ISdSFBNb2mZEk9efttboIBYJjOOq7QAXOlcJ1Udf/DBiNotdoYI1gaN8cDdiWAt+Ay8GnrmE4nLh7xBBbYT/gpJRdxcAAJhBm0xVbnjSd02aW6ZnM4uYzz+DESBmAsWMuurc2VtVMd+Fm4faMNEeLZ9QRRidLelK9RxQk10Ts4RfWhL3rh9ZC3LrhLMXqSedZm7GmWq2zA2r1Z3TVnq+BWJlRyx6pAer9YOlIuA6knrnwzS5Yeoz78dqzIx1kON/PICsw+nTG8GyzZehaBd0wNCoCZ61W57K9K6WkS3DErkeYbGsG6AZfuwkpC4vM06y7JSpLZJmGWIoOKBy3jT1EN3f1Nh3zExer6MXstu0c8B2tLx/AwsB+FX3nbYUbi9hrvrIHV85CtDnkvL+8JrsF9gjADAmLco+iZgBH6v72x6vJWbvrQGWMwhTzdaZLXbGnrvhSfRurj2Ndu9FW30EFyT5RjTjcMsAIoDVt8OcqY+tYXDwP3728S9DioUaS8nho8UzPIhu6yUDwuFRSFcAUAUDX0rV67fJOGg1CSzAJOmIimYYIIHFSwAOQs0w5TCUsfpyRSm501JxkYV0m1rqX+KtfJuBTOXtDUlQ9rOYZnocYYn56Zr72LplkQcv4/H6nAgFsKbzw7RBnu5Pi6Lj7xg0JzhUY5PhDtGf5rvVr7V8rmAM+N4xyfwqCFZXjkmLdk6SFGTso1QIeAedl6VLu0IBSBcewGEUARGeNOaz4Mppj7e47KXyPgUQoMnFXP1dBxXSVZnyxmTAg6MrcWhcNGAbLt8U3ErMtDYO69O7LaMp0UVd0TYDUeJtl1VABstoAgoawUdRaND3gmlpgDJS9qqDpxD0LBxwYUYouaJG1dABAtopNXJBmreuSkErWlZkykGQJNU5ZgYKRQDodsQMJDnnwfCpgsqWr3mvxqwejMcCthfhsP7DGUH3dl25IBor1ER1oPNVB19wTpJthYhOeMR3kCBPDnT3nt5ISVJ5Rr1F3vi+G1VSHmw3zIqoAOFVRutE5DgwzvrGWTxcEYutmwFYUrGnpxeaFqNz/ugbtfxFAsIJr88yocFaGFOoPOIJfvVk/oWmvGfP3xOiRUNTKLPtQk7j7FOtcBHPM9p62r2rjM43u1Dpq9K9cedVBOsweEkdGpLqdmIjNuSCXgqJ66KI7IFr9GymUlX4yCkPVFaayiGK2I2Qt3Cv2pXWp8XIR247XwFFA2RbADQOdPxu25LDbpj+uf249XEHEXr3jR/u99XuEbwXX1WeH5338lxWX9ehuZnYjaURQjCMqtZU+TH/40uJiRZUqysYtCoKhxnWMwcfRVwE3UbzuS21xuXht3dYJTG3IUbyObHAJiOGZeC/8LJ/OFqn+sZtrY5/tVFcf7drFGkBS9zcWtgJkUTcYZyRFlw9LCexbGKC+ExNp8ZqkShTcZuvuNJHD2OMmbTQLyWo+DYzJpBfVRYLF+YlYgDspMyy5+L7ZKWfQXNsFO/DrGpMs8ZRetdBjoyfWEWhwsw6Cq7pkrvuCx/NR6IGyvybtVPtZp/po9cVSRpK0OjyZYgHAnokkVxsQRuEobj613EPD6awhC9GrAaXRvXqypl24fAwJ1GQRMbWfdNCrMEBNGtfhfO+nPX5rgAEsE9W51w6SweWnEanTcHmxlnEFujNIhAOgZT8AXATPmvcAjNRdCwzUEde+GEB1X7EsAM/OqYmsBV5axOCoQCiefyL2KamW3rjcWvX0CR0ztAnbDdG7pumve9110PNVYIQbolxS988EJkdS/4kBTrIFrA+EOYHmsAJ5yiip1BWeSkKx7R20XJYzfUJi+/7XnTatew9IHhragW+sVGnxcgUkOW7XHPPYgm33cfvCjfIAHMlI4wN5rI+BbnIPxg4+6P8MGrL9GwFwFPkAKKi/Eke6WKi0+O4awNv5EBYCwjRGEuscyXSMVaSu4nYrVqcgVptOsdZLP5GuS1vzC6JP3TgxPFeBt2GKzbm2CaoDSi26UXdAWDS1E5vjK/Z9xA/pO56OCmTVdYeQmGTFG0Cn9xUFqMp8xtbsUHMGcHrOCqg27DTvRl2j0NiFt02tnVrvfl/TKLpJMb60i47UnwyiBZwZmSfxh2QGlSIDbQfCl/hwL0NThbFPtMeXOYf374SLY7jaYzjrGWqQsNHqILtPxYHuhiB5izrIrqV0QOPwswAaa/m0zPlauIp6pXm2B7/YS/v7yzdGb8aH5HrNq7PHNDDOmC4yT42+sS5rJo7hZuDx6YPNJ9Ug1TVkasqbOrBu9UTRoLRgkW0JjctmwB79PW0SFJ41zPD3Up/WWKABNFEvcZiA7WXi+sy4h7Xe4ha4Fplo9GLsoCa3qqANP68v+uuhLiQaU4LEdp2QElBQQBxm2kSJokxI04ysC1uAGaVk5FwEXHMCF12qDoT5so6wuL0OIfss1EcPNKhDWLnV0o59a4zVfcu7WTiCFbvTvRi+8+idvnFfApL6zqVj1noNY9AF0Bdi7NgV1uyxcYffVx6j24IBHTAFfWKz091AvDbGaMeeigHY9yOvW6pHgNily/pvZZI1X40+sqGSUNCthTAESG3rDdA231DwRqsbdZYa6sMFyQb4uMmb6BEtb+0ouvAXdyBD0/viM1EHWWExQmWtEzfgeN/o49AzLXffGzu6cXXHJbiDcZrl2VIXKWEVtw3f18herbZavtTdlDq4JTBs0rL8RtN+w25dLgHA8lg1xB7fyvF1wxGnGg7Q/pEIS6Tqu29sHI0OcvT+ocGKwn+14RmLrEAZGGVczzHqJ+PzA/a3Kl6HXPYAF0GuitXkjdHKaswyG+QM8Vg5xm+j0cMtx5AKiP13mhEnDAZ1KIZbO22ANjcWzwti56of7oU0J45d0z1GS+5ZkQ9yId/JQN3ax6JdFHDSXRMp+a6JZaVNLFIf26E381opLWA+JID0ZK7oIKnuw0M64FUpqYvD46Q2PzcIRxGxq25nfdR6JMJVwDE2nmt+a/nx2mHRsMY61TDOnslZtkyILML8HhuGpZ+rOrJOxN4DrPvOLwNHB7AOHEfZN+AZlWg3hgw6OjUvjkxQ0TxFWs+JxHWcGOoDGjubSqxdikzwua1ZqdKsgi6UGLITYmWQvmjJnMG5YPb5+TrbphRwngEu4DSLj2QRXSYXZZM2t1v5FneDYUBILy87ORQcD9VJ7i2LGtlCxHaruT4yclJfuBkdERyBoxhpqggRnVX3FR4dLfmHhI5XkaWg3vN/aeX6IvSVsvhUuBAapv2cNdS51C1YtoCYA3sEAoAAzahrPn59XY1AbQyWcYXxwHJRZ/1YHLIPjb6XCLB7mqbFZveNhXV96KHutyhc7y3W/du6SHqFwUAipCL5KLFDscxljkuWVQNA65vn6UcF4KuGyEpF7K96zVh/Lk3khLSrkkWeMsqsbj7MPpCCCzJncE5gFkOPrfqTPNlhywlr+w0z1xvtI02a1sI+N59Lg6ZtAZLAHtDtvzX6PoU83BxnbswgKxBKK+gRfvyS/EN9418Lo/JajZouia4FwdoyuvMmnrZrjIq9B1P/yiDaHqB6XVNjsVbfx3bBiWUSOZzXmRKWtQ4UPW0tS4zxrzHJNaBdsMgg/kSr47jxa/thoJq669QzSW6IheObYfooVSATgBCR1cZtWzqRiWTOOqm4pmDLNVIV48JFy5KTgKbkF8GfMmIUAKiCpEXMXflbeyDfOjZONUwpCSiWhKIqGE4JKLacWkEBSVq5S1Rkh/1AGgbz0f1FHtdIkx/sGU56JF4825cvaVlGArYS7aUE5/BwFAZJVBvNPsrdD1prGYlk45B17Ci2vsvSHSi78RQa3uvfpbYHLQYuGrwTv6LPOLBUthZ3FYxzr1vXnqqnGjMsuENt06t9JB2BXWCI1O5oKCuKB6BM/f1uvxorPwfI6mpi+zo322eF6WXcVJ+wz1bKaBBR37c8t3FZi0kKjAKCpH6NwsCMecq5PGczcKr3RNVlGltthfuInONW2kOp5bXfI5q1XgtkWmchLNtDVjGaM7hksIrfqcwy9TDNImKzAKdO1pEVrOLn9gJgqMeVZ5q8rEqIa/HHl/2fQcxyT4re0jIWv1e/b/1xPQUHhVtz8/GR85EK+8Gxo3lXiPXwr9t/IDSK+OzLnel50EsKMNbtEtbS4MXt4pudGmsIABfE5QV7sWcNBJMulhus6c58/RupaZhGv4oxJWNxAOK/lRy2SvpaEzUjzi0juIaOZk7bBBbdI5GyQ9XBJXIxlZNsZWAzcBLVzbEkYvsmh8JtSvkIQeLz6koEYlmEpG7byyipYMoZszh9ipvPlCW1nIGSAc6qOpiRcxYshQA/WT6ggKOF10HnIG2oD94UaWLYN5qgNgNpOlGJ0vtFLiNq2uARwhFFbDuXv2P/T2qe6S4fJYxFbGsU8fsVBNtCNe4yoqPsTyy/MDrvADcwJQM7nx2je8YkCowxVTCrUmxDtypQxGsL3VYAwsDNZDcZqn8DC24W2gUaq3t81so26YdqeoLjOqNhk1UnGdnjEnwio7QexAyf5eLKQyuBoEd0SCX5SAQJG709v4CzwCWPOUKg7jCQBy9FZ/e2mruy4IWInf2vWLRzcP8RC3fRed7s+ao5u6zlDttxA6jh3h4gqnW/Kge3Xwt+V62LIHtTNhvHQXrPI4Hk0Yw0dQjYP8o2nfwIIQ7uQ+AKzHAJkuHcL1J/VQPX3ryWv6UOof51YiVI1zO3ZICouqcUmFqr+xt8oj+l7tsBamwAiCI3DEA7kHQg7MWsACyR4VHs9Vx1ey2IhZW4O5AAQ/0W43WNn2VRhqjf4gFANsLuwsI5FpRtD6iuttu00bivx9jrW3v2btHHLG8cr2sbbgY0qgY8Dga9aPEmyqC0qyBadF+iBJlNNNAILRM1OtsPngfFOMINXhwMYo9tSQ4qUHLTl8cJeMgAuS9EHeQhfNwy14PUwd/rziIe9AxyxFbHX1sbYZcvjp6paViLgerPgJiswQtjEwZJ6I00sfPEdFL3ORc8e2AYAHLDFhFZZdRTAj04O2hFjOOoXQzpMXEWslVA3KbAle0d2x21nwqUqCCpFhezSjfAGeJvQIjRgLO/69e46ZcxNZ5MbmDaCrgpk1Ayy79rY6t9x/uGDpilDqBMBNY1ItOcdP62WL25yHJoRSWQAgYKyYpBYZmzWCaex0WaAkTeFGjWGOT+4hikRes/NPFFs1m80x9fPTwLtly4aWgLqx0dA7NqRiTCTQvWv9eBkbO15het1VXv1zDNwO4O/bZ985A0jJ7tQ1xuCgBmrqJsxaMWAA2szDjSg4vNdW7Lv62J9oX6DRQGSp1v3XzHgM+NMxHMGyzUdPXEZk/XbUalQ4TwlWf2fqJrI8YYubTnqVq82bwe2JydoKsYGaBYPVHDxq0K5M6jHlZRcfzIDcOzHCAH9HDPc/tF7BYkl3xyfchbSLQenwJy1OXFv0YBja3ZO+GZLrF7JYelWFPjgn+zF6PD9UGcrZ8rKluLgMilAlcAxvi3HtcZEsv0hvKxX7jXErQAiCyGFlkOTP7OXMShunuuxmGrzIcyJupAEuFp+afOfAu81v14urxzhN1RZ16qG1aDqzqWg6a3YLJ0UdOWlvaAjqWH00cfIGOg4aGcHycnDx0gl+xkJSOBqVwSYxfNOL742T4JAULq+Qgw9mgPHHxNRDJROrI/aq97o7ZvR2AMnWGY6D2hDgT13QYYYydrmGXHjOy0Y1tgFj9CB6XZz3sGWQFf40/iwuIDB1eXngYU43uoS9N62pjV7UVZYikopYALo5SCucwoc0HRja9KMVcjeNk62ATmXu8h/NXDoB9drApkKKTU1KHR/xkDpMV18IwUiu3ELlk9qjSiN5nrgCNgHNPvQ8SiWdPgaBQOTPHwJfZG1apAHrXwEFfzieBoFbUCZvYmYTzlK75L7WHTpg2sAni2zLHq3uqftUYR8zNC7x51Izuzv3WRXAPEAAvDr65icveKwccIRyvw1M7kvGOvSG0pgHZ6ASAuws7KLKBU5hllN/v1WAZ1kd/ss0KQWfwow0ydHrRjGqXepIM7ANlfBcZ5lnQIMM642M3YXez02g5z4Qa0AfJZS7IpWgb5Jmi6ERo1sFwL02Cbl24nDjnReHQgOC7YbkO/uYmKY82GAUWyV/esYVQRe9xmeYWM3DKX3AOOVxowbjkcDJCHJ9gqaZ1ujYCr+RbgsyWGq57F95qhz0Ta5bfGxzHKNi4K/3qqiAIjCONsIBzOlBDAKAU3GkWqCgDWwGveTVptkmjo1wF8D5KLHBgriokMaVsLPsOErRkXF2HLXLDbXaDsZsy7GbuLC8y7HcpsYq0t4ir+nL59xJTBmDBh40xxYSyi6mvZs3jzf3ToKQGgZwHEeZ6x213g/OwC5+cX2M07zPMOF7udgDvggG3z3aftBpvNBmmSNOY0gW3ZNR3grCF6zStbjP2CQa6CYISO3oClN5TWeb1RAyyB0sR1uxdjq1VOy19YuNoIrpzUtBCjc9ZvIl1tI7RIRJcuJ4lj7Ogxu8eYfuA4LIyJynXDjRnkVZF+naEtHtz7vFn74rNHHfUWwDwcZutdA74Aeq3oE0Z6VGYZP9LqruCdyzvkUFm0bNmhS7SY6oOSwX8dBqgp6Som2jmrTq8oc9xdXAggXexwcX6O3fkF5nkWMZcLbHbQlDM28xabTQHzBCIgpwxO5Hlr9GmhDKNeNIqEnkRu2ew8C1hfXFzg/OwMZ2fnuNhdyO9iJ88CoJQw6YIg0zTBpspOsDFIPArkalAV7Gm9ZsdvSR87sLUDa1UVxDqvlv4Bm0R/zeCyq/RmtDYdZLsmZj/4rovY+4NJee2F7gleOXWQbyjxCtO1ewdgzZGJ71EZZAvW+0aengn18RC6cluMBDQ4WtDHGwXqvsnekmjRFXoWCW+cDpRhLnVFrYqQjajBbSOuHbUfHXFJObZZadx1qE2P67M6cDTHbgFHYWrzPONcQfHiXMDo/OxcWORulg6ZSNjZNOFeqcwyJULJk6xgk+oCF0So0yhNHdHkRkuhB45ZGG2ZZ8wXFwKQ5+c4PT3F6YNTXFxc4Oz8HBcXFwrejJQTps0W02aDzXYjeU1Sp/ZtkOhK2a7BSCR5uVg75WKieK1LSV9xlmk6Nwoi7RAcTYXRgKTqUTuWuTpeW/uy9urM0fK4SuqWbacLEb9i+1w+tOCTTZr93oBBGkiuYc5BWDRK0zWg4dlhxV6lyQuad4Nv+D+Hs9xlBK1Y1unT4iZdHbo5KPLiggz73HzlkLTQ8KyK+fV82Zjq6O7MkQvm3Yyy2wnonJ7i7PQM52dnOH1wirMHD4RR7oSpZd18bHuywbzbYb53D6XcU1Y5IacETqV+jw0orcxGeQrMqxSwAeNuViZ7gbOzM5ydnuLpX/0AHjz9AGfnZzhVNjnPMwCZqbI9OZHfvROY+5IBu81kYhAoxwKsbN11voAAqm2124Gfz/GOcMFw1DIwBSogFgfF4qDOpYJtfLa1zK+1gXaoDRW99621WK8mM66F/bFEDDyKTvKaOPHwrdgB1PbpIK1B1keWOYybLDUx9deGAEvNn/oJCsfLV0bbgDv7WijLrbPbsmYVkIhSeLYFLek0CKBYG/YaULqerMtLZZGRJRpgdxkI8YNZLcDF2eM8CwDuLi5wfn6Os7NTnJ+e4cHTT+P0wQNcnIvYDd2qdLOZsNttQLDV0AmbzYSy3aLMM5CTWKJzhg8IfYFbqpSpOSNTa7XpIC1dF+fnkrbTUzx48ED+np3i9PQM8zyDAeScsJtn7MqMwozJttlNCfNmkm1VS/EVycE5lN+orbKXa8eXXE3CWqY1a5FN8cIlSazukY22vwUDXSWUFdLWGF8vKNVuNe5zpu1hv9LHGwaykO/GSb+hjuFwoa99ZsOtWrHXiF9vEFncb14cP9jsSncsFrlIR//R9c9V8KkXGr1jpzx3JncAXzXd0eUJbtMQgXvU4FsfSftWaNTmSxiByIweF+e4OD8XxqYs8vz8HLuLHXiekacJu92EUmZM00Z0fjljPtmh7HbgLKvSgFkXeyXv+FCdIFPtUJ42F0NV7FcDjYHkvNthd36B87NznJ2e4fT0DKenD/DggQEkI+fsgEKUcHKyxXxxgnkjVm8uMwonWb2adaEOcAUHtAoP0fPVhtEaVirIOUAaZkWAHIBgA4j18aaOrsus1ngkLRvy8r2Y+Q6Yl+J2d6V/YAiOj054dojYwIEI8WgE6UNV5K46v7ggbRgoemvNbaXL0xEs2H7ctFTXe7kbjYGPitgX5wJAZ6enePD003j6Ax/A+Znq+uYZ0zRhsxHxesoTppSQE2F3ssV8coJ5yph3CSVnsK6ELStiFzASZC/UmB5jjK0+NLLH3cUFLs7OVSd6htMHD/DgwQM8/eBpPP3gtAFIW8mHiHB2ssXJvRNMF5PoUY1Bqo7UEKHFjLoq+GWzjxoRcQAYZmxZAKKxzwFYHpNhLaSRwbWQ3EvjevRg7vrh2QOQz6aggBdFbJgOEtUXsv46A45EcuQ0WdJo8WtC0JmVMqtoPaPMOzGCnCtzPD1VZvY0nn76aXzgV38VZ2dnOD8XgNxMEzbbDebdBaaUMBEhE+Fsu8G97QaZgAygpISZCMQsy3mZ1bUU1dWiAvUsOlAxBkl67Lc7P8fF2RnOz05xfnpawfHpp/GBpz+Ap59+gJ3qIPNmqgSOCCf3TnB+foFpc4HNThhyygmpkM5xTkrjK3UyadtJYay6WJwIgBFEzKqr5BYI0QPhoSEwVKtDbr7+QRPMMv8wwi0B5Lp4/WiEmyauH8Vt3IwKTTTitFuxO2t2P2ukMfJ4Z1xnJ61ebKmhrCpO8k5MHVjWV4PuKMxKEQY5YzcHBnkhTO3stDK109MznJ+fO0BuLzZAKTjZbrHdbDBNGRdnJ7g4OxfQTAm7nOt6kwRgt/M1MGOH5yJuPHMnTjcgeXGhlnVN29kpzs5OcXp6igenp1UHOYtjeJrER/Pi4gK73QV2OxGxi1ndw+wgBvsq4KF05X/SWSt+vqiorkOzI6fp4qrY3DwVTt2BrNawV3vQQeoxQrxL3W5Ifytg+zd6lUzT0qm/MIo7pn6vED5OWQeCo3OPMY5SMdVHwKCjAWR0IdErxwXJGBk/bCrfiZ9+jUPSlroa6fRLHaQ5jzdgGYCxd8dBk/XKQrxj6iIEDRv0Vk5th9a/jZM2aqNvWUxp9HwiYldXGgfI01M8/eCB6yHnnQDkbrsBc8G9kxOcbCZspoTz0xNc3LuHTU7YJcKUdY9vA3tjkCn58mmsesoFg9xdLFnkedWNnp+euR4yAuQ066ZoU8ZmsxHA3+1kAJgrSBZWUTuAoxQSNUCp8sIQWNABYz2tKoQFi+z0etWwF9vSoP+73rP6STaGkiYt0Q4fkzw+j0NwLQsl1ojpHSgZPT/7e2wPemvnPXuM6qIuFzcGySMud3YbYZnxo8V55Uf6ETE0CR/ArMHVX7UYd/vMUFjv0a8lIOopKV2Sd/ne5UacynQcoHtlfNOPVaTdzQ5GuyjGnp7i9OkHePD0Azz99Afwq7/6q/iVX/kVPHjwQP0hZ2ymjJPtFruLc2xzxkRABuP+ZoOzzQaZGYmLLNXLDGxmgMWqnKZJyylX3VwpYE2HgeHu7AwXZ6cC1qcP9Pc0zh7Iz3SjT3/gA/jA009jN4tb0bSd6mo404TH1I9zu936bJx5nnXvFxkknNEGcLDia8Xrniis1FsHjmzHPW/s6ql3y/LjBmCr90G9ty89Nw/7QfKGcXfAOBSxbwmDjrov9ohJXT/saWh03SqIja1rZv2HLsOl4YVuWB+J0UEvOdJBxpk3EWaHiWg2OLFOYNdCeoJs1vhBRrYaGYdbi8XXMBpBducXqus7w5nqIB88/TQ+8IEP4MED8Y2cdztspgkXJ+fgMuPedovtlLHJCWcnW5zfO8GUCJkYU5LtWW3PmkyEVAooZ1AqUtfmPL0LovTFuYCk/uazM+ycNZ5WHam6+zx48AC7uYAJmMoGebNB3m6wPd+qykBE7NnE7HlGybmK25RApDN/4COiMio0qpG2KdURqBU4g2gdmOMwaD1F/1DyVhHqzGA26CDZVlo6KBhTDW0uMMWVpDXTFNdVg1cD6cNJV8NtB4mg4dOHhlsVsR+J0Iudi2MaPwugVwgt61dHtCZiaaQmHhsWV2DqDDIGnuhA0kGcwjdCIkhX9DP2yLa4Qp/R9ew1U/kIsok0aqd1P8PgazirHnJ3ce6uPufm7nMqLj+7iwvM0wTmgkwJ56enOL93gvOzrYjAZ2fYTRlzTtip+4/nW11wiBmUJHPChhg8V/HajDRurFHR32fT+O8c5xcXOLu4wKwAWQh6b+eites3ddqiuTeZNd90e0s9JLzED2nxVmcu9tp1Y5FBTPZBbFGP7ZUqVnc/B8tFS1iQhMVRB/IU/TadLpp83UL/KNfj+70ablyCkr/6bCt+D945Ips8KoPUJ8M74yeOZYAaSYeHvbc+qqxUUf1jjdcthLUF8+j9hj32YvQKWPaAOUoR16luAHfT4NY7qwN3AGyNMOQRwf9xdt2f6f+iq8959IXUWSwXFxeYpwwus1iuT09xfnaC89MNzk/PhPVNE3Y5O0B64rLs/0xcxNVHRVvm0ukfW0v2rvtdnCuA29zsiwuxYpPsHnh+IXO0zTgzl9l/cWCIM1io0+M17a8j6xTLNDYdVLHayp1Dm1oK2fVbVZpgkD9lTBSuGrHy8qmOmgD3hqW23dZvLHtC0zwMO8Pxsi9zd3wZeLbfMyBcStD7WjWae9FINRpgrhKOasW+NVXkEcM+cBwGr5dQ2dxWfGdE6z8Y2HWwVKMDwZFu8LLEsTGsnhD3jbJrXCtxc/jPO1kpYHWenue5AajdhThl28ya8/MznJ2fY3dxAS4ZBGCTs9w7O8PFdouLc9Eb7qaMXZaZK7sIkCkhMYMK6zQ/cnHfDDPFxOuLcwW4C8wXOxRlkvPFhTqz60IVZlzS2THIScBwnrErBbNayF20LrOzxwbATGLguoAIrRdnKHtum064pdzPy9/1nIEJhliaZxtjmgEiKkiixHa6nsIKvND2uvroFcLltGX5mXXJh4Pp/DI3nxHAXjccnUHuF7GPlOprhyhIHAiUV2kseyutBb6WyUWdY8ccDVyDDqv7qEZamcTNjGbGZDQyA4lSZMZLWYqfaw7NjS6zyNTFys7mxtnbxXgF5OjyVK3YpT4bRH9nunHucmTEXo60qHfJ5tJBO+pj0f9uUrZhQK2SSEhDSNOwTL3suZl6ieDYH0XsSv1iuq1NUX8psMwbZPMK4DiGCxXpF0/v+aKTFPPouDyVh4Rb8IN8FtDIZzosRBv960p+a62t79swMKrM00shXUtZnc0xjDcCzNrz1AB93IlxuCOjxcsVEAzQxK2GkXS2DFEBUakidrNiODeAEoGlDkCth0BKCYmlTN2TIA2Ye8j3TYfzhe9jZPQ8OsYCFBGBMQxIdfWfuLVEcbYb39eaamXjRzRQ+w8Aa8Y17WMXoH2xXr8mH+mZNPsy3Y+JV4v30WgkLp4ZKDb6ytaSHUlHPdBG7xuloO2HJmuEjtKACjOGq2+M0qq6nESEnBJySpiyrMwzZfEn3G5nlMJIRH5to2suTlMW5+ycFZiqC1PNkqxkk1Q85NSJm0BY8iu8F/SpKSXkLKuEbzYbbDY7bDcbbOcNUpEy25xssNVFcjdTRtaVxX0/8kPUG6thwEYb5rmvvINhRf8KK56bFdznYtfq+ptxu4kqcst3R6ATw4LNXZLG8d9DwgEAHXWGB9TBeKzncBQHosvj68ORdZC3ATzrWlbDg1WwjAYOA5tBGhuGE+K+XuBLf75yoLEdZWKJujURERuvgUnPSkiv1jz0womTzE6/5VEgipkBtCFLg6UkC8jmKWPS38nJFvdOTnBxf4fHH38M5+cXyAqKu4sdppxxstngsfv3cP+xx3Dv/mO4d+8+NtsTTJutbnMwgSbxSYT9XNarOY+YD/MPTUlcgfSXpw2m7QbTvMVmN+PevXPcv7hAIaAQAVOSbReIsNlOePyxx/D4Y/dx//593Ds5wXYzYaN5k/wS+hUp99a2A2K40+kuR220J672brO1RSnY6XTPMu9QdheyxNsuTAVV0ERg5T4omu5uTc+43sVqJpuDETi2eV3mdg869VV+aWhbeatz7NN0ybcvCbdgxT5uIEOSbljxSufLx6Uowvp5KNVW17d8T753RdYa6yYAU299bMRrBHDsLNirSumYeWZdostuVV0iU+/6ERjkIGoRT1VcToScE6aUsctZphFuNjg5OcHFxYzH7p9XgJy22O12mFLCdrPB/XsnCpD3sD05wWa7Rd5skHSLg2QgFzfNsl8dQTSj+oxtsqUgmaZJtkvYbDDtZmxOZpzcO8G93Q6FCIUSKCfsVOUwbQQg79+7j3v3TnByImxSVhxKQT2wBJLIeskHmXbQaUVkeLk3KOjZIgWsunmZxeE627nV186mv23AUVQUjQ7W2relmWW9y1rHXd48Z2PAiQajxTPNKGbnI/bm4k37WQp/hp05pDsSAt/KtgXG4bVRtJeER1LE3iMNhGe00i8b/SKLDNFGUBx9bnR+OUhyezcwCIZaGINsWMGx5rkXrevAxMsGCDQitu0x3aeVGr2WdhSgFf06AHa9ojLIrKLrZpqw22xwsj3BvZMLzLuCi90OF7uCadpgu71wBrmZMu7fO8Fjjwl7PLknIDltNxUk8wRKGUi6KIQxROXZ1ZUpAGcEx5yRuCBvJmGPpWBbCk7uXeC+rsjDSUB0VjF92kx47P593H/sPu7fu4eTkxMVuacgblPQUbYNIpZt83OQDEAZjttBMSACc4BGqqDaGKN2vpq7LTtXAnNsdZEtYJM3hNgyQl0vjpdgUsGxHVA5gqD9iePA2sBuD/fEa9SfO6Zi8yNGMXNzxO35yjv7wsMBSK+TQzkYtX+fGfJ6g1DdZVpXDWs9RX+he8X+3xsOsCZi1+9ZxwJVAATBARGme1RXl6IzLFKhCpZQRqmfTSSb0U8pgTcbFJ26xoVxcf8+mGXpMEoJOU84Pxfn691uh5yyTzf8sMcfw+P37+P+/RPcf87jOLn/GLYnsuVB2mydTVJWcZuSMOGe4uj0Q2IGckGaNiiQrREygC2AQglIGTMINE3Y3jvHydm5OIoXmZmTcxZgvHcP9+6d4LH7yiS3W2ymAJLBy8DZLOAV1fMTq++iP2AJmjE0w7NHrx07sMfZXJl0ls+si2u421NwTyplhlm1LW5W5twMyIM+dXk3W8Cm/okgeSgEDYD6kH4eIGSMJhz6WZvOW2WQVxWxm4Gjo++XfKh7yah0+/2GQK1F1RwfwBZX2Gb8Xrw+/i4bKulxAVhmg6DMAGe4v5r+rSK4RWp6yjqcrkrYNggvRAr5NPlCtOxT5sRazIqpYYzl2vJIDR5ICchZxedJmUnB/d09AFAGOGGaNgqOwnJyysg5YbuZ8Nhj9/HYvXs4Odni5LH72N67JyxyEoCkaZJVxbOwSAPHjptI8SiDpJyBaUJSFsIAZgBbkvTOIGCasLm4wPb8Aue7CwetnJOyxi1OTrZ4/DHVQzqLjHPml/jYpsnE51CGxgD9Fxy2oW150YisvVTrfrFl5uLqRbMu0hH8Uefou4n6Pa/LwHebfhDys2jrTbpqW7M8Dx68fuhZY08q4+f0NvflZ4PLQg0QWs81knprDHKEp6HOLnk5xLGQtxsBpU5HlrNFIii8VkfQfuRapmifyG2C0rpvoj7FLYtsOkt02eC2c7SVGoW5QDK6TwXlVctujE02erHuu62W28vEZv+YiM05a7o3OFGLta11mSjhQg0H81zUmpyx2YiYfU8BaXtygrwVI01W5oicRcwmHxZ0vKjMmP2amrjMhSeLQzoxI3PBBAIT4aQwWPe8zpsNtrtdA5AbtWJvtxtst9ugg2zZo8+NDy2gL24f2zox1JlMrNNBWcPetXbRrOLeruRu217MDWOc3de09QGNO2JiAfKIp5HEeNpDY7sCGB4qJzbAGPv8KEKLl+UhagYcT3ab5nZofTQY5KoxocZ0KUJGTLTj1c8bhtSnu8ioOV4DyUOMNHbRRuRGcxSZHlllct37OPr7+RS2IrMdevBqVrO2TGLRsZZCiqWB2+vaKf0bJmYX2WUwUAPNoi7CYCyShZWBiwN02RYwCDmLNXrKW+xcR8buNjNtxOK93Wzkd7IVQ40ZaKYJlE2sTjUZ3tHJ81QBiFAg4j84gyB+lxkscRgLnTKm3QYbZbXWRVIiXfFcXJCWOkhCu25nLWhL0WKMsvJ1FhPq1fJj7wZdsTWaxrcxLDPngOg/W7dy53rJMgfXnrCCj7XSvqU0drBhfgYAfkVwPFje7Pq5pW8UorQUbQ9jyGnB8bpE95E00hwrrIHdlcKValtfcLArYF0dgZMo01NJOvLL9LZUKitISRdtSAmYZySds22trreEHpKsyGwctMM110/xsiERCRtzZ+usW/wppqY8YdrMmDY7bLc7zLM4eQuzJNVNJgEf9YWcNhvRPWaNj6rOEVA/x3mWckMFSzlWcHedqqRPBhNG2myQTfzWpcw2No3Q3gPUKi8uPTlnZ5M5V59IW2VpXKhVjGulADggRhYXxWspf2lURe+3G6PVdTdnnyuuc8xVtBa9406ngO5cvEZc3uwutOFgWtuGDwqA3AcUh4Lkqki9qqfZJ2K3bBAQtkZlxlwSaE4qLs1IuxnzNDtACgCJ0YHJgIZq5VLzR0/25Mw6J7V7SleGxs2zi1ZEqOIsJwgUZUwbCLilrDNVJnFgZhbdp1nBc8K0EWfyPGXkaQJNufo+UhJzlaaplAo8pYTyZVZ1iqWxyrYMteBbOkFIzJiIQEV2WBT9nGdHwVBAetL1J6uj+GBCosnSCOAXytTr3MuxbQ+VQbKrCgiVMbqFWtmhLQriixQrYNp1nncOioUVHEP9XZsQ3CBE9rjGtI/6oYcQjg6Q+yaSXzbJ3J65jdACJa3eG53XG+Q838BxKOJ65xVrJmkDplJAc8FMtn3ADjRnpN0O024nejgkEO0kHmWUiVs2E/VKZmVtFef2J7iPh84TWSSpYqdVMcU6qt+R2S9ASoyUgQwCqACUQKkg+/Q3KFipi5AytZwS0pSRcqrpJtcyyLYKhX22TJmLsiwFFpd2OWhqguaJZHAhEh0oADHo9OBB5KK0AaO79rj82dcpXJfr4jbXeh6pQJqXIwuGMc2qehFwnFuDTABJOzbDDIw18gw3yjCDuplRS337OInXDS4aaxFQUAjawHw0RhuAkazNx3Ou9QuEgewGiHorOsi1Z/fdW3zrGjW5fIMqQCz+vSJIBnCsIms1hrSVQgBJ4ydA/RBNkSW/i4sdKO10IdaMlC9QkGQ9CGZMzEhpRkphGlwSYwjCN+O87YjYBniMtigjOLYGIdQXHUvsRXKGhkQgJORJwSgx0gSZpaLg6KWTSPV5YuRJiXSqYU1vAeq+N7MNHLrCzm5uRGxQcIFKkn8577JA4uKTygxiRuLU3Lc548nTlMIUyGUrcl1yBMTlU81fq+n+GS93XWnH9NG7uJ1Ftyjw+dk5zs8uMM8Xyi4vYItTQD0hSH9gcnyvfwdO76PkH9jlajxUB9aoEIyDeXMeBrO9X+ilGO2z/h3dlqOOkgFbyPtgTcYVMteFDwoR+2EHGa0AY2gLgcoYBTF4ZgES3oEB95Fj1b0VlkbNlDHNjGmaRW9WWF1lijOwhNE6koAZnxow6dODmuba0SEd1UAmsiF7N+YvEcBJn2fkREgKctlwIzIJB7OQ5qQ6TE0XzwUzM+ZZHc7PdX3GecZuJwYIB50sUx5tZk/KGTkrSKpLjlaQJCal6tYZSKGAo6oACEv2WAvOf1ZmziLjd0KZOTB1qhDPr8+dZlmJiMVSvbu4wMXu3LeutVXbbYHhi/MzEbHVOGOFTWC4PS8OFjEhdgy0iYphiWEHBYLuX86BRBnLDk1pyOEW6bgGkMXHm0HdQJJrl7gGkXyoAOks6xrvrN6/QXoOj7iyRwAqytS8NHUURCm2CiN2MdaaNWgHpAuF2ASkCcyQjsMidk55EgANjT3ZIrJKUWTqWNtKuB5219sZNGKgCa3YOzrHWCTNRKimQ2FwegSb98Nx7X1FpAqU+jRZ2Uj5FAbmUrArsmviuS5yu9vN2F3s1EKrK7Tn5P6VOWfkDaOwGIIIgt/tMnEtYBibMtZpf8nmgDfgGKrdK051yjZdz9ig1WwEyVpl6mXRW6jbKYRuiFHmeHFx3i0CfO5uPjzP1opgq9ykkG+qldDpVENbWes4PYiYQBTKJuJNU7gsumBrYxEw+z5Pi4Pug5ckK+JgnVVXJTt7mplCt+X1fK+EW9FBroVoTb0kkj03eaWk7JC6GqTBcRd/vNd9SilDU+gwPUt4pxK30PktEhKQFAYJXXYrgykpqCQgnaMwkBUgAUKZTNSuDKwUWU1HOn/10xP8NtcjZYd9lrq2F5lu1ZPpuyMW6XpPqCin30byY9GHUlfUFL6rkRcGSIw6MwtAXux2OL+4wPnZOXYXsuDtvJs9HWmS1YMEHCdswGBMwmABFf8VtEOnrtMFKYicVexsHLejqoLhddmwx1g4KiovZ8ssRX+EFXdEpVCnD84BIF3EPg8LA19c+P7kzHMDwjJgpaa+LV9VBVMB+0ph8LzXaXiEIO2dFBil+FoJi0ICV0nSvvQ1onuMi5WHkJ+7LhTwgfuqWQcecRH7pgab4Sg1BMfLIli7ORaxAetYbNu8QGsQlGW17MKEXIB5ZswzYzcTpk1BnnbYbEUEm3R6X8xHzqlKEqHFi7RXnaxjqhoJww0K2rDEolJT6aKyHBSPZtH72p0Y1aCEoCONHVaSJAswiJgp+sUCxm6ecXGxw9n5hW/8ZVs67C52/vlpku1ap2nCZt6g8BaFGXkqYExgVmMLCTu0tMr0xaC/BBqA9OKMA57+62wosMdmO1qgAmYo256GeXtQQNztdpidFc6+je7u4gLnunOkidhn5/LX/B5t6LWl56orWGqAsR0gxvrV64Ux4ETGSKp+4HA9GlWWsS2Pr5CUIOJb3XQ3NR2X+2m34aE6io9o+vA5ebiL+PJve/EMSnxJJtsRcEFEx4lqupC7+qoIyM44JLG+sgqAAgKoyFYCSQByLkBWgJwLYTcX5GnjBo9ZN49KKYkhIaWwlJUli5oEN2JEV2bqOVhZkDUYtvIILkD6/pKsq65P3XtIwTHlDPcdpHaecl3D0dipiGIFjJ0u5XWx2+H8/Nz3tTk/P8fZ6Sl2u53nM08ZG531MitYMZE0YgIwTfLdlECmr00K5iqGu/sRCdM2DgxtE04eHfNkEClMavBRoAyipM+QioXuIFnLtNi+N+rGc2FuO7aHzpkMDOfnZ/67CCzSVk9nFBkAktRZ4qnlrkHikEEhNd2pnRe0EoYjf5AM9LiJZyFKDxpi6IM94128Noyrjyf0SO7F88OMwvvCQxWxLYy1DEf6fncWx7p9dbAcDbu7Tc2zxl1HyH5giFZi8/NjqO6OhZswU9DxZ9E9lhCn/p02O0xT9g2lqt4rJlwZ7WWjpIrUlrbWm9PpY5UiDSS1RbuoqmzRwLEHSPuUfI7VwbvIloJ6XfIrFmsDyQvd38Y2AjMGSUTIc90nG0SYLib3XUxZt4slccBOVomB6Rq7qkxSUth6xhhI6r/MvpZm9Hls67mbNuplGH1O6+wY3yHyolqud+d1g7Goe4wuP2LxFv2jWGaSqlUqQ/K2EJmyttbGsDdSAXj70NG+Zw5oB2RXO0X8s7rX9+NgQ1T7kWPAIfSR+ts9KHaPU3s/pumqgPlIi9iPVOhGYONjxiJiMCYJBFFbUaYwgGS6SAIVYZCZE3LRLQe8IqVBby4mzNOEeQoAGX5XqXQjNtIHFCAcVGvrjXliy7X3v1SBR63JKcxAMSZm7BlF/BmpsMfHKm7P7uLSgePpKU5PT7G72IGZddri5O9SSph2G3XPSUhzQs4iCvs8XQdxZeANe5SUEOpuhaYGqBVu4CZ/o5htz/tgZfciYJa6h0+/fe5ud9EYYs7Pz+tOjHpss2fMqm8zZoz5Cls20UVT7aoD8xowa1onZh+JofQSTCtWswoqLWjeVrgJEK6FO4C8ldAyCFP0e2cqBSDGPANIMxLNoBlIeYc8TdjtZrijNHPYsoCwm2TGSg6i/LVSyC3b9PbUxbuUspKDTvylnH1qpFsyIQzR2LAwafguggaM5+cXODsTYHzw4AGefvA0Th+c4vTBA+zOlUEmEtF6Fn9JBpCm6ruYcsI8pyUoWNqy6SeNUWnumGWVJc17LB97xPTABJmm6FP6UIHeZ8T45mGzOr6XOn3QwE/1jKenD1R8vlC963kVt891+1xjmfMO5hAumGhTjpLXn4GCr4jezQyKPrO3iFNtcCRv6WQvYh8zQbVdUyACY0nvsvDoA+RCryHBhebVgm1FiLXjQ87jRyPN72fTjMpepTPpSEW1gFZRpHSSCmgGKM/Iux12c6nxEeHc5gpPU92etAhjq6Nlo4UKX8eijKqeLepOrS0rk+RRfPFS9Mms+j5b3YdDHKWIYzTPoocrturPblax8hwX5+L3d3Z6irMH8jt9+gEuLi4AAIkS5pNZkyKMcnOyFcNNyki7hClnlFR07xmtI2ONKQCkAYQuFiKrrZvPgaketJxsQQ+dqti3uQiQ7W6LdU9xmzW187ye4/zsVPYQVx1jBEj5nVXmuNv5dgoAgwk6/1z1gCFPDoqmejAjTqxFP6+MPnp7kT7TG1Va8XolWOfhupSL4KOqKoL6ZSlij9rZ5RpT0ojioO/SfOfm86wQsbvuuwz7GIwN7FZRqo9q46+6GAq1UB/rwDMmSo+XxdhrQIKI7TdGCFnZpIkc0qlU3EYBqIBnRsozdmnCVKBsTHR8u/ML7E52vmCqbdDUzoapea7psI5AXcPkIZh7ktfyTTCqEuKsIOQ+jwhqBmPO3WwZY4+7CzNSXOiMkSpin52eNgAporZ8J+dJgEQXmZh2GfNUkHJl7JZ+AwyfkWS5LAlIul5nU12tmG2qAiqyghEI4XlljlxnxbBviWAguat5Vj2j7xV+dibHCpC+UMXFed1OYZal2my2jLtyee1So2Okbrm2BUB6ZeohtzetjYyW699H+lqJpOq2dUxb7e8NAC4ATAeBRu/YPm57ZldwDHvF33Anx4MB8lDr9FVC/xaPjoff9eFhZdTZ/9UIms0ra7WvZTyoOk/G3s8yQ6aGQfeHZuwKUFgMNYUJDJmLTTkjq97Ncn623egK3Fnm45aCWVcGSpwR8bGKjzHBEdBiskxHVLMZ9ZD9kNB/ARYnhetRNxdAw/Rvu4udG2MuzkWcFPFaWOODp5/Gg6efxunTD/D00x/AhYrYKSXs5tmLM1HCyb0T8YukhHkzYZ53yCVX3WwPjmGtR3ABU9FtvwtQggpE00+Q1Zg4xMPzrI6s1jQrg5znGYXr6t7NPGplj+fnql99IPk8P6vMOc65llV66v7jAJBIKqu678Spl7ajY27ZsqobpHaaGm7qtfpk2KDQNZcw4Cz6xt5QG+awf/jt0I4WD+5BaV8vVW8FkJR8dAP8FcNRGOQxlaI3C+tpCOqXw18z4NmTNULQpbATrGG0xvpKAcquYFcYc4Eu0FB1e9O0QWWdwPZki+35FtNmwu5EOtC0ySglgRUgCSbaeqraVK6jtwOsP8JB9OqLw8qjHlgs4NCrWMXqUop3+mix3SmDOjs9w/mDBzh98ACnp/JXQPIBnv7A07g4VwaZEso8q38jI6eE87P7mCZlkOcTNtOEkncoOWu2uQGOHPWkHCp21rnmOvCyrSKk7VpIZhL9XxILm7EWX+eTbUvW6Pytxhg1yIje8RRnDx7gwdMP8IEPfMD9Hc/VpcnAtV2AQvNPugASEFQH4guZKOtyeTLbKKVqre9aqzP8ZmRfAN3w4hVDjWP1E3YwIC03D4R2y4Gr5+loInak1jeIZHlpcc/otB33IbIbPRqA4z6d5Gp9NcAXF63QJA7irKAj/1RmJQvLzoWxmwvmYjNSEua5CJtMsjyYuH7sXCy1RVJLZjWCqO9Qg4P9yBnYQuwcTfpiZtjBv75OcqGvpko6Qcy6Og83IFndW+S3u1A3F2WSu3OZg+yMSsHz4vwCILhD9KQi9W4b3GDmne8ZbYOQoX4FE1NZCLMS6V9UHXVGECr7jWVFDCQpj1Jk9SIzZol4rSCp4Ga7DsbFbn2GzPm5i9fnZ6c4OzvF+akySF3CzPSNQs4JOcmqR0TBlxOtCO1zy90wY4ubxHofgUOteMtr9NJYtIsrhCjJxMGziW7BGi//0ILAEmqaubJIudWM/FfOyPF1kFdJw9qzfUU258uXmjGyAcMKlnsBcXRt9My+C7xyQxllxZ3ArOb6u5hVl0IJeS66EEPCNE1u+TSQtBVvsoquKfUNn4bpiFSXOd7n8JcsoToAU9X3hsjiwFXVdlX0jK4tPIeN7305rznoIGWRiosLA8m4WMMFiIBZRceL83NM0+Tvyd4sc3WiZmMMxo7GhhrTA0e/TasfW8jWyqSARP/oelaJvgHIUo0yvh1rmDUT9Y/uDB4Gg/OzMwFIBVsDwqSrJ1Ey9tM7gQ9+uoqSi9iNFTc6tFOtaqrXxoa/5ela6I0zJnoMYWHBIpuLGs96OkjbNXvZtK5FNw3HB8irpOuQZ69olgfawuna/wIIj6YZoCj6WGVhKaEHdjXPZsnd4WIH95VM06TASkgp4+zsnlg+DSh3O+zmHdJORMc5zWp8SN4QuU/cMLRNzxigvxHHIj02luX58JeDfotRdY+zrZ5uK6fPDo62OrZYdi9wcXaGsyh6n53h/OxcjTIylXGzFcv17vzc10Z0cJwjg4xMK6z5qDNqwMl9JhuA1LzEZsdc4LNREiBrMIp1HgiWa1sdXveuNlCcbY61scfzM5ydnaq4fYrzMxGz593OmU8iWdQXWedY69YS1ZWn6h37Xw7HUpnkjHfp6tKCSmMJRl2pxxvFdaRuG2gPennZVqu/yIGfo2vBxjA8+m4+Nwj7wPBawNi/s6cSKpMViGzB0mZWmAvIjN2uyKLQJDNCcs4CGptzV/Cb07CDSxajja04brqqSiAbbl2TbWDGrdgl7Z/a981dYxVj2TufxeLxB5WC6fbAnQP1XF1iqt4usK7dzjvsZC5Ovh90uw90zAcRIaETPYNezreF8Ky2IBldXKQYxIJNhTyPrirRVb2jS0/DHOPWCbuqGqj3qurEnfezLGTiCy2E9uSzhyi68iR37YnbRfRMqrUXWDuoyOcAat8zHYupUfZtI9oHBUZ7Lzp5cMxUn86urUlyYv0sE1Bfrwu32LurER8QPqgB8nphX+0fVsCNuANjseFYv1OdixUgigAUM8IUs9YaajvazbsZc96h5AnzLK4qKcmcb/sewF3Hr407Oq8v8081tzYcHzyisOetL8rofjXqBWZF5uBX6Jb2xMtd+zymqmu0Jc+i+BlXQ+p/JoJrxQ1FM/cSCN9tJwG0lnoDybILjDKCu543agiu1uqmzAIwRhGaqPVzXDiDI+QvMP3rOEsfLQx1kIewlmvS1n1RHhhuQQcZRK2VcFCar1omXUlc/o32A53UsezcNAKTcTLssagrYm/gXRoiSM6qzysc9iPRaXi6uIEtA7bRja9y3omeyQy3IBetms4+6tw29S/mi6pAwwjxLEzdfeGtl03vlmIL1iZKYWMy6+Tk4jsgC12kDpxaHVwFikQWZ1gtPDi0JzfSSElx6QBzVImLrNoAgCBax9XQIxMO2yYoUEb2W0pdDLgvr5HhpRWrO3CkDkQbsG8Z44dyuCpePnoMcn9fg4mrQNAvNqPsdYj0IA0jmj+MuE+s6HRE3BURiUBiCE0ioiUXtzWzzOpcXFw8KLO5xZzXKWpnMuPk4uwM57ofdNK5z7ab4LTRVKSE1AGfZ4TNPUVEx17HxAo+4CJbqKIyUrfIxB8GeKL6OrItF3IWg5Ku5ZhnWbosT7Jp1pSnOq86Zd/AzMEzzAwxX788yb7btn3rZrPBtJmaHQpzTphy2PLBmVbdu2aNWfYzSWKNx21W58AaZQ3LC51bfl5/56IiiSuCG6gly3MYSHKWPb0nzYft2T3p3j7R39HK2KdYPmvCWAV041gfZR1kr7Tf+4wGuvIIZyBpApZd1X8pnq+koSZ0EP2anmUcY68TIRhrlJkfohdJstmVWRhTFbfJEsQsjstUgjhW3UR27lx9gWk6D3qnBA7ACDHXiM4sGzCaiB1m9XQiq4vmzDIdknTDq5TQmLK7smE3dsgD7HGh2RjLOvo0yeIb02aSbWCnjV+f8oRkm3zpj0j2ssnThKwL5srz8u5m2mg8FkcFlGqwqCuJy4CldYTAvGw/7Bk+8vJwtKyH5lpkhqjdXHWOF3Fl8N1Fu/0ssZdJmWYwTz7Hmwi+Ja3lJZaJO7zn1O7fjVZqCFphP1vnH30f7PK80if29tg4Pg+O4jnT6N5hX5Im2zbMR8+K3QHiVUTsvbN0+h45VFlUpKH+oXjG43hCStyKuUhHp7tpxdcKkkRGQFt3DDaxMgXRyY03rhjUv+bTJ/6OcyNui5vIdLFBShfaaXYuoiZ1EhYFv+rvLJEw9hP0Z6VUgFdki0p8Bny1G6+KxQrk8f2oHwysLMusHwc+AzP9ZQVH33+nA0hjTbZ1bP9+BFgDRgdHMt0kgq4unK+Kpm1j4VBPpr+NTuLmFN8aaHYBHGfXMzpzzAm5TDpACgM0C7Yx4NyAY/IfRTUFBukesftQVeudNPS5iJuDwXEcSXjXb+8HrBE+thwmQOxC3VU9R4DjsUfgCAvmrq78cqVU7im8Rcm1ujwXswNtpObdNmVL4tin04BkT/pHDcXaBFklBfYInUfMCZkKCknnlQYOB0lnkvrX3WN2O3UVOfdlsQQ4sBCthFVaOalDc5Cu3RhSzBhS/e44FiiR7M8F9aPjAuj2s5JOXYncDQC15zV9giAgoEpSnidsNltZwGJmbLcn2Gy32G5PMG232Gy2wgb1N+uCwQ1r3MjUy+3JFpvtBpvtBltdSNd+Di653e/a3Alj+zF2WZxJJmVz7COeAIpxMTMm1emUNrVwN886W6iyfbdSl2ClhrgugTcgEKacUKcUhtk/KWEzTZg22cXqaZqkTFNu8lbFkVDfVCvFu2RHI0dddUFiGmay58Wr6jnjhwZkHYhEqH62YcqXfPImXPLR00FeEqhrBHpV/70prV4Hx8Y9YqUiYxopqbtBSihRxClJnbvJGY6wHUIh+D7R4OgKM/t6ibY0vy26IOJ88uSknJFmG0TUF073qbZ8+Ao0ZijgUvWMyiJZ3+MkSSHKMsXOckPkK+GAZU6zo30oSzMuyC7aBGzYrbZgxlbBcbPd4mQroLfdbnG+vcBmu/V05ZyxvXcSgFGeO9meaBwClKKPzCKipoxMKYjWoY6YGzxZMEh1nfIdKcIgEgHS2WPcWyau96jidpl3WtbSjvKUsOEJtkOjMXkKZZZTDsy5MmIB/6qzJdNDBlnB26uT/Momj0iwjht6tvoIhNuZSQMeoLp2GPQVRFXE9EtLrSTpdYdBqiKc3aJ6o74zTmB3FltNXbBgLZi4va7nIE+PqfAAEXtNJ2dGgxxF7tRPX2QXt+s0PdVHTtJBsrKTlLMslzbPyCWjFEZK3MRXxWtb2LUey+fYy54L6+rnWh5sU+z0OX2XClCS6PQCZWmK2Ri/W5dznRvditmqR1R9Yp42yEW2F6hiddA1TtmNPI1I6mJ1p8IAmzc+3HdSgS+2pyiRuBM1qm3fyspZZKmr+ZRgtKlTLIuDI1SszymDs7ShkgJLRwVI091OSQDS244baGrbqS2va9chcEjz4PaiDbdhrf8eFsOVQPmK4Nin6ZjYegsMkru/y7t0yZUYDBhbsbnlihGnLtXNLj7XKmgiOK6lbASOlX3Jm+QdTO7LfjKsVscSwNHcUqq+sslH4wbUTl1LCpTzboed6u3c8TpVR2oDdAPIKmZXoGzAERCmyElxsTXmiFie6rVSdDQggEwsbYY2Z/5ZmXNR8bfXSfZAWMoMIlroGrODY90Gts4m6VyqLH86XdDqMNZnSOpC3xxbietuw3Fc6qxZE9I8E9SQY3Hb6kJ5mpCiszsqq41uPMIia76Sqw2S53ORX89Qp17i/plrhMsY3oAJHtItH8XwcETsY1Pmm8R1hXev9JlhYxBrtfRNYwRFQCKbeE3uisKlYIZaWIOlW5ZK0zm+YfGDlBJ204SL6QIpZ59ZkyddTDdl9bULnZ57sKvKqWbqIMm8XdmaVpcFA2SjLE6yoIJuGC3SKLtFtR2llmqJ6ueXXE84qQHG9YgqMjPYAdLceCKLjMyxZVOmY9Ul5eadbOZFqkcM0yCBMEisdtU6eLZO4twAZQS7WgRSJuJ6JWVCgGwAFgcoCNuNjNsGzZQJjX+nrZCeUsjzmPW5iH0s68UjIv4+jPBwAPLIBRokoEcneOeP/LaiAiUDvspyzEJpOjNhe0nE8sZ3Dy7atvquainNu53My9Z7Ys2ekXJ2vReIfEGHyoSiSF9Fs+oQXWR2DpPoTiG7ERLYAZKZQVl8PFWJFgpGV76GGoaC/lFESvMZhTPBSV2ABCAFUKecg+ht1u3KTBUF3PAkC0eIYDyjgDmLKkAbj7E9A0rW6YK1FCrILWbNMDflGJ+VQVEd2NW/kRnI2XBSVjwvKVdVh3wExiCtHSXNuyw6UQEzZ2pYcnQWb4JRxpY6HtCYH1K4SR9+SHrKIwFkaEz675qTLQHu83Rp/vrO1jWCRw0jPbgatM5rJfuPKKxRmFwJPymQJZI1Im0flewiI4BeJ9mtGmOuJLIUmmzLUFQJKq9XBqmxeWgF4tr+WDtXYQVHFiCUKpS4UiqQhTKWeuAIkFBndi6z/gTMfBXLwCrN/8/mTJuuMU3BOdqNZlznQyswljlhTpJ2BiFz0S1gVZ/oa3PGqX4tu7a342Di00ObQQbaNsmnAZrzd87F782zLHrr4OygDtc/upitulMyfXYiB0wDxUQRJGNNkus7YUOg/sMRLCOjvKQzRWF97Xj40srpjWH6IXX+KwDk/iw1Xc7xsn+n7juM0dFAMgO0UYQbFIDzqmwyGlnlu8puwmevWnlDY00Ut4UGQWbYRNEyY5oKprlgs5n0XkFi1A5mgBBYUil1Sa06N3snOx/udpinWVb6SQKeYG5nWYzEwKZwpESKGjASSIG2ssRkpUcFpRgwdPn2C3qxBNHWd+mTbUyFRRqrFkPNZrPxdNXZJOLiEt2pfC77vENKAkK0YwATuMxIhVBKdIchFzuZGXPDCEsDisbsWpeeJZACqLOAckYuE6ZJN/uaC4q65Lh7lRp2qnqjNZYRRZ2ktR05FtVLBcpFs1N9cgRDFUACKHZ1fxlWesewTR/qa9Q/18RJwwhpcBQPh/CxGm6PFR+BQa7rbB56uMmoEpCxAckBlV+o2fp7izTJiG6bbCVmpFTFavvWbjdjLrKqD8hmS6iLRzYDDusqQDvQnHQzqHPRSV0kTBcb5HyORMBOXUByzqBSKkg2+ijNrQFOc1V8XIpOlywze94LF6QC76QO4BTiVNZcAUm+fWGbdl3M4HkHLjMIxR2kN5sJu+0G292JqAh0UNlsN653lPnmrLrZHeb5ArsLAngWVyqeMM9ZQZd0325jY8pqOQjTxigDcDasEe3mXA0LBIQ1khibAF1VKSWUafK52qXs6uwlXxgXCsSl/Qtl1lE3Cfb21bgloRmGsOh73B9cr2+6HdKEw4iSwxeu9ZlHKhxRB3mTwucWcSgI4BFw1kBpmA672b/UxusNXFneYlaPPd6wW2WvA3m/lzDdnk2QJZ8ig5wyjCcwRO9YZt1DO66APU3qWG75azuZby3qS21N2M0z0ixuP4Cym7UipGUmDTjEX5B12SpVF5CUV1Q5GuNpIgAqQEL7euG6sMNOplIaeyJAFwkWkJy3W/Hx07KZNhsxQGXbNiGwx90OcyIAM2QbihklC0DOiZCmVME86dqZmmdZOTs4AxXWMSQwy1K9CXyRXAQ3Kq/XSQYTtTLPRQAyzzO4TKpiKK7zbPfSDvtpc93FEAaYHMRv8uGnbZvKHhum2DTptX4aON2gkfjGWICvMi9SvK4Z6Q92/bYhGMuI2wG5Y8O2ZFn7zyLt3F07JjU7koi9L+FtWKXjwAIk43EEzHC2jOQyEA2jXq27up2RWDn7qlomz4Zye79Na71EDHWmDjpI70xZO6J8M1GSbRSYoSs9SGe26WXKgFwUNHcSbleIsW1HDTRjDn0JfsfEkAOq3DkyW/b3WcU/yVhcGoGo6hJrpwzA668z5rmCpLCqKGZHQ83s5ZyIlD2afk++YwaZed5htwOArAAjYnUiAmWSDc7U2CHuMbmK21QB07YZaKzTJRhmen2ltwUB3gydO150cJvnCqxxHcvw1+oyLoDBTOKW5AatOghFUGwlFm3Rpn9UUGkI5Jolu9mRs+Wj1n7MVd48l41gNH2x29kzqsNiiMAYUt70y+YZWk/6s1LEvlqSaVknh9Lz5r2lPLwaDY3vmWAIjCrEwKT+B4xIajUmiSJedEjQ5fMTZDECc2FJJH6RZRL9VmHvBtrxJgfLpCZtnw3D7R7MZtFOKSHPYtVmUp0hA2xGn7jZTFOGLYsuDYuJPyCh+LmwzPZ+bQReGCgsbpO2bYQwyBmMAkrGICfMhbFlIOWdMz/bGzx3DFLiEgApZYecE8oMna/crr5dB6jJ/RFBCYkm682e9Oi60/g2ztUxvFa5OLNTIWRlnBN0n5oArtE3Mu5BY9vFOnAyoVr8ZddFZ5JNU7M22G2TsOAssTEve2dvMl30DY86QuXo6QHJof6ZFhib5DRseJHMhx5uzc2nLcB6be2sofgN3a9AFAnJtRM1vMiD4+6pOGJTe37pJ/SOvWe6yGmakBIjZ0Z2doEAkFDH5gR2Rtm6dLh+rMhWsHX/aWGPuRRgJv0+q+8d1cE+rWSCZVKhAHzVj1WgFBHR9GSEAt07tY4sPUCiAmRxUXsHaDyJ4OK1MbOcs44T5PdaHaSJ2JKmnAhlhojVZsxIUDAEbEGPrJuiUc7qEmXbKgiI6uLnDTjOgaE3zt0BWPNUB1HWEaE6kte9snmeMe8mN1TZAGf54R3AaQYXguOwbh1s6giiSxBkePsGqBOlIj3XpjJ8doCLq/FeurPDMwSWtwOQoSDX7jVygl+qQNiMaNTeA+rIeWha/DS+xyaA1PGsTikb4IZ21HZ642WMNYJZjTMlW2eR3UIp27gqiPhoDbADJHUgGSK2HEQDg3ZMSmHWiz5Dq0jPFd9iXDpnGzCgLAKKCpxybPdDa+/Yhecv+ESy6TlJ5yDnjEkNJu7BoABpTuHRMMRhz/GZzTmfdUFczVozpXMG50lAsRRwZtVoZFCqIiTD0hlXN29F7FrHZiGHpxcgIBUkNvempAwyoZAuS1dIvAB0/nwpuvd3Ds75mhrm4GvK7G0rCgDL9hc64bqM2oQIbN4qBiwv7Me1GhF17/SpMoxgCsfdKwbGzwRI3q6j+KDg9hXmtURsYCRZHxZo9NoeDtiAYwdSfQx70hPfKWwgSTp32gCyinqFk4Kk9HZfnMA7Zd/6gpEh/OQWN3no89X7SLYO0qWySJ5BATCpNyo0VvKaPgZpFK0bjXV2mYKXwMgoDFUpGPNLQQdp+TbwUHcjvUTEai9Qy68uBpKIwFkW3ii5IKViCUNiBrHsLW2iX6uD5IY91imKvcN2dCdSz4XE4rhOCUSzDPgMX0HIispKq/AskoHWPNuqGbqIRm1LB4LjFcOo+UbAeobw6qGH2xOxqR+wopN316mvA25t1DcPocYji/Tb1wTHWA7x+aTzCInN2dpYXhWxWf8W1iXTlEHCNon3RWTNsptiKgA0GsEulUtwbAuigqKv+DPvqqgNWW2I7Jjnum4kEDPdfZOMdDrAVOYo+scCAJRkmmPhoKslTFPClMTSnbq0m8hbWEGl05nq4uuySK3OMkpJVynK5qs4Obgxi+qv6hzNc4Dbtu27WAqAybYOrc8Al4JECXOZQaXeo1m8AayETMNIOaMQAyS6ZkopMEpdfQl8lKZ/F9bD0QCSqN1NbIlaVSztLsGsuzGueEwd1TtYvF4kcphwAOxuPgBUCU3tIwgswZgcoPsVh+h0eI2jrGNvYG8FuoOolhtUpATEMdvAUbR7qQIkxGjjujNb4MEXm53CYgbVRy6WXWU7UVStwKj/tyKl+iu6r576Lrq43YjXnegZ5TWiKjLZU4nEUl8ypknFLdUJFisU2ArchMkd7cMCxBalg+Ts4j+jTmkkIvBuh5SLrqiTgVKQ8xaUFDhT8kUtTGdqBpu5FF2OTtNJCXE1qoZFJt0oiBlMBJ1vJCnNNkpkaXHMSLaxECfMRQa8JHt1iNN+4oqmXqpci7Zpidz9PSws47F8uVS/EK33WZc9dd1D41eu0K/3SI3HZLc3BsgomtWOhkbfFp7urlH7ZwUkBR/7Z+VvR1IPSHCTGlhJV/etZckPARIj9hXeNExfqcgRZxPpiZTBGj8gJMooUQdJCpA5I+W6YIMtxW8iOCLr7TPUiIPhCdayUNAWGtWtHckMFLE8tzpIQY6qe2Yg+Bv6pW7wqQvEAimzwkSC7DldGaA5fWeyZdNs8y+C6XJjuqsVWIwfZO0okYxQWf0QNd+yytIMTjpxGuT6YGGTImbDBy/NUCAGOoQhbr0KImHbPijJnHtKSQYWlv1xuKkTZbGhjpitPitauZbBA68cHwgcXf/ymMwTwwYGrANj41sczpep7G3nYWBtzgbppuGhxNLwqZtx7CMySKBv+IOn1s8HzFLioeX9cGGxluSBoQJZBBBtdPG5gHjNzIVO5O4jjqO7nbMOwwKGJKzAGCebQCgdwKzX0qsnsJhiBSDTBpRs+f0Nsrq/+EIOvhRWqiDZMEZjOMF5GnDbiqc9gIxZW10XWWZEq7UBJKGKn+5TSgb5TcGDoMt6qUgsAAgABUQyh9wAkiBMU8AROoeddGqhsG5gdt0hgtuMpHv2+ixEyInBcwFnzUNmEWtTQkqTV3xBFbXtL2wAU/wnToE0U/Oz7LKu9C5vJnH6LjbgyVRQsuXYqF3hHGHnR8FFAVQsyhSNYXRfz+h5ykiy8xMv1DHEeh7Rqh4iMI7fqk9E/Ki0Jb67rk7oh4GbQWIbjqiD7Auvzd5ecOzOlz5Z18tyD9IHx0KxquAg4iwSFRyr43WM3YZOHfm14ZA+RwEkjR8J6KbaDCjLEjdJGCNSBlEGUkbKCpAuYm90Dxc9DptbOVNJtpiE7F9iu/vJroiWbZUCjFSp+MxlBrj664n+cUZ0+2EYUAayyDZn28QJK6PWuEQgZBKTxAbC5KiwzGFmd3gCka6h6SCpRaRpB6t7jpV/YfA8g80ZvajPQgI47ZBUl8ubSXWSMrWzKECKVKG6ULTgaGwebH6BfdvXn+UxGauyedb6PpVaYGHw4pQqs4S2P6kwYbFmrDnQMh1Ttv+NWE+HvHQQLx2EPTLyIxSuuCfNZQURGSQtrgGxPgeFE0bcy0Ts9XTuOY/6MI+3ebq26yYdKspEBhkYZROD6hYOajLmjwjodK0gQqWsoCZiNNKkbigT0rSpjs6+gIM8Z9unmqjteyojMMjU/oUyvMKhuQert83ukJkdc3X5iXpHmPOwTEt0VhoYI2DzyX2dGnHJURCVRToIGQRQXehXC0vyQbaHjAKkV1kBz7abI9w5G6xMcQ77wRQZKOSDuh5kUjecOYNyCSKusHbRIQLG4k0FIFtJ2DAXOr1jpI0WtQ1W4Bwcj+hcUInUBhPiY2Bfi3OqckAXqvDYtWtyqT7AgIjdjUYFnUht13ogVzwx+0K8TQMavLrRKGJx6VDaPHgzEL4ig9zzsQXYYAEeMSzLawSu4Xg1quWHDxqbGvym9jC28dAoGxF7DSDj90kBI6YmKGc7U1DTeYT1ZRWlJ5CyRsoVIKMlO9mzvlaigSA1onbUb1VwrHlrhCLXQQZ9XvgL41VNZcaWbvkN9UFeep5/I5O2oDAjC0BCO5sRLlJxmuAsUogV15lBlodGDxmmCUKeT8QoiQEzpCQGc0ZKOiPIVytOQMq6DS6pASmgxIDBGdmz4dgYIHtbbkbg9q8XHfk7FWO1nRpaxcbWAaYNKOzicYdta2GcHG+2vW1hSGA7xOTI6sMtgszjFjwkuyiSV8xSyObiEzHRerwHdq4cjqeDHIw4PXiMDTd74oxAucoguw45eqRNaBM/dfcWPmlUG2UUq6OYfZlVneMIT2YGouDS5mYfRQERpymLMWaaNqCsoDhNoLxFypMAioJjUsYpGznpdqApAmVcpt9cgurWswbkXiJc2SNc1La5wtVCXBlkLdOgWRp0NANl8vuSPkZC0ZkzAlKJbKqjY18DknLMYWZHNbGRFbyK2ZhD+sFg29AsKSvOsv0BcqkbCelmY8gqFaQE31LC69+YY2xbYZR1kPThILQ36p4PvzpWenvh+KwzxxBFbWz1kOrNfcRhpEc/RggCQH9HncO1znr3j+E710nizTJzK36QI3C069E9bjT6jEDn2m49VwkHgqNZsVfBkaqOUaMC0Na1NXZy1mgfNZ1jUtY4iZV62ghrzJMYZSYBSErVcu3vmRWbyM9t35ua/pCnfWVrLCy6+6jYCtNF2nNOXFrmwszu6ym55MVISaG8iVjnikNXMvfEODja8yZeg6DPmZEosN+gR/W0g8FkzuUJSZ3SOck1ygxwEn9Fcy21peJyUo+Nld7bsMSufP28A8pxRO0viNgi6oaWdSktfETDQaLeMxuOCpBxtJLzq+d+n6vPrYRuCDZluMNWD45EzbOLPKrPG5kOL8Tjj8BAkto4DSCdEWZdlTorOBpgTgqQycVsdyEJCzIsrNbBGtpkXolQ4ELwM+5/qMf2nK+GLZGJ5Tr5+3XGTAEXsdraEOISoAEowcuuMuxwDwam9fvyxUFa49zw/jrE+R4Q8ZuUkQmmqrt2AsDJv2/pbPXYkU3qna69LPDryv0igK59q5rNsVdB18XyyGDpbYHjkeO9NQZpo2VMayQO+8Tt6D9G/TVgqfC9eYrDgN+xyA4MPWs9ixw6wo7kCwrM0ZiBKdOy6x5Tjr6O1efRrNUiSlex2sTWxvBCtmNiXYTB02tog/EOftygZQAIF6nD38hidGk3qH5SXkm+0o9PmXOQogC88PUn5UvROtymMa4cxIs0mfO6HAujD2m198OeOkvWSeHY9vWJvc9ahzFxhHLvBlLLQbjWh1Ed1MzWWGg1Hi3D0BZNPAdsQH6ECJsXikofHHStra/Q9eI9UjgyQNZMXjedtDg45A0+4Pm2UUkj7j9Ew6erxmwU0zg5NQxquKK+sz7xbzTAm4KInZHzRl15xEATGaSI4/q+TjWMfo4AdOpb2EzKc1QH3IoPFWxaSEE44+68yy4ZqzQWOcspRfG7DhLsrNSilDS0wCbR1ul1EZxZRegduOyCAak+m7TSYufTr8NA0r7Zsk20bE3rrR3YCLZ+J3yKYcfSB2VWs6rnrs7AMoR2RaEUIgTWBwwZ+2XJHtHgbQYta+oH6Gcg3AKDpMGRngeiNSwHe6sD2YWluDnnQWNZS0F/LQA6EWjtcaON8dvUxR6pcWBcRrqW8baib8pZXXlSYItVpM7xXBmli9gpWqnTIP5gsfZFHiI8WNK5wYUhAKK7xxWIXOx13x4FqiLpYQIKdiDkhgVVyT0ssoEAWA1ER4CzLQ/MSi2+mcwzwOLIbsuoMVRkjxZosnJCqEwO/xp5a9k+ITrhq0FNgbGyc6ptwmLqyLC5HTXbOwRGbuVi71dg5Bq1r/ROIW4598OQl7XaPJae3zhHD/KHALWkt07HbXnGaCA+JEV8hWeX4eYAedmHIyXs63EQRbRWRwF9pOuzeCo8dkBaIzooqf790KIayy6N/0rl9yJSYDeWUH0nAn/VEwbxOlXLdOrFbF/DsOoqkVqw9e+4WoD83EVrbcSxPmInjdLzsEzjBeZQZFE8klkjUhqi1xN1ZXB5AcFnETmb0nIbiPDtMiIMA+HllrL6HtkK6Fq7Tfsjxb1BAwmDogHfUq+bvHzjcS0l6pAhMsfKGuvfyNrrIFvfD+lsEbytTH2xVXAdHq4LlgQKq+cbIeQhRi3AMuTnIBUajU8Xr9Ly2auEgwGyWiF76rTnHXvewvBwGVn9xh4dJC2LuyMBNui3H118rnvA2yB1d6zpt3+7JLTnlZqFb3FNmDI63wDewM8dvwUs3RHcdswzdx7KvhlVq/vC4ryCZwBJBJbSgFQP+LXD126n32RuCr5uzaDfMT0eKdlxx+LWeGHfrGBp+kquD4SCZo+B6zd4rjN8UNwCTiTMsXae4DJDBJe/O92sXDOmWPcIgqtEqrdALN+m3B3SAxC6KN3tUV6gPqfOi9syQkjXCCFYAUnvcaibq+og94HUgh1S+F5Ij4ydlb70yV3E6wMhNdLmon/vT3l3egN0xG2vB3lp2A+Oh8awd7xZbRVXKLirlHFkL+CuobWJ8c6VzQF8khkyeULebBUQTazeVKfwPDVbBrhhxpliO6BUy3VrPJAUWecM4m1g5zH77mZinZ/rXeqerbShrrcoLMcWIqvpcJEa8OXNGvYYGCSHzhLZprDIIGJDwYKgAJhcv+g+iaQzZUDVMTxVfa4DXqreBeS6Yq0DY/6UtXx7HWSb/cgO606KoiooATBhusgDyNR6CAPyEcKlzG5PZxTd840y84yEhweQTeFRh4EURCj9t2OQTVRheGnF6iaSEI9djIy0gsgCUNDpJsP5Mt42tMTROmSIOSSguuXofsqpThnMqdVDpqzuPMmeTQ1AohGlQzk1aa5gZiN73HyqmA4QcfS2dxKYZp2xQspWtFLZ+El9wyFW6CnYFnjjgrhSUWWvLUDWwSWUKAOhdNEgCHPj56gJhBmr2Fit8zJzs0oVKKllh0i1fogiazS9b7xPQRfcll1tGzVf/f7bXGyDi5D3UC4t8AQAJrlJtiS3exAs07EPJi8Dr0PBTdpgZYJG45nb9rg/uhVDlX3jSph/M1C+dYCseRmAzPCU2uqkJUi2IjaHMhjCaduWumd6plXPl+m9GpFsWdgiVY0ey3we06re0UXwAI7xV/OyBpDwMrOUVXE6AiVX7PF3yAGYAxBXkCT3XaylZeVgX6TQ+W0HQdPPcj13XVwHgAFs/V/3v4wJ7jTRMb0+6AYG65boYIVOHUDGXw+KcZWl1baDQCENMLieB9ZYxe9+INgXKji2rkjxeL31xlXmh0B4IMaYDtKmfdZBaRDJqHwODcchxAeFWwfIYV603tZG2n0MbW84njRxiyGKu7Juo60ik1TMbmbOpCpWU2O9NgZZN7VqndjbrxoIGr+rIl/bIf1ak2Qz8Jjbjh2XyigVeIaEJ7BA5vD9cL+CI1Dnd/fgGGLuAaRnm6rfBQNUCJTzIEUVHNnAUhcIgW/qJSspueN+zg6cdSpnr4vsReyY/zAYcV/2o/NQDXiGwh7R+ZBXO3FRQj/YPaLh4eogrX/eVk0/G8ARcHGsisu6dYL5OE7V79GcxmWBioQ6vbDOs0ZqWfd+I6SwJ3aciTov7jplZY8C6ARZ/ouFvek5jNEv2MLDqpDwHQfxoszOGl1pnnMfRpAAYmCRlEW3CNU10iR6YVmHc9JfDqqRnsXH4BR9DIAI+2G7SM1NvejDt1Jytx4UXHsdpGjJboC8DyncGkAOydyQMQa2yP29cPmAa4tA3WGV2jvRum/YIU0LNjD4fmBNTZtepIf8V5X9wRAzhb9qsLGZM61rT3A1sXUeR/nvxPyarihedx03iN0uWqvxgjlpOTJ0nTI48Oi+OaDSEj60vpbwjrEonFCY+4HVm4kd2AISXOrlFBhukjh9ECFhj9GXkSnqHgUYQdkNaLbNhemBq7hNi7qIybcyrhVgAKhLsZUwOKEXsfUXxXKPtDYygpL6RwVsDBSDbiuK8YCOqVFSMS3BKL5rSYbHKYsruflUPye9SJAGqMeSLOtae3JkaOWvtnFIPPHSUmBbLYABKBLZV1ok6edSL+YpUxdR+H6/VsFigF+iY/iGLSARGKTNmnGQ3DRiHTWGmezA5dPcmjRoJ+xEXu6OXdxTkCy2t4CL0QgsS40dRVa/Rkkel3digugXI0hSdamJxplFiHplHvQIb1/xAnfHYowhYvgGP4D2PFb9qaSJkHW1i6oDrgA56XkVs5u/KXeLgwSQ1NYsWeFQ5gHsRgNSaa9XnWSoLLTHPn0TdZsLB1EvPnPMujrKLKY/HsBiG1BsaqsXsVmbWK1zS+Go69QyXUurP3rUYeJKDFIqogUZc5nwBHYgd1MhaxzHoUVA3d/R2fitaug48FNdGKaQrEOaKJcdFPO0wTRtRbzenCBPmyqCG4sxAwGE0S19Up0rtop+Z4pyWriKO8U6pO7NIv0xlhsJo7KOCIAL6XqxMwJdEFak1mp2B2Eb4oL7ixp4armgxhOZZKQXI7Gd2jzXUYKFNXKq6w0aLpuxKVXHfF+UWFm96RxFJ6lidpL6ctA0MEX0Rhg1lrBcHHMQp9sfwHXNSrPG7wHHZQNjG6Oaa/XvzXrilVx0RijVXfPFQaxN3Sh1l3z7BuFmIraWOyNYDrsdfYxFUHiq5RCXVdzS5L9CzvSw7UDDc+2jkSyOQgP23TdW68Ako8GoHaf91ZkylTHmaYO82Qp73Gxcz2gM0sVdrBkC5G+j53L3EWMqNYnMut6i9bsS9WBQMNd4DDy08Hg2EAK45MDwSMCgodhUf0QApy7p0rN9WTTbBqIxxISoFnkO586A67lUh83qkcElUQblpGBoOsZUmaJNH/SpnGHOvOssq2HGrOTUJzIw9AYQXaxudZBeO3EfcqnIkLf4F83gtSiveM0fCHS8Y+Y38lWk9vhSvw/tItUrxZjxMr5LSY0T0eMC7i3oILXTNO45XIGzCWOatlzZZB9CLobNJmo77tuGn14CkqPAi4Nx0pqqckCLIGnrNcbVeup8a9KFc+t6jxForH3Jv63RJR5XwFtIbXrR34msMxSQDCpq/LCdBlTclmcYdYaMbHXvIrcXbgB2CjNyGhZRly6z80WpNvU1Akj7G1iXP29AZqKzqTA2vo1uBEiZwpndiZzM6p3qjCQKeaK+zaMmp1ZALGcOlaJgHsBzySLDeRwQga682uJattfwXGgHTUneACh7UuKfjONGbQH2QZcsuNdfxahGHZm7nDeD683CcQCyy/yV749euUKF0cqJi8nh/CrJaDhQfzLom6P3KqeIABGmFgartGy0NYXZMirK5Vzf1dhqW48dpTIQ0Sma+AbtdG1/dC9CNmNB7dAUAIChBoiizToBQJZnC0ApMFb/N4JaB5AGmLVny8hPAHNRlaENfBHgQieh+oUWlEYgEpidlWcAw5RlozNfh9PUGVQHJt+oqxM/3PQTG1cEIh+Aos8mANu2wnSPts+PTTX0quzAXn8VEG1Qsc/35CLmfUDNLN4jhZGefy349GXVU/u01bXkrMXV8ZAjZudIAHlF8LtqeNhTlMbN6ECUD33Ua86JchXL3FCTw86Ek67co+fWQZ29WNouESM46LtqB5VOJOqtsMqisxWNvQEWAwRdACMBxKV60OgjokKjsMp4aUfxhVpAzQZWPBEk0e3LbSFOrEY99DS0BeD5sC9Wo1bynSGrI/5WB6WwB1Azb70CIodmIKRngYp9bXi5jt184uBS/0Pzi6f12W4tpj1psPu33FFxpE8MUO66toCbhmd4LvazJ/DoZNAeF820Ee16l5CwR4wvXVb3l6lT2iwiCh0Eg6GyissL52M/DveA5nw49JqIHYPMGDRf8eoKCQKY4P5tXMEpiqSB07SsNYAHxXT5w1YMrWjgYBtrQSOt0oOJw2ocS3UKp2xjMVUxO09VnG7lOme2rjbrg1ChFRbTMnyNsF6P7/TMcQ/4PSToG4YRQxx5pTTH3L5nLkCVeLO2lf5b4zTcJn961gBkbAQrgsJxPhAUxlLwcmxmJgtrao7lJVr+JTibaWZiLKa0Vf0WEBxtuxG2FZ1rZ2ot2a1+MUjm4UIPSAZkVjji6yi3WI4TgMKgBLCtoBMA0tOrf91QEguMATJQacTJUTGSjxVSlOSXY6TNXjYAmp0djUGaemMSZ/A6QE2h3NtiWhacHVdOf1iHHYFeqMjFhxcnj27YJ2JrUbVO49ae98lFz0w4fF/svfduCFVrLYr9n8FgNOo97XXWuakuCjFcAWxpdsbSfCk+M1J9Lz7VjoiLByuDQgA8B8lkjJJaoAyrhAtWU9dBq7J/yRQ7UY4juxzlY9TxqTkktdCIHjKLy5cvlV/AJDpPYYbdR8hKNNULEQujbk3PIyY2B0S6mRYpXlJNqontIS+krNP/y9UJP+WEnLdhCmddqb1+ODBvWi1AHxgeahc/9GMPi2Kusca1xym4CAbqc2kpcvOnv7x6ftVw9X2xqb9SxSW/5Ra9IEo9IyHwTlcAUzOCxdWL3V/O66m+31vd3IYw+uRqMLbTgWUzl3e5ZmPzUUjaS2kB0MFyAI5jVFxP9GobJwKg/pcoskK3LPKIRKbv1LKKbNXLdCCud5JkY+yJCj9A5lYb2BHJwAI7t/QN8kBWzwaQGYkmpKyqjbyVY3XhSamyXEl78Ui5VPGvsqDQaLzayEXHQ35WOgIOgSIfKzQNPF473ieAliwNiZMVlQ5sbpzr03QVZNvHr24YDgdICnC3AEl0leyEIZwPCqtR4g+u1wv701aHIMRpbEa4LHpvD6ESbKSKIrQ/H0epUWPy5FPo2Pa3ayjaieO6jUMQjAnA0pjST0Pz9RObmRj1+Ua1tSKujdojhQOvOw7TxUSmhq0ew3ruBobVKqTFtQqQNb0RfHoQNHD0ldRhlnXyTxj7t7bZrJeZMhJl2aMnJWWUwYGfzHofyyZKIrWWmwyE0iMpnKCcCETC2oHWv51z0FO30EvdYNGuWNQ2oVFfoe5WeOZAFFkOlHuebQxplWD49wi6H3Zsg2RjaNvOYhrjoIvudmhyQ/ewa4bDRWyqFdzeCJVvjbgDyebhURxDZd7hleizHX2UtCiqiA0iXRFKQZDrtMlKcGicFk0DB7RtQIX7R+OFynpaVtD+jVtRRabYJADGHnXmhQNiaY7rs5Zwh9NF2knLr1+urEFIbdWNqBt0tUCr8/MBpfkWo7Ok2KvNIGAs1G67n6F+16f1dVP8BGjEV9PaYANGBlNE6ige9LxmFENVc4QiB2zw5P31bs9Gt5sGyDr1Svzr37VBk+1tk1wWtbYHJPvQM931p9ee4AEo2sC1N7JLQw/6oR/Ury9f8zbSExOuffUI4Uoi9pDsLK7SyvVnOEQ6QFKQdd065WoKpG3ZtqOcgxdq27CO3D7dh445xmuDd+vkfrla4urTARSNNRafeQFfqbqmdc2qup7S/rgZFhR5UsOAo/jMzR8/5HCrG/ajjcgbucUaQTBl2DYVlKBAZ/eSM0YgDtiavlD27XqOFbja3LPrQg1sI/hfWohMzgyXIrX9rbpm9hWHjEXqlw+qvH5HppqMY4RmYLiFeCvkszLMUTH3qdBwHCwchisZabyT0PIeYmM0UeHAuPfnb+3usro6Aola9NB21lH6nkGG2Hh4nQcjaXiqH+Q7AGyAMYBrwIZqvGWG7x3NaICxlBYc4zS0OHtmX2eue0+HpMakh5MIfO3WsnDmY2oEGNOM4OiMGAp8Wu563deVCH9jTSUDFQdGUoOK/g3M0sAlLUCSakb1b23P7QAVdaFSpj2D4+54Ubi6iAvX8rG0oQPLRKBS0woydUUZ9DdrNLVdh5aPNZC8LKzAzvA5TeiBD4awwrYdFkOjITbVhLYbU2twjLyLkNv6W/3kFcO1ROyegscZHi4NyY1xhR06DAfcWbmlJ/VsOWc6ymuaHkZd+RgxPy1o9Cncyw67xRnCnQFrlL/1a5aN6jhcR9Dq/tLqIUsVJRwU4X8PLWNaaW9DNhIAkAIoVReahNgGLN5GF8o1P1YX8dnI0BtA0W/V1dQJOVPjEpXIln4zdqtpjeCof7zO2XfGbsq8Jil03EUxjcvX251TTtRyofqMc24tz0YH6Q8zFqqJLiHLFhdHJ3glr8Fa7M/e9nsQ7Fist+ubhvBh10MCohqzQYC5qYeYnLU+eixSeWWABPZ1nsg21ivkoHDdHMa2QZVRmp6t6iuDUcZe26ODXLDJ8Kj0g76JhQctAv/1YNkCXCnihe13A1OMW5uKaM1GeRxcwWP2uLc+LiMFDSMzwBIx1SzA1Rof6LGl36bPYQUgPa2RPvZsKzlzTCkh57qKTnWLkuK1NTNj2mtG2/KxcmzUElzbRhj/9rOtprPbYUDF2AgiqbB8hqIQY1D/Li+Om5a02HIBl9brKAs9AYogKw8FwB2AZENKDvqigXJdmo25V9jsiYwP/NQ1wo0dxW88hixq44ihMWGHEX11KK3P948tzv1RdnH88hE1NvgaT7Q6V3G0itcAXKwupSjYFH+2AcdbCEtwTB2bq9sPIAIkUBlkikBZ0w0GMkaEt/2mAZ74K1aAjLs61oWEEXSM4w7srFb1t8kGHF32za2vVyxS/5qOoPvcehzNmwHzBp3hEIp4zHg/BMJRHMX3hlED6wf0Y4X+WwS4vsaP/Ubz2nWSUcXAhluuRLb+hcZXkVkWLqDltggRJOGAaOLrvp68l/esvNHmo3ZuAcEKTLYiUdjlD0uArGqD4gyyqiWMAfVppgCQ1HxL9vFZAUigMkuPKyapVD0js+5MW+A+eZcUlbEcqaOFQ9eiqoMwvTfW44RBXT/rAC7k4bao4YHh+FMNA9q0jOaImW7iXYcGceuR70pfpCouUf9cSL+ltpNU2L7d6FarrqTe6yF339BejxoWGfWqARyjH2QEyGFJuA7MOv6y89R3RlBCjvetrrGCZb/THyi5gcSKpIK/XixBWO1pevh2b9AwcIzATOayYwYcTXAVsZfQxBCvAEsg6WBUwXHQorTdAGrc6+I1CWLxLRMvCIt3+jS17y2vro27vo5H+KfKQQfFsj8cS8q7Tr+/+th+1HAFBnlJSq29Bft888agP+wVSbk7iJ1u8a/d6Dq/iTkwcKxzqqO1m4GwUjq31+IzDqIc9Js9Y+xak3aORrwK4lvvAF5YZqFQqZtMRcNMZY8GmLFjtJ+NB+xTAk3EbAqz5ts6edQpE1wUFAtx1Tcmorqwb9xIzGItksCiC/dSygLKcQBYKTZLQwoMsTLILN/2hT4ShqJsB1pSZqRgGAe0iuM1VfazRNXB1pM80MeRRebISPX1FbG/ftH9D2zUXPCKxdshjw6MoVFEPfzqhz2u7rz/4CFAyYNjXn9kETnte+hQtDwOst7eYhVR/xfOjzUYXT0N+uU+Xc3z2J+4eD/Khd5yF/wrdI9R3JUJliLgR2WWFbotPgXRxv+xmEN4SEpA8NhZ7cPkjHOVsi2z6aCoAJ96NpeDeFvBsTHoJRFDU0qug0TCovPHQqLmOOoglyJ1PDfx3tM7qmeCu+CQJSGwcquPqA9ecLCOIbfMLQzonQ5yrXFVw1qcoTNAlAjMMUMwRhvJRAT24WePxwyPHdYA2giJJ/wyALw5SF59LvYlgZv20Y2+ZoDoRoixIn0l4v7+aLSKye10kIFDto+FGTb1xqBwI5XsO/ca+PYjsONCK1IzF2WQuhuflVdhMIL+kWvHBmTvqWYF944FxnQ1YjiPm4/HFIwIjUtPz9YcsMIz6Oq/Jk3SbqjSEncvI2GuPYM0BluPJX2BoWvq3Xk8FL7pC42pWSFElcVy64MOHFFdeIxYxwEySlqsbS4MW8vA1XshDhpNGvS3jCFarQ0kYaNl+IY1Wy/4ijX+vD1L43bvX4xRD/ptpxqqSVgCPoc4mpgiBnpi63WTBtGojGzQa30obzoC3JhBrmkAh4puYzhd2q9qfe2n4LXEqAUAQtyJkWqDXjSCSwqyRuEOztbJm2qKeYtY20ev4jH3rKWokSa8XBoQZd/qoM7/pmX8HYPymTnOQmrH6rAJteejBUKf9WH6xyjOGpZ2zI0g/u5x5Ow7QJNu+0Oeh72/ZkdBi55qHJrbVtTz4UnLptXvmhuVlXcEJ69zHyhbEtCPlURYF22bt23U06IKYNmwAa/zgG/2bxjX+m+MJzksEuHx1WZ8SeJHt9dIC48Oaxturx+woo+nNibmalhyWXh460HGDnLssAKOoFEdHnLlaqF9v0P/xdBYn2MI2Ll4PReAZlCxhSDI+7Z14mK7boXv9Hw4AoZc0sYXLBHGhPZxhV48dONIWMfStq+Na1vWz+q3iFBCGiimJxRNw4Dtm4QAgrpTKxlDFMaWqBsgFoNRV+bMYijywciYY7ehliNVUyoSUxh0m7R7ArSs+5GyaSktS61Ctl1HQMv9wcYd8hGOm3txGt8+QLwSxlyl09wAt5pXl6RxmeYj4uTxALLBpv0UfcHdbgKaa6OVn/dcnZpZNMtELfjU/m8TrgD+XQLDfiTmAM6loMyzLpZjo2q0WFeH6/rJAI9NXxykx5VmFsWodUUqEnSAQayN61USoYrXaOtT+nhcBYhDjTQJ0U8GcPRzwMX8VMHQGJrHtgCqrnEEK0yz7WqYummqDjOADa3JxsS74l2U9kgy8vpbJktlifpqwMZ2GcowIFItSTlkBey2DoiNR1paA4cjahcbac67cAv85sohNFlT2UStV/ReuWk4OoPswbFvR01neabCIlE4vOKXGQq1E47XXo/6LlSmYkwSJD9CFVW5e28cKltpJFyirszFiuuvLAw3/mLQ8VXd4l5xN7DORdJi+ajerT4WAb6+72XgIjyFZ+y8XrOomq+ztslodOlAqlFf7DHQRObb5LETGsb6pTZRzfTLyCS51rVHrHnY27IUEEz3Gr/Vu7vBGWVIb6yL5vzhhWOQvlYHefNM3LqI/SgMOI9a6Duii857ABLd0Vqooik1gFI7PLWst383AGKNJ1iKKTVuN9Uo04vkCiT6PRfvsX+AbEGwPW5cd/zeckxq3GQkIVVfG0TnUT0sjAwD6WA9/Rz+BdYHs/j8SOzpxSLujlfiDeDobNK5YgCMK+r8P5TDs2ZPmkvDw67zawxQC0fvUlDKDCoz5nkGKNUFCxi6gnZkSW18EUDi34MSFvSDkRE1IJSij6H5OQb3mh4kO/ZIZPrRCJohedwnaQUcB/eWTGklDMGxr4vxoHUMIFmP91Dw7JnsAWlqmLmyQY7HV8vDh3J4KAB5HLJ7SbiFil8wACdfbee0Tzf9vhImMBmbqlqmUnRbAi6gYr8ZNKurSAISwmwQUqFhAIqtWNuKo20qJWFRf1XTTY0rjxlfErX+h9Uxu/ommusNPK2xDL1E7CuV4TVlWKmPu+gE0d3zvNKQ3IHLDFBmRNE6tEU9RrOS4rmkeaDKGGhPKsM30VjPwrcsvlAYA4mAtV5q+1h8PMRHi/IL5WKHFFolAxSmdcoAXL/TShjL84cTxt+rRqvLn23DcQDhcIDspJarAN5BA/0lzx4DZAOEXJqOtbTsS0ebB3Ygte1MTbIl75zFdY+pzCglgajIDySrQTo7QwMUa+LnSC/nIBXi8LS66BvBsRpmojN2O1sluvwE1hkAUpjpFYhYTXqjfwQqaI7qgb3nVwCO16J+sYJk6/c4ErN70Bgd1/TYOzV9/TdbY5tWiYNeLIZqzeYA8hVA49pRoUbjYLJIn6bbQJxqL+h1qiM7QS2K2wHORqlg7ZX7J3rVw0oIA8dNMeNggBwZX64aqDuj1XuXvXv4vfY7sQEdj9M2oNmgJDd3jUXalEEqDJDoHud5dushM5AyQKxO0qkgs7rPLIDRlviqrjAhAavBWYYvgCtXk4Ok7fKXW6AMOkhfrDboBZfl2rebNaYQ2zXVXzyPCKovLdtlO5I7QAEOhnHRj9KxxlLi+ZI1ribe08JuFWeta/trojWHZ4HLaorbn8Wh5VKHu1EsPtr44Dz+GA8Pm5ho8MgaCETytrc59May8Hyo24VGohtxHfft/IgY/hB1kCvN4KAWePzPPqxQR0MGCqNQASEhJdE/MhF4TkCaUWZCYvhiqdb4M2k1UcvsepDUhy4p0niTRc+pDapniw6SOcyB9r1clGkCiOyx+dJV2aMdRCZJ1GBlvSe9yURrudIe20ZmgDnbV7ZYdNaS+aCWUjurgWMMh0pOrGBs3ythDc/4/b1FgAH2sDEqXl5byP6XJLKNuFV7HCt0Eu6l3+Du74It8uLqwwi3AJCXcL1ACiIbWgtX0oUM4qFwPfax0XP9l0ZfHj3XD5ixHdfzukCsuPUUXdZM9pMu86ydm4BZxOxUZGVpZzOezSrW2halfRkOwSk2WJeD5ZMLUb0xwnQM0gwyCKzV2UwQfBeN3Ro5d51F3+0IYlUXVCbUxlj1jvU6V/bBPYPsdY4RsNYs2dZGcXlo3lcxvtTFeBvnc6dFIlVYLms7qmJ2fLYtz0GiaiPx52wlq6iHjFJVu7p+NdyNQxgGB20svltF+jp4VQ+z5SBUQ5/PfRgwuOeN6ebh6AAZGPLyRmAF8dnVsEr3Q80QDTriMkXU9rzBt0fQh4X0siql+P0495Zrwwj6KPKOyiCS2TMoRY/DbBoiWfm804VV1mjAGMXkfWVR4zCdqP3jYBgXf+iA2L9pq4l7epblumz8FRzRPeNi4kikdoykNqZ9A6cDZNXhlR4ES38Mv9+krSdnQ/2j/ezdXqcZFjc2wHNG2MXfldYSGO0Xn1xrkVHEDjpxdJ9muFzqhq5j6SD3itj95Vj2FcztCR68cdvhoYnYV9b9Wb3foBwI1iDCeHkVkf4yNLzkPWN9NirLytUEMAGlgNMsGzZRAc8zknEJSmLRJpmil5IxjSV7rFZu+/B+7GhDZfB10CIkyDJi0ZIt34h+jziQVq2UD8dTxrBlDKK/dICKDLABKTQ6yMKlGmr8vetlx+ExML3KSiNbDQahyA65Jc/RCGPnY7AEaic5rC4uk4CeucCL03VMfnipvTWANHBaXAtnl0Rw+Lci59+Tjp5FXh7x4WmwUCu1E1WUOZaiIhWxGGlQUKiITpILkvtGinGGOC/S3BpNuq7F0XK8j0VePW+XRuSNemkNbs5dfPRIrv35XhRsvqnbJyys1g2A7Wfa9jeWcvUOWL67YI9ysQHrIHeiHqwB4LGExTa0AFz7xuUi9sMI3Qj6DML3B4+j+CMUGhHQ5FgTq8DwbVq5gDn5figoDKaw3JaDHHtU+9x6/PuHNG6P2zhuy64jmLUvtSIYN/ms8Q7dZgbAie67Vwny/pL/9OAURd02bW18ZslnjtJ+cDcKTPug9Nn30QKlUlY0QBCBM6ZpcJXC74Mz7JHDH3KmbwkgD8tFZFt7nxvoRG706RsV8vjlVhzo2ZEZWRiUCsAELkVYJJkeTHSQzAmuO0MFkMaVhsLaiMnWXhyUE/cAp5c5dkq47nDkD9j++ryt6LOi5iiCIffn9n0D0FbGWKv3FlhbNtaAsP4tXAed1ggTWBpVYGy/2aoh4sBwKdMKWNg850AZfg1zrAOiDFztwcFy2Chpy6w/5LCuS1wY25oneE9mYxzAMZUGt2fFPhLS34zqD5rS3nTtKVjyf4bRjoJghQAiKAkIQlzAEymjKQWcStOpw9v1M2QLxbZLjZkxQ8T25LvyraapB0ftxRzuV2vvDEK3hYymwRyNHTTaEWIVZD0N3DdqqNN0fW5thsfewTKAUTtjBmjBsQ3V2NRZYZv7AxWOXm8796I4wvWqXrBBo2GRzLUc4stW/qgg6d4DdCCjvBQcHyZq1lKy3K6n4qrpOh7VPBpALtWASx1kG0KvPIQd7iuj2JBHKRvpKIcWgT0F24BiJ9I2p1V0qyIgACqglAQQQXJODJBYUZHr+5El2MwUYzaEFiTNj7EWJXk0Nt2uAUUOTZHNHKCdMgmIF0FCEAGFSYzks7iDpJRgeqrq5tOXWQuGJej96kBQyzXuCQOCzy6p9VHvOxiTsrigO2v+cjC+AOCGRcLjbNMfgCuWZXd8EHschprvWgsjFulFg7r8DmvZyJ3aHlZabATekKeYz+XpzZlXM1A0rDnkmQfP9wy7u9S+FgZN1KxyuLdWNVeFzcM37TLR4pIPRYhZfdbKxB+8vFIWouIAfyPJa64NQ5uf5QuDNw9gjtb5Iga0NxWkgnhVO/JSfIrAGAEpuvvEpuEdl4C4JL0DhaYj/vWnSkFJQGIDSsgKtUDQvzEIaQmQTq5NpRC+a78Akp7gUJi2b46xUzM4wfSCVoy63L5gZRtXLVYDHYRvtrrHpXFP8xeMM2vhWiDpPbkCov/XgaT5QdYkysBg3goRHBvFhHe6pY51gEH+Uh00D8+Ov7v4zogD8uLe/qLrb47JS21KPTCOOuDVs3dlBnkZAjej7srL///2rmw7chUJRiL3/P/3zrXIeSCXSIRUKrvc7b5j+lRbK5sgiFyAgxR5JTEtRvPxyiKdFUKOlxfxTo9KjVWv8iXzvIB6xjonVccpjRV6BB1dFK35ghXcMbLRBwjCHLebO3CTmC0NEPPz0w5Io9wsesgEWN7AvbM35MIOwGbPjr9j4y1Bs8qTwyK5Uqq6iLduRZ62ixBjFuLfiVkuUfMKhhKP5veaEZLSJ2t27bwD2L0MnP+lwWrq0Ye4ngnKec2Bc24LeWCO3shW6m3kmHo0uhw4gBww6DjOL7JZzp8ZDy7qawmiJ3kqbPtxqhUbnwb8Gv7/rNgOjA+R/snrFH1ADzMZY19DDwlsrRoToAMsgssIFqwx/RLdzacyiwbFPgBcc4Oqmr/8B2JTas/vBoRRXDciaYNqg/Q2pku2BukDUNLdCIDQIGORj43GZh2kPWMi9agvBz4NCWGtg7SCzQMflWVmq52cteu46GoLApuVOD1OzJgmpXwR6aptHLQPXA90XgCDeriVtTJFrcmdMpGScMnDnbDicIdn7gLQIvmoh+gxiPPl+zf63vWF58PTIvb9UGXgI6Ph0ezJqE+S485Zb6zzVB+hluhN8bLBX3RO5b9+fYCNtGQ3Qs/WJLyjsoiNwm7OxMAwGrjVG8e+578QN4lZiIxVhDI+K6kVZpShDQB2UNFxzdMvyREA1GOuegIcG0zE8xU6VY38RWcp9zO9qPAp/SxnZbpHtykG40mMPhWrjy18/YQf21BKefNBS7xT0EAwphAcpaYASv5OQJBvZlJnPKwA0kkBbuPgA8Q8sMq7MV9JmVG4qr5ZxfzsQhavZ5APLIwrdcnn0ov/LtK/Ae6llZ28w63y7AvA7oFGSJXoEAhgNNGWmi0nXd16HCSrSFUMDDoaSnFulpH2nNGq93LGRSV2/SMMIFWBAEA1cGwQHcyrk5itDlqzOFUAkvJCI+QAV1vxCDQd0jNCrGoFXvPxrAf19EJUjbrN/JxZqxPRTwDSB57VdXo9AUuXdTRHFIMibCDyCkBKGXmlppvA4fixoHEn4VbffEkHfk2YQf5Q3rz1VL6fWw/yWRI5h9X7r4j3G4aig7RCqrGDFavxINMvrkt2XgbPuOkMwxmkjjSL715kLk8KeCCBx3WR0ob7EPheU0B76iRbM3Z5tGozo5vBcV1xyFY8gRHj7uzyswZDPZxnLdewyvspi3w65ODIA8WcP1y0iTnXtQQnnahQ1vvgeDscx97vE87K+GUM8qtAbGKU3worX5UZ6vCvTroIXS59ks6SQcQNKd6yUxVAHZTAoEmDqrkjOWt00Ggj7mZ/hfSQafRAQbUiqd4oW5JRBjYGTJk6QrLxIxubKd51Dq5A0ettHB9BuPyQaoyVSqMC5rh4mjM5A0kekOa8zoMDDuWaSykrVdS365x3Q2WWXwaQd+P9K+vwqzJd+LzwxWUWXN8365eObGF+E6GTY3Acq5OPGAbYpGg2i3xsKBBgLMfW/Gw4oLsY3Yi5dgdHEDCKpJ7vlK08KKWJwpxvBnt/SCcdJcr/yG+wwMdDfJTGleju0VZwXBhbiBFqvFvderj+WQ0D+BYMh6pZBl3Ijh8lii6aH14nqeNjQcqA5/rk23rI2yBNUstHsknhC7Z9/Xh4Cqfmh18t/n8kvoeFJ/aDE2iYb5SbOv2dA4vfgDRBQxubJbYO6ABNZQ2+d/LeR/ubWGTEZWI3RNBNLxnLoqnpJYuYSoYQYpRxJJTfqfDJHM9Jt6stRn4Z6AkYrr7HCQM8PtYPYBgAMpOTSVROlcLMMBdMc4orqoiRaqoLBsUzafcR0M9pZlz8nXjA+WAPlxjDD2qRybnuJD84lP8YfAA6yecH8v4agPxq+v0IvK7SfpS3r863zhdY/ARAoFLAZZE1l864Y1UDA6A6lirrTSEdQDMO2IFdOgGSN9KML9Ow5dl6H3mxGTS7XW8iQzdpLi/D5acDvshuAKT5Slq8Ne1qMU7xfyozUUFmdmuvigkZud9N3yKaRQBXh7PlTNuZ4TiOqYtdcVi+rGzVwIB4XE2c4wWBo7NK3qiVi/Fs+Ag4/rFQMPIM7m+EWVL4ZHiNiC3Lww/FFQ33LgjOCHGRt1eCYWnCiwIFKBTdIAooOjsDKngIZ3bqPNXokeKJj/pp9Rz+iWog2QE092GcLbVOUnqKJGMhjXYUicSs1sqO7ApICxYJWuWc/Tg5DsS5bxdx8XF01GRa6RXuCF2N9Nw78m9hIAUkZ3Acx1k3ye7ODCur6/nz1YOQ6cR3BDRzkCwysJ0GLCSj5sA1dkaan9VBHtjaA7C5ErePbUxDb8wDpmKsaJ7faF4PU1LdYM/wwJkVfJ3X289QeMKK/ShmSb2OnSt11gOROolODkfVtQF09RLvCnP87Fg8x4Nji7wEyXFWOJL4uaTu0a/NZCrYnTOP0bV8ap5QfEP/R4OMziA1g2PVidWi0FHvCWwy/CwTIAfLVKmGGkCAPgCTB4qGLG/UTqHVU/LUIcTmJquJ1lAGjwM8zJ/EaWOiAA0AKdJXMOnB9MjwghVb5MbAbk0aoGB3kkXmV02iW1gz4CCbdaQ5RsRAOn+7Yw2U49I/Fs+cdvcHer2TeEdVJ8yNcvrg7oNfvs/5q3WSb9T8npSUB4YPUMuXGmnOBp3lu4+wKiKTeu63777/FcE744NK8fZbQVKSIxoysm9jMMyaGLGLylBEJUGR4/FXW4N0oIkO5pc0thTFy6KqeU16sN2uRzeesFx3mNP4mGGjvduGX+YC1BU6diObgNDPGVSz0ojPwn0XEygT0GrnYRY5fYspKP2XxplpIObBScd+5lq+geOflumUKXI7IOZsomSk+S4iFfcDdSx3tuXH8yDtIHNkczpd0xksTkmK4MNbA1L1HQxgQCxEkuMTTdONEWKKaELdC351HT7w0kuNNGeDx6fDV4LdiwP3/2yPxJKchCV/DBA6dVIGEGzDRTdtJLrVAaP4R5qeUGkf64nKFh1YCn3IBuXgwYmJQPe9MFOFQPrQV3YFxLaqbc19QK1TTizzoYjNtZDofTDkKNXRY4knIpv6zXRGA4aDWUxbVNZFujid27yO43kpuwRwN+IQyYzkGQQC/OSillblPQPHh3XzZT05gg/lY0CgwTrOK9/7MCh6xB8szvefi/0XgWMNkiKgnVcn7wTMxZsljI7jnSkZTYjYC1BlkJyNQGxTzjRoFOeMqNaOSply0Bxi/nAtAsbc69YkGNOq887W6yuQZLbjhiigR8Pn6FkNcUCdVdwPgIONV17/rA92UbmI4LrKA+p5UZusyjzXxjVE6PT3O4Yck2mwPdxPRsmccbT3zyb8fPi9APn1A9O3CnOfZ+GWidzjKkk9FcDiGTMSak5FD+mgSCJ4AcmLVI2esfjGztmleStgpqB8VzvQxzqSlUXdDQnaSumuDBb8POsFH5dvLsjxmRwUKmAWgwzyHmYxGlrfu07yqXAVz3cGy78lfA1AngHh/xE4AoCrckKqnHV4eKJKJtZyFwQOesMJJD/1SUghqN3ARnosvCtoBRzv5PcyLZEEWbF6nVA6gMop3yOYCBb6mD0mAFcQ5vMrK/cPZP194eUzaQB8GAi/NcF8onMHc1SM5bwWOshURsaFVaJVBCxH2Tkh7jt3oci+ELP9muvzmC3GU7IGUxeVyuwT1bEquSpEO4CtsE4HjEvdY6lvJRDTJKskWM5th3WTy6glajBUGAx4c21zZaaudtZBVhYZ4n25NAMlDZPeQBYLjJS88FFUwUdstMfwhDr4cVjkSez6uH+dj9PbrE+YmslL6TleyiDX8PatQY+Ddw5nWEtAvMHYPI7p0QSm8RTrHw9M0vtVdCyyLB/yRb1dqLZFYp1FoMZfQE8yb9Eqhe7jeIxF+ZRBUtWszhNzLPlXaFi158gOB1QH/D4NFFi1s4uWx6BVdIjHdOd4WMeoBIKsm4znVswxTtP1a7SZWeky0lb0/DRnUZ0X8RBODYFW1JGfm5HdSZ8yfsBGO2BAHMcLiIx3rb7t31cS89f5QRbFkESFRD3zCGyMYJnMlF40HopAQDMq4v4ANT37+Bb7mVAZOZ+AksNVzNGkrR7q68kSCyhhzXKqMwf1ZAIDflJmcLRy+JXoXMJ6yQraTYbf6nhHIqoC6OXekR0wSLK6sbi2BBhN4KhzmoWywUcMB8lwnQGxtlKfUoHfFAqlZifxV3Xsn70EyOk7FfCLvz3zqdF9J2BfBJIyqALAsOFQibluOE/nKdSGdpaXkoVpUOCOfJFQ+Racp8L0HiGaQ6S3hCNQVmBMwFzl4zPh21uxV8xgdg2Qi2f/BIWNhiEYMwTY7UbkAIr+PIMKDZfxJ34HnZ4VMsp6LHSkeTDYNEhTaFfE9jbK79TZOZElkVjYdW77LqYHIyPA8DckEpIH36iCkPp0v+mY8zemOG4RbVuNQpRfBdKHcakv1ADw+LZqAL3SOfIQp955QXF63cd/VOHOJPn5uT4+GxbsjHP2oL+cv30jMHB9piiHsSLb1ivD1+ggT174FE7N8d2NjJ/TiwqcO9GdEYiAg5u+BngQMXJgFD5mcWrKZzAuYisFQakDmiFk6CI9UYcHjcc8f+GuTkyySUM3UW5kt4rUnEvxPFn0E0SOLBGTigzwKMAMTI5dLtKgOw5iXXuCI4GvA38YoGyVIVUhsZF27ImsOXskn8WSsjmq64Rd6nms+aAPOAHjDED5HZTAUTDXh9h35qs5ct7hY8cL5x0o64quad5VyXq51U+A43PzoHEAvPrs+LNO67WQWMPrGKRTh3GC1Qf4yoIsE3sGkWcd5I1XhI8KEOskLuYjq3h1OmG9FpTZCLIDgjumxU8o6CuJF/eaSmASSJzZcokuADKLqcmSvdxUXsshMbKVJdu7SQ4q411icSFS269PgAY3+kiuKoSxaEbkK8pQe2K1POf5w9bq34ZF9APwAtGtp3Knnyqdz212wsizbDwXiL2ehoUGsOQr28NV+l7uVfmXLz+KbALKwtwfvQ+q8yfCtxexPxz+gFidJ3PiQuI1i9i1I+rUyQoohP4OpTOPZ4e1WKXZi63Ei9KBnbUlgXVXpOYytpAIbuWJDqEJRhqUamYH9kwROzm/oPxzOpVNFLbEInbvAxzRC+sL9mirC0lrsYVsjfFIV5JFMpCr5W3oZw9ENway6UffMVgzBWbp5UJc94tC6XwylHo4qnnmcMBq5Ts3QzDokwQ+GqjtH+J7BLJP4sLvAcgPZOxbxf+VgZhIiGXTD5iZDv/cUNEAX1CCtt1ippXpIFp8iv+5n0x0YL9H2Y0VWHQ9JWxZPv6bhQYOPGRBnyZmx2ytG1B6fiL0DtlaeWcsvktQSSA9s8aZQSpytZkUko7fJd4loHyEBK7uGHGP6ZpXbj4/4QPBm9oHMOIpgPwwDn01eP3F4FiZ1SwSO6hV62h2vCFuovkzY7EFOcTf0ftKx5Yh2IwDpwPjDZUDuxN9qBLAoDtB0ww+nkVKszBpAvAKgAluzL5LTg7XrQJd7za/Esye8uFM/24I4cK/3d/amL9x+ESVPgWQZ/qzuUMCMKX2LDs8kdhvYIWlM5zo2V6Ymv3vc0pdZF4wxG7Ox7YKzoE9FbG7AypGHqeV8xQppneKl0E4il9Lf6aDnEEo3v2kGHgER0RZo8xcT8tYzr8gf+sZcEsMy6JotMeKn1mPZeBR/++8TliFEezxjmKPsnSnwX5nuOVillV+QnhYVMTN7/iq8GkR+8s+wHf+sh8ORzHMt4DtvY9lv7WjYejCpHWIqrE/RWsaS2mp2FYATaB9r4A2NTAGVBdLfTkuZlWH6Yj2AMc7lj6jRXcNhH0mzkdGtvUbR/VCBEOVMRgs9L2r9FdS/Ig83zQAfCzgGiiSvjLZ45Qe5UhSFW3pef4rWPobYbhZhb8cHB8GKndRP6G2h68ER+AZN587GRHSKoWrCUdyfO53hkdlGP172ov5FemC2LQq3B2HQWtrCXx97wMce0frtGR/7+i6o+mWoArvaBn9aEyWcrBIEgFJ7LSChhtLWrT9OhXEenBDGyBpH1IgtJcNyMhTtJlYfXWFGYmIPaUxKUVlV8wzgMCAOZGNEchZbt5yI5fHy6GCljHx8khl+zV/zB6neP1Jit/dvPLH63lyJ3rcT+600JNa/zIpbam+OLsyk247m2WEr4XB8/DnrNiFX/+xXGTwj+riI+5k6+oJ70D2HA0WDlbi+qqFqBbgyWJ3S/F4LCnWDFQnsaNXRuPMTiPxBI4oMjlCj2vZWVeldHB0A4aXmUV1cfCU+ibXUMwCSkynznEhTodbUgipAczhukQJqWQ+daoAZm5uQU5VQuYt6pHAMNkjcAqSEpyQaqAOGsnAV2W1p/Rw+V43WumRizrgmN4z4VUs7gCKE0usILrOw6sZ5Ws37coB+loH+czI9Unw/EiFPcregQ9RZ5x1eyM+7v3Douois9OjAXgdvY+dB5s7Q/fqFA0HS7M6d+1M26vFutNIrFyu2hnFLQVRnHMfyORwkqBTQJTEBH7zQEWnNwoFThBTPiYqNqJzd6bMADu/R/yqw7DvJH7RywIMs0ZGnXOb5XrwgcjuHb+7sdAa7VQdUhuTEJsu38f/X+W7PrUMhz5wMupdBh7waUA7JHW/v1WCziC4iEMpwRiHvgYUObwGIL8DA/xM+Iio8RHxm0BOBGMbAzUx2nz6muxQbUPMlj78Gx0U+ed+iGjwmTSuW+x9h04AGezFmOEAkm1RrGsH8cmbbrBIzf1EZnhMn7767qgP1Hr3wYIQczXfOlJwINRMdQZHTkyVE1T62dUF2Bys2n4txOqJjoM6OsUULJLGIr6mahBIOHhnkP6tYQKocfxF4PRIHfbddJCfDR/BoJfn4bIyb1ix5453AZJaRlzewMw7Vof0hmGHVqCpscax82DzJbR6OkN3F60x3Hp6bxD0AZC9j/v7jr7vBpiKtJqbuOt7WUsb27ea9TQ3O5UKjCKTKrkCwKKSHlXiVE8o7KBYqZUYMD9/AG5mlXlNIg7OMPVy/yYkxsc0v7jnT6/BkZljxrxCkutwQjRPj6/eeRRYF3vMiDyT7S8L+f2536ZO/XeF3waQvx0cCZHvgvNXgXg0yOKsPDqYW7HzR4xxdR1sFHDfR43ZJb3v2Pe9xDHI1gDDBrX9aQBpgrFdq2Vv8oUsPi9zm5wHmzPWo3xIYFL0f5kGu/OsADKCUz6twMiScjLGpTBYyyLpZnR09ZmejcPZOOOMEsf6+aJwBaAensqJThX6oUg+Fg5NrJzo4yr9gjz+X0w1vAt6XwPizi6EOiIxDz/vY6pgh0LaYIvNnbtDr2irx/RuTKrBtXS97+i9Y39/HwBJs0yAFK23bYOIojVgrPmdourwRTyKiJWBoYLCrHtjcOVrE0ucfSlnC3NlZrXls4X80JcdVANzO/J0jgfzy/OFYxkof0XMC4CkeH4TSP4EC2djoYcPMKB/B0B+B/n9YchOo1BjKQkwYixxMMKeTt04dkoWOQCEb2Pf9wGU+3vxd0zXktGCmmym85LBQW2zrSYjbRdhvcsLZT9AgoHbr89AOf9oYAgG6brUm+B4v6qrhXnElKzV1R5uaS8U2BlgYYeIfD8CR/2NDPIQ/oq+8JuD18kH6uXLAPK3fqdCVo4N80qhW8TKF/k9jvisSxb6leL1cI9BdKYw3kyzZzABBnAsSxhkdIDiHmDZ4z3XOwow7DoAusjQPZrayffNFq06vplBAgMgQfnTKU9hPFmBJirIB7vTBBwHzRV7XLat8o2PKopZXA/3o2ac2Y0nBRMX9U+An/6QE3OklIg4W17q02Gn9xurF58J3Beobjiqy2gvZkUVl6tvxo7n/uAhF0z+WPgygFzbEb9IxzdXyMm9q3ej461ERDqt1s7LmONvtvtxJCZuD5DMTuyA2bti26hDzUyGdF1q192ZfDDIPRil2qwXQNBivUigqaBLt+umzXSAJINVFJVxDgmQAYxzPSuVuDCvMTVyCPg5EyYAUTvFmVBTF8mYP4yW+i3pBVAmGx/lMou3GrMOj6FqwZ71vqU9MegWFpnvF5D1kjLo8iDJYIkHzWzuTBMIzo/eDidE4awf/S6Y9O/Lg/WK+Mz5+ayV+7eK2N+Z+Qc4vpJFLlMxYPSOI7b7H7NI0Mf350BMzn/jQQJHN9YMQ01/f68DQBszcLY2WCoAoAHaFM2ea9Kqqw+Aw4QoapjKeYsXbAAQm2GjCmg3ILBVh1rLInhJJ8DVKc3r9fw4T2b130f5B5Puh87SWi6PBh115KuPM7CD4uQPUMGx5vVQRzoD43Ru8aRKAJTWIvzJzvRK9vhIb8jJxt9JxfGF4TZA3lrKYaZXZ69Ijvl30lNnXTfCXGGvq0AJ8BS6lAnlnzqecYNPzZ4auIl650/GcehMoyDITunHsOcNEFz/uI+/uxluBn8U9KZ4a0qbDDZgA1pvUHP/UeHZwecMkjuJQ35WlYQeEwDQfJ3K4djeVHJpNpEyn0AdSBfto45fbHH3Okog1L6H4SoNVtmxmgi0C6QJWm+QbeSlkwEo2HkPql6+T/0uXBuZYRZLdXF+/N5U2Frhh/qoLe26f9ztBfO3vYoj6uc0Lr2XcGqe7Hwy8M3eFVfhxYB5n0Hewqf5oYOJ8cn4zsOVWH14Fk62jpV3RwcpMzAu08jxzaNToHT2cZitQSRB0DuL0HFhHkhx27ukC5eFNfUO7Xswyb4nQIrCNlXYxxXZx8BjOkl3A4L4pmi8NpDXUykNlb7FY1FCzn9XDH/NwZrT85LB1Z/PeOt3GAl4nZbJLwxck1O9uz+lGIswWjUV9JbqhRae3NkuUlROULOT+NY8YCUTJBZpfz0OLe+Czp1h5nC6EHjruPsoeCL5CQ+xhiHupJHr9HfVCp4LB1QkHGScONeJlvzxgP3g+Weg56UMkit+6Ur1RWLBQ5Z4McrdDYeZctcJzslTx8zONTAo3X7SaNMTDL1TMtCqQtXE2ABGJTAgXeTuYChoqtgDYCT+QYdxSJqAgSuKqhc9Z7osgiFatzYYHcRcmIDhcWP7r0gfBhIdL3l3q+yK46WdLCXzVFiMAU93Nt1dF9vR93cwg9Q29uGB7VvTpAWDdJ0tQ8CsN5xZnR7+ZyCcREJfkUnrD/Ssz9MXaLB61fOmd0dSqo9MbZQObvWV1xK13xqeyfrvd/O56GtfktyLKfcTKU+/+R6A6HAKhc2c0dFhhyg6ABAYomlnZoTsjn3fQ6TUvmPfhy+ku/2MIEDbwJXvBhmVMS98rJq952DIIn7VSkJcMelsyw1ABo7aB0scbDEHRxVbu7I1SJdkgTNITlWmOgDcma1bVYTApwCiG6nCYPUOn7YIANIF2hpUBRve0KFjZlEb6oaSgWCIzKMqgz4OigmIvlhxZzCEmqGrp8GLPAPmtpPd5mPt+e5bV88liP7F6PhkeClAHljjCghfCI6/GWtfE0LcYSbJfpHkA6kpIrK4KKZPswiTMdKzzKAA00Ha/13sqHcI9mrF9qx5Xg+dgSQEmlTsC174HjBu7OhiUGZI6OeAGXEoDrt6Xnfdt4YAgJ66bGOQWU87lX83wNxJPMaYammqgS4dDUBvw4DkQM+f7I4MMhsOUl1g5+R6FNNHaQMy2PevAMnA+OeA6ctS/uad+K92FP/G9XoSiI05iwACiNziexDDiujcIX1HiMLaCwiwg7i/64rBIer6YhfNmGiDtu4IabmUwm6T8SLicn1grMc4ZOuMg/yihk3G9HvdmWLL91zPJPc6oq/YCKo7hdLAMK+EZNeIQQ67ka/E3mORCEU7Z0jLwWKmuohvmOJ1ittdaQk7GgRdd+qDprNj2MApd2rmm4PNMnyV2u0i6u8tYnO4yqnUTvkpLeJKSvyScDOPqsNgoYhyjs7SgO5TDceCFX3fsb+/Y9s27MS4hhg43hvTC9/D/7Hs/OcgJzJ0fmEsGWAJ6dAuQOuGUYZSalP0fHpjZN78B4ECjuEe5LrC1iBQNBmLcTQoOpr5mW4QtV0HAUDcSuRJrDTevqnYVMs0qKimg7xPuXQ1g/oKR6HIUwBtTLnsQx/azEnf3ZA83XVYgGPJkhtmKgjmt5kY/75jKGl9N0i1dT7ZPp36SHgR5izdCY9E5Cuj54m26HY+imZC6bK+lKa+AhyB77yr4atGw98uldwUhRwcXaxCg0817H0HdglA7PuO93/+ic7WVUMcVqgBwfsAhfecZhhs1NI7GAVCf7cDvQ19ZAAOTyVkF5SBjr4SkBjwQjbDSoFs29gywnLYoNgxOr77W0LEtmY1faa9i+ZskhGTdHGhXUhQ9HK6qkF7HwMG1cXQxWZdJJc0BtkF6uTRts89847ItSMlsxpiOP1UzfNolgQ8j0NH6nrSwSC7qSg6eo5rURWR8p+Ttj8WLvtzBceje9ufC/e3XHjQ6Q9j/qXVV8vfVd2pUwXxM+Gbnw5Hdaks/66C1v+O905fssPo9xo6tBSthqEm9I/7jr3tZmF2tolwR1HkEmf7TqxEEyQd1GpmCGBCrPT4lcRTXqzX6sp8F4dRQwBtJjE7aGIsBtwtf+Ii4hAvLZIBjirjWUeBniyZ7cjJMIx5dzNsBTNTdB2qBu0de38fwKNZH0ppNx4sDtM7U2Zhv8uczVNJlpNSmSSdYI9cn8zuy7faTf9pbUJ0VAm8G0ghrV8pTX8ak87epz6T4/YJC7eHlB++COd8/nPhPoN8lPojg8xcSF0/lrdzWlx2levMnIz35/fduGA3ZQKSAygzoVnkd77Cf48fkO57wyHrpsowHogMpuGiqwJoXdFauuNUtx7vhM5OT7OEqmPsdFkDnJe7IJq/4JgpI5Dm+jLXK1p9+OpEVq1NFWjDUOPbnLaYUSPpHsQVzoOJse3xZwAiL5qxMlL5Opmpf1RAJVZkB5Vt6Agtn8F/ORvixa8gqfPXn114fD1P9sucDUrD+BYsVExKaMyfMwjzh7xaH+I+dwEyTwzzU1Rn4HZPRE8Pk3PPhbg85f9AqEQ+uf3wOvz2qYZ3C/Dp0fEjQ+yrRPqTQi5B0jc3025ugyPjHeaeE0NtR9vb6Eitmc5v3GLG6KBQZmWc5FOjQWmIbD2m6XlcGV/6IQp2AK2NRXfbW4P2bZy34Wy+q81SQUt9Y2toOvwjfeHewZabMUlzr2kNrussg4dqzjMnIPQBgetB7Vkt4Ai4gWkYqGQMC9Ih2tC0Oup7WeMly9OBQYaHM485o/59F0pmjH3v5or1HjOfNBgkIKLDZmasegvpYZYE/g1Byx8AdTCmQLWQj990JP9oeBlAelerkjWtvLIqx1Pl+kAlXIDkvINcXaLhA6GMqsfRTs+etdklSZcVMN/Frjp89TA6tLQG2Xe0bUOszGMMVFXR4T51PTrrKOsFwZ9G6RAH1RgYAVJGmAxQRdCxjfndOvwKuyqaSvgaSmsQHX6P3c99amPb0LYG6Q3Yhr7SdAi2rUTmre97sEZ3/h6AaYzX9hJ3xqjdpxdmgUUxgBrO7FxsTj1m4yk6qiFZpOUepc6ca0Z9FuaYv/Q0SCf+/L1D1GYHQbGZlV17g8LqEVn/qlJA+qrlzt3g2jf4AQjf6cOsIA38O0mTpJnsFmuAtNwZq5d8dgLJV4rbX6eDPH2O4/x9IVVJvJ0A5ZuA8hCCjK1yrCfHZxnw9mNKBDVrpfrUQWM7ajKU6pgfrDpApDW03gdAFpHfXF3UF8KwjhBllIu2r8R6qHMb6PBUvShDE7io2oyBilniB8iZVVsHOxNtgFjet80MNM06xjb0kFZ/YzOzAaZRMtUqPu+7MTBikwHu1cWGP0EsoBEgd2a4yo7HekjAxVvbikLEvt+xZouOc7JiH0Xt3HtHbOX5QUK9DERBdLDMkP+n9vW58BHRaxHF1TUCw1W/0sVzjwJ/21fiypeL2H9aIpAbx6vze+EaHF1vFVI05tHNrigAc4YBALU9p12vJ+/vw61HZICMi6cAjpZW3te7Ar47c7shpeQBBA6zrkx9yiKJ2SpQ29fGV+0WFTQTmX3xC+kSjBHSxlQ+Hfq11hp6a9i0j5WGXMTWZgac3LHRReZYOd3cn9Ioky5NQ3WQopvhmNeAjTs2mBjwSmsxoDhIeQdVU4UUg41fj8EnDQoJjA7WPUGy+GrupZ5HTEOqGGs8NTIeCeU+Pu786f98h3sUaMRiUToHAnr05D3gvOjBLl8kdv/VjuJ/W1hTfxLJtRu8tdF5IAA60Ntw+QGGf6GtAJ5W4wRLacYXJwPU/ZAgmMuokVUcBJDYx6K7arNR2gC2AHAZOkkHRwdF2VPEbtuG3pvpLzeobnmvtQAsB5ydQfKdfD8LC5wr3qCHdKj8vMAAuCuaGas6BCIdbduI1V0suRZ1ZsAahqN98UvLdRiTTPcr4MWPJi8D11Wdhe8OjldhYphfqVd8JvwWgHx2YPs2A+HpR5LL07OndTqu6bioZsAkwScQmzoLhhuMM0FIAUlpA6yaO163FcOkXLmjt2eSVUfR4Scx1HSE7gsoEHQ1kb/Rijixe6KQztF2Uty2uN5MpzrAdcdmxp5Yp9F1s2qGIxf5wwl8AY6TFSX221nUBYvBaDblTwTNfVN7T+d3KB1TPWmqKLSAI8/mMR2pL8nGqgADQhf9R1ytlkdNjP8WHeNB+DYd+PPhtwDkaRedGtv6+euafrXqJVgGZKVWoofpWFfXE0DoNNVGVQmTgATqD66h4STcmmrHQiJ3y71lB/vygbg0WIkTngc9dDiaaUhuRRD5YhGcNnAWtbjMh1HgAOmsdiv5FAOdZsC59T0AtPctwLaFl/SoAPZnZP1ditX8WSogXvq1eiW5SE2AWQYFVjFE8SfdZQHJlR9kGm9Az8dfr/LIE+e76AumQtDn/VDgCI7iLujufUZAukaOkAcYPvfneWD4QPC+cW2Muhf+6Iri41vf+aKPn7kC4ZVgGxlwELLH4tvOr11EIx5ZKkGGDgvpOFzyZ0wgvp93tKHKC2fqbmKWn4/OKtFJYg3DzV1shqvK29sboEDTrWbadI+FOLqIrjBgSpef+DSS+Z1ZT9H3gYDIV+sO0XqIzq21YJCNxGw/d5E8jFBNqL9lhUWdOEAiO0bd9bAes0rCc5zsczBh7R3aMLQbIkAfqx0le577bhq1Kig6MA5dIxwc+wygrrpIAHTt57LRKcaoxOAZTPv4+DNA4/rW8/v5//L+DH4TmcjbZBiEt+1HmVuTqTr+5/SCT3mlWPhDOkhnV4uqPtDz80J+KYufxLRbYsPiC5euOGhaiU7pLBgaxqyz3n1twx5AqT0hdwBdw/a2YWsb2rbhDdap1VhwayhrhAdCNgILyQ7m4nG453QIMVMYSO69l5kqfl2pk7OI3YxFbhsBZYjfBPYt8zdm6iAYrm/AVHSuDn5tsZmXlTWYrJwbt7rr/0LU1qGDFHI3ajDdaw4E7ufojvm8w6S6m9Q+GWfUpj2GSxYtcxZVLTSAJ1xGru+0x68OnydoXxsuSM3d8ARAXqV0wPH1M0S9T8n6fOGCYd5vHxPQ+R+hfDiL43RPRt4jfh9zknbOLCcz5vDSiHxpjqZdk625z19Ylrmz+MwTBTYDVrMMD/VFy+iDSR3B0hmkmm+l6Fh7UZoM1yIZzAq26s0Y+BW7ptMz+yISnQswbG2zXyuMEQyKyCmUCMAk5udGIGelW4tjZ8ArtuhsVCjeA5OMdjHKMTyMbMqnms+nDVL+ZYFk1EoDWXXj0TKlMPSPPmumiBGzGGps7oJFLNn7B0KRgK8meiugUzorydCPDozRBlAnBoVlfkYk9m8ZcUj5c5bXR+FFWy5UPnT2guNREpKzSKdCnnyCe+I5xzd9A8176h16+SBK8ZTAfgCdnnyPdA2ZxwYuGYNlmaJGm3Dl6i9jOpyH1kyMtoibLxQhuTNixB5sMQGngqSO1WLCLYdBMkFMkUxptw3C3t95DxglpucqAPttDdv2Vhid+N6ziCQIIMkAtW3YSJ2w4Q1orm9l9QGL2QSGDI48UCh1VR3l7waS3Rb3DafyqV107fDFjkPMnkCx/DqxRQPFmH8dOT40JNyhjEscjXY13TqIwh8JDwRu7l9OQFaA+crgBOQkZ8+m9ntFbOHDR+B2ff8+OL4onLTPuHzRflcwz76ReXF0HAfHsTvhmDHybivT7Hsv77RtA/Ar9G9tb6GTPCipCTzEWJu0lmKqmO+lqRfckVs2Ha45YxI1FEMk3feO9/cd//z3H/zzzz/w3RRTDQATpQcottawvb1B2ntMU0xE1OmvMcIGbA6Iv34NdcL2hre3tzFQvY2Ra4slbxLMQ4w3t6hmoLlse6RL1V3DL3ITIFZlV1uF0+owDFa0ontx5SmL9u40y4mMNAGS2UbGb/ZnfZ79RNEORf3usvHnQnKZz8vYXweQZJXND+3ofg8cL11ULu9zNlbsdvXg+O9xdRqIlKwkExmNmRim31UXsSkrYQ3RSD9GXHIyLnOie8fenUGP8jUZe6k0caYz+wRK6WyzDtLZlrvCxPTANtZvlN7zPQtj4VdmkO+xBuO+7/GcBEDuA+T2HVt7C70jf4BRBT1YxhCjJcTyt33Hr//8B/o2wGPbNjOo+IZhzBLd0MPpzGkigDHJjELc77A19H3koyscHcPXFKjbPBxWM48dJu3e7vPabZV3rSImZ02eRcUrNnYLECkn88D9ROAe5PpoZueZncomq5R9SX0v0xdgqJNmUXt59jj8UUfxOwD36Jmr+9HhSef5eUo/UcUT5khd0vLpeRo3RdW4zGLtk9A/KXwR1Xlu73jOGB25v+TshJk9TmIlrO5sBZ4E0AaxlWTGcU8RnAoSRiNV2152N3AcIFlArm1QHUxQVaGb5mIVVl7XrDKwiwjabqK1uwZt2xgQtm2kv/xGFQivnLsVQB1MzCLeBOLup72PZSohNsMlh0he6T1cj2iNRz0s+MEiNmxxXM+1YTz/Xef8rwnzFzriGwHqIzB8gvm+SsL81081/HPBfRUXQp0BN6bOEce5KUxhlm4ASHauEB07q7gFOZZ3WLWlyUAhbWKRHodK+DGSYg8Q5rgIw0RM/SM2yfXQmmJT2Go+ahK8HAAyZ44gxPy2NWzGnKUNK/je7Bp3mNn4Qr95Bk39FNWNKkXeZt/BvlVLoY2/Vp/E6FxtKJefm2fLuLEGprsmW+FM1A9t498bCoXMKytQ9P7zG8K/d6rhN1GzMDgK/6da2UJi0Hh2umZrQ+TugFrv+a+8ixr/uFctuMzkxO7nfjLUaw/BQNuY7viblloHHVdIdADDqj70qzmfnFlj+lZ6Xnu35dGg0P6Ws1MmcBx/ZpF6yvFkoVNNbwGeRgkgF+7dhhFszBXvaNvwV/U0et9t5XJe1f0fmwb5jn3/Z4jd+56uP91B2Gk2HCsHS57B/l8fdPrrp2tw/J3htcudlTZ735P9VCcpC9pN8QMpRq/eXYHk6LCvq2TOuzhbnIGF86fA2CJVSMweN0QEoiCdoq3c3cQsqA2yKXbSr7QmYcRobpiAWc/F62n83I8w2BbNdvGsQQbSho6Qy3KmSpChnutcZKplX3sRsF0OBTY9Mb8hA2yUTgSbANqFdHeprzuw4TaYb1EJkBhdAulpfZm0cKBSGjiIXY7l3HDUQZqu8Z3AcTew9G0ffPm49H0kRipCg+U4bjaTKNxCT4FSy+GqZa9Y8zqG6jTEf1iVdK7YOOToIr+sj8zLWp7h4ypxnVrh45tdDe73w+tWFAdKZuaPMAOm+64BOPe70tp5uaxXYtOjPDKoqSIA6l4Rj/JPucZMjc7LfRX4TBtxYBQkq7SOMcCxDQuttjGlL3STBsbhOuN+geQacwBqe2umrUD5W4BxUX98yJ3asNXKwi9p6N1iloq2YKq5MRixR8uL+rJqXG4qh/jMG1oQI0cHhE6WdYwjqpzt0ll/6yhDy7FBbFZSV2CTsYo6/LH3FK3DSPVODNJE616t2KmkRQyS+Vkk2wQNoccxKhFx3XZXQHN2Pt9dRByd8IShP0xzdSeVNqP6tfZEnQ7O+joNiBAani9mBd0JL1kPMgBCHzw3MakAxkMhkhVe+WutwPFZkSQfv+FmG3izAp7suPUVOZQbpi2MMcKAsYmgA2iiBgzmuP22AbD7Hdg47rahvW3Y3t6wbRveNnfMdlGNOl6wyNwaIY5hHTNWFJJ8ZjUoBJA7yxnArE3w1gQ7ZGrcPo9SbDOqo7gL2hYh9sxWAcgsEkBMwNiamJ9ldVty9ogwcDFY+mIS5ODtRrFR26N9NcGmb9C2Q9tm/pFZH137YIn7jvf3/2L/57/BIN//+cdWNd9jEBDNjbkk2kduwiYmFYzrPMsoXihNr4aZKea12k30gDN6Huk6CTmmB0j55EfArPGw1Dm+DV/T2nzKu9MFYvpHBvkEeVqEl4jY1t1f+s6p6PzkM18dZiu1M6q4uGKQJi6IhOcIVK3Ta+xrZ5vZA30fltshqm40KNgc5234Bra3DW9vG7atmdgtOSNGMp/JUrwD+jqUaoCpxC5pVW8uhgGYi/hvm7PcDdBu7dRAx2TvHNV75D/SBf881YYmGlnZGrBZets2DDYtfjbHm8DRmevQMe6WF3edSqPQWKlcg9mpf0Nx1ynTmbaO1vYKkGaYGtvz/hfv//w3GOT7+39toWFbykzHiunO4H3g8oEsdI/whTzouz3dw7D4alQ1Z4/fTeTRs7MUsUrzANKLhx6B45yZZ8pwI3weIIOJ61Hc5MdORW4XNx+/M7/vz3zGFYiHqadEbFlco+vse1dYmAKug8zyD9VZt/Pm0/qajNW4MXa40+aiZhQMTQZAOkgEMBKzi5xYp5fpB3rmKHrPZaVbAlv/cYAWWsO+jV0Zu/jqOwmwwhVu9RBB1c6rmoF/wViNKTZfe9LndrdGxEZjsY+yL7Wq+SrSHPc9GV6CtcB9QgGYb6gv7LsCyPeie4z9ZkjnOIA3v7kPpj7QtDhn7wJnmYeW5hVXDs/x5Bytyp04OaeqHGXtWhc9aAbMeKMOjNDVbJtjVHOGNL4Z9w8spNPnwmt1kBciNovD9fhiRDsJ53F9QOROZLglYs9gWI00NYYUm+b0RmtxK67HEgDSzNfOlpQRaSZyT87f3pm2t2CSsx7S1no44F7kl4BSbVXrmtl1R3GwUnFH7oaxsfQASgdS7WoWbHjpDCyo3lDBERjMuoCiJBhKDAI5z9tBEgRwHso8aQPLfc+pf33vBSAjdyJoumFY0EcaMCs27NvlXHQ31rznntx7+j+OsqXcxLywWK6BXOotBjiWUvL42G0eidhK905A8o5Elor7KZ5RJ9f9ec0SQ//o4jXn98A0F0E8bZLa9PTp2+G3ufkw0zs7/hNx3Q5X4CgJjswK4sICepnZqYGlLTsxtkptw71FYVPyAOr8CGAD2lhv0eY6o43ZJ9zJ8sfMkfIR4veCPU514OVpzRitG5G2hq4NDQNQxmpEQJc+puupzb6htX/PyIpPER/itGDbxNQGdLxtNu1wqBSarRLExqBRt3UNybIlLO9nY3vuHIBj69De0No+Bqq+WT0aO6XFe/f9v3j/5590lncGGYAL88Uai2q40T0Zv30bGmCCbVK7vt/E16zx01MNI/1H8Rz47HSs2ab9MJ76ZB5fFP5aP8iPqho+BaDRkWV9PhI4vjSDZLHGGlhodgjfqmqs4pANyBlWsJAh42LMWbapdTxDBRUfa7YcCAkQPRMz1Vy86mDb2gAs9AbZ3Jgypuj1Juj7WCld+nBAb2K+hxwhLacW4nXjn83FDhVC/mQSsYMF+vTIYCYJhDrvCWPLtsGs2lW6GVMZexsrB7F7EuBuPiPO/T1dftz3MUT2ALkcGpwdsuoA9G1lajd/Wt/+OHwG1DT/+iD3dLQmmZVLn6uzvxYg/00hxNFw//GrdRgYHcquhUgmsUGWb7EgkAQbikEwH4Aa0DUolvxK5lkEYb0eRhodfzF2YRQYIxbfIEsmpuBH1rjFjBUEjAyUMgEkg2RlWVJiH6JzLwCaq+/QFE4CyKEmHZ1VVKGiaDaIeOxd62yZTvEm4zdAjEFOYszMujRwXLD8j1GBG+GjLKNEgJsAdlNHV+Jl9cC94Cq7eXD5aPj9APnpjzLCnxpLP2JLvBFpituKALhxy762NRJfkSb1U+ZPOLuD4E4dzdC55JrLV8K4IC4njrwMJ6GGXcYq3EPMdksy0NuKQdp/DLzmujPE6fqbAdJ/ljH4PjIVImGsxMGrx/qVZRogLSYBSGxlK30AcJ+AODY1Mx1kzL3mVcK92mjAGsB4BERn0MIvfFX4UNzzx7uAvsNIWIHv6h3nj0rXT98SiTntilF/4emjn2OUvx8gv7uUcBZkFqX9z+dE9hCv6TJzSFc+hwKfAMpPmH1krmrOHkJgSn5nT9D7tpBtM0OSCuRtQxsGbGhTbL0FePTms1lg7jW1nDloujHD3ZMa3t5+YXv7he0//8H29oa3t1/49TaWO9ve3sLNxxfF9dWNdsyaghTdAA0QU3PDcUt2bIlAhj/xBT0sX6U2addHBNimQs2fDkNTMH9jyrBryMWCXyVJl0/60fBRQjORwThWAkk+JiUkqSVvpqVYVtrZ9SfClwLkfZ3Jqioev/vo212lL3PzuZPXGRQXICl840Z0x5KnmB15FAnXxADqEKUnORrGOjmEGowZzVUez5tmsh87aILN9sKGYGxPIK7LM5DcNPrDmIYsU79Rl2cjkaGHHOtHNgPDAYobmgMjr1LebAfEroBUY93A3uyE6TSeTBKay8mFPlJ5IHLwInC00Y3Xdsx51vZUMEJWEfBalaxP9mFN4l0qwWUQjLqPmlXEJoiOE/dE1alX8QATfxzIrpggvZNv1LiAA8usksUkYqsen+GMkvtcitgzSjw3ZLwMIA+fcAE4r1Yyz7Gt4j9NMyoQLxhma8TP6z/8WXXCmHeCMvo5ncp5+WLmCTR+gY9T4wUurlEn4ObGLkLqllm7qbLRhvctFtAN0XoGyEjbxKgAFQLIbRsAub2Z5Zo2KjOH9/EiL8821U1hKrmqDgOmqi34a1tH+ErzAXm2LUV8jEnPOBbWGOUp4FgYpIOjY28FpHm4zVNqHC51iKcGW1/S86m5f448cr1Ztdb6AvsmHvQjq6Z0uEDguABPXb1H716BJPtUA77EIaIO6mya5zDoNX6QPFzy5SsG9yoxoozqHwThxStPO59fPD77oNHFw6tKF/j6KjsFhxd4kBGe5ox+CdLHFnsEydySdcyBhhhANgwAdJcZ789xGPCRuVBnI6RnEoT70vb2C9uvX3hzFmkgGTNo2ti0TGHO9rEHDQ8omZFYbizW1uR1G/dhqHG/SasbWnsnylBwzYl8pFtF52psWqzYI3L4hIMVYnGVP5eWW0z4mEE+Fc7aGiYmOIPVA0ZZpxaC6pefUx7DMtFi1T4uqiFElccfr4CJQT5ZF69hkDOLBVYXXha+2t3hlbEv1SBnDWlij2chmIMsPnyRKogxFabDI/L03ip9foY6tIvYog0iG4BxHGsdxvuJ4jnP28/9GY0NyeIDuEi6DT3kr//8B2+/fuHtl7PKN7Q2wFLMJaq14Yozoql6Wy+gWHpJaw0kp021OpwFM8B7/XrHTAOLz6F2PaKL1T5ffWtbnek0idlllPtQG+cGVBuTGNv1rH/WFfIQHkbIjHFFFhS1UR6Zol+YuG285kzyXLx+PrxoV0NERqSe1ifioz/3dVYi68vF9SfiezlAU7WsYmax2mt5nQNrZGY1lrnRUmf3xyuYrqOsaSPAsbimGDgKiTpZNDIZ+c6DIombfXSasY0DpR0A+RZ6R2eOG+kew/8THV2ZlYHyMBdKMWa9TwNI2aO6k3rA8lZlO7jPJjCWpBNpUdpYsoz+ygIcs0oTdOPio+DvAAaA/q7GTh4hghKjnKOev/5hTJ9YY21IFkN8+mM/18ifvzs9smh+zjiV0vRvkfl0lujlEjquWR7lfw57vomR5vuFp0Xsz6RFxyxSFcFKFg/fCDH/eMEis3FejLRlcKrsyIGy2Wo7Y31LWtUcBo0yASStIBQdxzoCr3WpIjTP3MGRXXukrIoeYE2sjLFGJPPnP54LLCHyDUaZ7FJzhbIQK2LM5XgAAAmsSURBVO0bNZsV0wS+zhKL2AmOw8m9iNWog9549xPt6iBim5nvBl18KtXFwJvH8d/6mfLsmciyen2efpgMU+xLjvKniK3qNatEKJ+r37/LUfzzjPn/IDAbmn4AiZb27I1KZWNDdGnzexTFWKEGY/8a34mbffwA2AIcDmITQFq2etehwtSx3JlCbL3LN/ttIVKPqYUbmrRgY0Dup3PwLwSDUT0Psjk9V7avoDqMjioyAFRbsP+ifxRbzJgYY/yddZA/4UZYgCzI28CIQBWx8TQocnhiPcjz8Ns+7/9BO1qK2Lee8hHWG8ZgP7FDn87uLd7Y5uiEwK0Co5jOUdEg2oe4SOs1spNKiNaF0fHyagONGHtGG0/RXGWeObMZwGwpVgeLHC5FzFar+JoAFosR20nvw9d9lKBRpQy4772brtXA23Loq72z4SVXdj8HxyJKvwIcvytx0JPjT0e6lMe9ARFRPFlM+4m6esKKfVFCkdQt2PkltT+qKA73P/y9i1XPlLZ+y8Wt5NsfS+Pqtc9JSNMFqfcmIAOqfgVmVLCeHCKiFlcWWtJrsqRHwyIRuoquYxk2uM7RoJCP5hwWoKXzuXzA/EUc1Ugkt/MDQ0TmE+VeTY+v+bYWDlzQZl1Po80MkdzeM+fx2ODL8sh+jbzthTPbBMej+F/kf/u76hIPseUrwHHVd1c6x2B1eX1dhrya4vLTmVrkRQDRaDvxN6CImCSkZPlO+PSK4kIoPUb9kp/D0xbZa78p1bZyp1PziYpzz4Ivi+QaDC9fdvEFsaonwmfXpbkcW+jdy6XrSl9y8TRz6+zR/fdsMAW6QIXnB5tOTVn/VvOSQJYGkNaa6XSGMQTagO6s0cBJvQmWphoxi9V91F8MWFYWpZWZYODo4NxmoHRITgBSFTPS1FkqHbm/TxhVCpUcC/2qZNa6CJp2dJGxdmRLgMzvWQHybDqkp+nTIR2g/fjqsz/sy3o4uP/uM6nOKprF44e3Fs8dgPJWXk5YY0RqIOmiNlwicZUIN7TnauXTOshc4S6PcDj7d4U/W6rz1AdDFIzlIWC7BwhUdqi0iU2atTYU5bNIncetNfQmtlo4bKsMW4AimAQPM96cfWaDv2MDanlm1Y+qY7brIwNVCbwLWBJLRZQlQVGbjF3DRMbKQzIWIu5dhxqxC7TvBrYDEPdOq493AsgxHqQO1OaO/9pSHTCWZ9sO6or4gr9J93iABFld/FSMn3vlFlByRCuEdvJlyCOJhVHNK/P9g3CfQV7ToEBx/yvwHHKGNJ7XQwR0RoPUKOhRka0ZVWFexbVlErH94SFK0sgDhMd9duM5e1MeXwWTJe9yuPboRRcZ2CVilLjHBAJtg1n6HixpxBkvHwQ7SdYmrUH6AEk1XRzQAmhiP8MicWn0wbGSDw/ctnAssQ51x3LUdlG1mlZmF6VDBK/GIxa5pSVby2uCpuaSg2ZtYazxqDJA1J3EuypaH1vYaojYXhbXddHivbwcm7SDWB3rc4KbVNb+aJ4sAWU694JOh1nJMYYB2VcXrwHnJOuKx93I0XpALP2Vbsry4ZNjA5RwCs9IdMKDL2OQl9Fa5kRt7T+Ts7XI/yPD/vylWEGj7GMeWin/YxHbOi+L2DOO28MZ7QowV+8cL6+/x9mLc+uY3jgMLCBgqg7NA//7GBC6Qtukg7SfS73ibYzYTrIwCVE7N5gxgDK5NNIsQJjn8V3CIuN5Z3BMByE/H68QKFL9lCuWFw2GSWAJRJlc9wgDS3UjFhDgq7HQhqK3NgYWW4XIt3Gwaivgl/PCyWATi9/SIH/y+fXy6BWBUWdBXEpm6jVdXFsGPSuHlmgPsO9QsQTGO2G8nFEQSNJAEWndDJ9mkAOANAonqwx5rkLMysa/jPNuptYZ5cwdGWRUIn0NCT4QlXsA5vmEmd8yx/e/MA8GDx/iFIglqLNHY+0dxpBsdoh0RUyt09RBOlZ5kQ5W4MIkGyDb+I624nkO2CQ6F5CkaYdekG6IjGkqXwCPsTmqxuS5kVEkRLohZjLO2DlcxO5jEd+tjzU01QaRhm0wYjXddEMYtQp77M54a/cO52/ZbJXwtdUanu3yDSeGg2PLeUhOHj914/1Hz6369OO4C+fT83unwUdweiH9V6cH3UDJaU0c7cDZHoTP6yBZse7GGqH5kDwk2OTQJWH7UOIPIvqAzuFvDAmU3jjGscLYvLqLT0dsQVAEnnk0r6xRtAXjGs3T9p4Jg0lGMQBXc9B0kOwDiaVpPhOA4M8SgwzmWXNYciuZBT9P8MzDar1GLK4hEZlTaIWhI8ZKRBUgVwwyWDakgiIQ54svVuq6GBnPn/xwuIzjDmC8OBOfi271NqGhuuojRBRqIIlBd8Nrtn0lkFyd14fxInR8XTwfztLZS0+3gOzQtzNiHXse2A8uFJqAyItHhKhbcsAYQ0YPOo/9quGMlwXdzEv0uwCgzF9IHXDAq6J1rZeM/5ieTL+UArgupfw1VkzHyWqdqTjDBqT3sXybDmOOeB2XbDogtoMD+EF37rXDzO+7D+JPsq6r8HpwnO9f1+Wz6X/bmTSvxNGXR/5VGXsm3rnRhhibYqpTMbVOX3721KN8FH9IB8lxI9njIhTZYcIAV2BMEheuc7UCxjnLkjDJIjadq4NjZMgcwxsNUw6QqiaKawB7bCU7584Yd3HfoeO5cjQsaMvC/oRvEj4OkF+KYCdRP5Pm2bNfnO9vEYi1lbnYAMKCfadnEigGwLBcW+Tbrwor1gisQPLybcpuADyJ9o2jEhnzsnXshCgyfCEBmwrpetV4vBqEcAWOEBQ3jZ/wrcPHAfJPgMyzDOuzcXyH8OSgoKXvVY7GUt0jxdSZAzMLs3nx1ZV6D/xWyLdkbjK/RiUQvmaBKG8zMGzGnHO3mqzGkmaI7fU6RW46WMGRh5bHvm/4S0nGR6r0ZXOxlWrNNUags5/wwfBU9c1CKnfjcb1i400myXkxUHqhWupBgvcuuUqRWd3x+Qq8BbzU/BbMsOjeD+lylvpNaBqYWFqPaZmgi8tSPQBHj/i7guS36tJcSXOluRrHpSf/vvfDa4w0QGlEP+H3h/tQ96hjztrBvL4Wdf9AuEkw68M8eGPRl8YFiYfct3OwycOsDKBEQNAYA0hGvVg0IRD9u6Lg3xhm/CGi4GqnJ63YoncWi/sJP+En/IT/w7By0voJP+En/ISfgB+A/Ak/4Sf8hNPwA5A/4Sf8hJ9wEn4A8if8hJ/wE07CD0D+hJ/wE37CSfgByJ/wE37CTzgJPwD5E37CT/gJJ+EHIH/CT/gJP+Ek/ADkT/gJP+EnnIT/ATrG9w3o+8mVAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[5, 9, 1, 3, 2, 5, 5, 3, 8, 5, 9, 4, 6, 4, 3, 2, 0, 7, 6, 7, 2, 3, 9, 7,\n",
+            "         9, 2, 6, 7, 9, 3, 6, 4],\n",
+            "        [2, 1, 9, 5, 6, 2, 7, 5, 2, 7, 1, 7, 2, 2, 5, 6, 2, 4, 5, 3, 4, 5, 1, 5,\n",
+            "         1, 6, 5, 5, 1, 5, 2, 6],\n",
+            "        [3, 2, 2, 7, 5, 3, 4, 7, 1, 4, 8, 2, 8, 7, 6, 3, 8, 5, 1, 4, 6, 7, 8, 3,\n",
+            "         8, 3, 1, 3, 5, 2, 5, 2],\n",
+            "        [6, 8, 8, 2, 4, 7, 2, 4, 0, 3, 2, 5, 5, 5, 2, 5, 1, 2, 2, 5, 5, 2, 7, 4,\n",
+            "         2, 5, 2, 2, 2, 7, 3, 3],\n",
+            "        [7, 7, 6, 1, 7, 6, 3, 2, 9, 2, 0, 1, 1, 1, 7, 0, 7, 3, 4, 2, 7, 6, 2, 2,\n",
+            "         5, 4, 9, 1, 6, 1, 7, 5]], device='cuda:0')\n",
+            "tensor([8, 9, 7, 5, 4, 0, 8, 4, 0, 9, 3, 4, 8, 9, 6, 9, 2, 6, 1, 4, 7, 3, 5, 3,\n",
+            "        8, 5, 0, 2, 1, 6, 4, 3], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[20.4688, 20.3438, 19.1406, 19.3594, 18.0625, 19.0312, 17.1406, 18.7031,\n",
+            "         27.2188, 19.0000],\n",
+            "        [20.8438, 24.1406, 20.5000, 18.7344, 21.0938, 20.3906, 20.8906, 21.1875,\n",
+            "         22.5625, 26.7188],\n",
+            "        [21.3438, 21.3281, 22.2500, 21.4688, 24.8125, 23.5625, 20.0469, 27.2188,\n",
+            "         20.8594, 19.5000],\n",
+            "        [19.8906, 20.0000, 21.8125, 22.1094, 19.1562, 26.9531, 22.0781, 20.7188,\n",
+            "         19.8438, 19.2812],\n",
+            "        [19.9375, 19.3750, 22.8438, 21.4062, 29.9062, 22.8750, 19.0938, 24.5781,\n",
+            "         19.2656, 19.1406],\n",
+            "        [26.1562, 20.9688, 23.4844, 20.4062, 20.8125, 21.0938, 19.3906, 21.5156,\n",
+            "         23.9062, 19.8125],\n",
+            "        [20.8125, 19.2812, 19.5312, 18.9062, 17.9688, 19.0312, 17.8438, 17.9062,\n",
+            "         24.7656, 17.4688],\n",
+            "        [19.4375, 19.7969, 22.7031, 21.1094, 29.4688, 21.7969, 19.9375, 22.5000,\n",
+            "         20.7969, 18.7031],\n",
+            "        [26.2344, 18.0938, 22.5312, 16.4844, 17.8438, 17.0938, 15.9375, 17.3594,\n",
+            "         20.3438, 17.1875],\n",
+            "        [23.3906, 26.8750, 22.1875, 22.7812, 22.1875, 23.0156, 21.9531, 23.3125,\n",
+            "         23.6250, 26.1094],\n",
+            "        [20.8906, 22.9219, 22.6094, 24.0781, 22.7969, 24.0156, 23.9062, 23.2031,\n",
+            "         21.7344, 22.2969],\n",
+            "        [23.2656, 22.6719, 26.0156, 24.2812, 25.5312, 24.1875, 24.2812, 24.3438,\n",
+            "         22.3438, 21.9844],\n",
+            "        [19.2188, 19.1094, 19.6250, 17.6562, 16.2656, 18.4531, 15.9141, 18.4531,\n",
+            "         26.4375, 18.9531],\n",
+            "        [19.3438, 22.2344, 18.0469, 17.8750, 18.1562, 19.3125, 17.7500, 20.5781,\n",
+            "         20.8594, 24.4688],\n",
+            "        [18.2969, 21.4375, 23.7969, 21.5938, 18.4062, 22.4062, 31.5000, 18.6719,\n",
+            "         20.4688, 20.6406],\n",
+            "        [20.2969, 24.5156, 20.3906, 19.0938, 19.0625, 20.6406, 17.9375, 20.9062,\n",
+            "         21.0938, 27.2031],\n",
+            "        [20.7344, 19.8750, 27.9062, 21.1875, 19.9062, 20.8906, 22.6094, 20.4531,\n",
+            "         19.7500, 19.7812],\n",
+            "        [21.1719, 21.5469, 26.5469, 23.9844, 23.1719, 23.6875, 27.0938, 22.5000,\n",
+            "         20.7344, 20.0156],\n",
+            "        [22.3906, 27.2344, 23.1094, 22.1250, 21.6406, 22.7344, 20.4531, 22.2969,\n",
+            "         21.5156, 25.2500],\n",
+            "        [21.4844, 21.3750, 21.8281, 18.9062, 28.7812, 21.3594, 18.2500, 23.2500,\n",
+            "         20.6875, 21.5781],\n",
+            "        [19.5469, 21.0625, 21.8750, 21.0469, 22.3125, 22.1719, 18.5625, 29.3906,\n",
+            "         19.2969, 20.8750],\n",
+            "        [20.2656, 20.1094, 22.6406, 26.4844, 21.5000, 24.7188, 20.9531, 22.0312,\n",
+            "         20.1875, 19.3906],\n",
+            "        [18.8906, 19.3594, 21.1250, 24.7812, 22.4531, 25.6094, 19.7969, 22.4531,\n",
+            "         19.0938, 19.4531],\n",
+            "        [19.6094, 20.4375, 22.6250, 27.0312, 22.3438, 24.2969, 21.6094, 21.2812,\n",
+            "         20.3125, 19.1562],\n",
+            "        [22.9375, 23.7812, 21.7656, 20.5312, 21.0625, 21.2969, 20.0156, 21.3906,\n",
+            "         26.5469, 24.1250],\n",
+            "        [20.2031, 19.5312, 22.1719, 23.1250, 21.6094, 25.3906, 21.0312, 21.6875,\n",
+            "         20.0000, 19.2031],\n",
+            "        [29.7188, 22.4375, 26.4688, 20.6406, 21.0781, 21.7188, 20.6250, 21.6250,\n",
+            "         24.4531, 22.2031],\n",
+            "        [21.4688, 19.4375, 28.7969, 21.3750, 19.7344, 21.0938, 22.3281, 20.0469,\n",
+            "         19.5781, 19.3906],\n",
+            "        [17.8125, 26.0156, 20.0000, 18.5938, 17.3438, 20.4219, 18.3281, 17.9219,\n",
+            "         18.9844, 21.1562],\n",
+            "        [18.0469, 19.3125, 23.3750, 21.8906, 20.4531, 22.1406, 26.6094, 19.6562,\n",
+            "         19.3750, 18.0781],\n",
+            "        [21.3125, 21.0469, 23.1094, 20.8906, 28.4062, 22.4531, 19.4062, 24.8281,\n",
+            "         19.7031, 21.8125],\n",
+            "        [21.0000, 20.8750, 23.9375, 27.5938, 22.2656, 24.4844, 23.0312, 22.1719,\n",
+            "         21.7188, 19.5938]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[8],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [5],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [8],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [6],\n",
+            "        [9],\n",
+            "        [2],\n",
+            "        [6],\n",
+            "        [1],\n",
+            "        [4],\n",
+            "        [7],\n",
+            "        [3],\n",
+            "        [5],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [5],\n",
+            "        [0],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [6],\n",
+            "        [4],\n",
+            "        [3]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[8, 9, 7, 5, 4, 0, 8, 4, 0, 1, 3, 2, 8, 9, 6, 9, 2, 6, 1, 4, 7, 3, 5, 3,\n",
+            "         8, 5, 0, 2, 1, 6, 4, 3],\n",
+            "        [0, 1, 4, 3, 7, 8, 0, 2, 2, 9, 5, 4, 2, 1, 2, 1, 6, 2, 9, 7, 4, 5, 3, 5,\n",
+            "         9, 3, 2, 6, 9, 2, 7, 5],\n",
+            "        [1, 8, 5, 6, 5, 2, 2, 7, 8, 8, 6, 7, 0, 8, 5, 8, 3, 3, 2, 2, 5, 2, 7, 2,\n",
+            "         1, 2, 8, 0, 5, 5, 2, 2],\n",
+            "        [3, 7, 2, 2, 2, 7, 1, 5, 1, 0, 7, 6, 1, 7, 3, 7, 5, 5, 5, 9, 2, 7, 4, 4,\n",
+            "         0, 7, 1, 3, 2, 3, 5, 6],\n",
+            "        [2, 4, 3, 7, 3, 5, 5, 3, 4, 7, 1, 3, 9, 0, 1, 5, 0, 4, 0, 0, 1, 4, 2, 6,\n",
+            "         2, 4, 9, 5, 8, 4, 9, 4]], device='cuda:0')\n",
+            "tensor([3, 9, 6, 9, 8, 8, 5, 8, 6, 6, 2, 1, 7, 7, 1, 2, 7, 9, 9, 4, 4, 1, 2, 5,\n",
+            "        6, 8, 7, 6, 8, 3, 0, 5], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[19.7500, 20.1250, 21.8438, 26.9688, 20.4375, 23.3594, 21.2812, 21.2812,\n",
+            "         20.1094, 19.0312],\n",
+            "        [17.3281, 22.0000, 19.2188, 17.3594, 17.6562, 17.9688, 17.9531, 18.5156,\n",
+            "         18.8438, 25.4531],\n",
+            "        [18.6562, 19.1094, 22.5469, 22.2188, 22.4531, 21.5312, 25.2188, 21.0781,\n",
+            "         20.7812, 18.4375],\n",
+            "        [19.1094, 24.9219, 19.7344, 18.9688, 17.2969, 19.1875, 18.6875, 18.5938,\n",
+            "         19.6406, 26.2812],\n",
+            "        [18.7969, 19.4844, 19.4219, 17.4531, 18.4531, 17.8906, 16.7656, 18.2812,\n",
+            "         26.1094, 19.2344],\n",
+            "        [19.7344, 19.1719, 19.7812, 18.6719, 17.9375, 18.1406, 17.3906, 18.5156,\n",
+            "         25.1406, 17.1562],\n",
+            "        [19.5781, 19.9844, 21.2500, 22.1719, 21.9375, 26.5000, 20.5312, 22.9375,\n",
+            "         19.0000, 19.4219],\n",
+            "        [19.9688, 19.5781, 21.0312, 19.8594, 17.5156, 19.3906, 18.7188, 19.6250,\n",
+            "         24.9219, 16.4219],\n",
+            "        [19.8594, 20.1875, 22.5938, 22.1875, 19.6562, 22.0469, 24.7969, 21.3750,\n",
+            "         20.0000, 19.2031],\n",
+            "        [18.5156, 18.9375, 19.2031, 20.9062, 18.6094, 20.6250, 20.5156, 19.3125,\n",
+            "         20.0312, 18.3750],\n",
+            "        [23.0312, 21.7188, 26.7656, 21.5781, 23.8750, 21.7656, 24.7812, 22.8438,\n",
+            "         22.0625, 21.2188],\n",
+            "        [18.7812, 25.3281, 19.6250, 19.0938, 18.0938, 20.1406, 17.9219, 18.9062,\n",
+            "         19.4688, 23.3438],\n",
+            "        [17.7969, 18.8906, 20.1719, 20.3438, 21.2812, 21.1406, 17.8906, 27.6875,\n",
+            "         18.4688, 17.8438],\n",
+            "        [17.7969, 19.2188, 20.0625, 20.0156, 20.1719, 20.8594, 17.0938, 26.9844,\n",
+            "         16.9844, 18.5781],\n",
+            "        [18.9219, 27.0938, 20.5625, 18.5156, 15.7969, 19.8750, 18.6406, 18.7344,\n",
+            "         19.5156, 23.7812],\n",
+            "        [21.9375, 21.1094, 25.4375, 21.8125, 24.5938, 22.1562, 21.5156, 24.7812,\n",
+            "         20.8438, 21.2500],\n",
+            "        [20.9844, 21.7500, 22.5469, 25.0625, 24.5000, 26.2344, 20.5156, 28.0625,\n",
+            "         20.4688, 21.0625],\n",
+            "        [18.5156, 23.9688, 18.7812, 17.5625, 18.0625, 19.0625, 18.1562, 19.6250,\n",
+            "         19.2969, 27.0938],\n",
+            "        [20.2500, 24.4844, 19.4844, 18.2500, 17.3906, 19.2969, 18.3125, 19.4531,\n",
+            "         20.6562, 25.7188],\n",
+            "        [19.0625, 19.5469, 22.5469, 21.3438, 29.8750, 24.2188, 20.5469, 25.3438,\n",
+            "         18.5156, 19.2188],\n",
+            "        [19.0938, 19.0938, 21.3750, 19.3281, 28.9375, 21.5312, 17.4844, 26.1406,\n",
+            "         18.4844, 18.3125],\n",
+            "        [19.1094, 25.0156, 20.2031, 18.9062, 18.9531, 19.5625, 19.2656, 19.2500,\n",
+            "         18.7188, 21.8438],\n",
+            "        [20.3281, 17.4844, 25.9219, 18.4062, 20.2188, 21.2500, 19.3906, 18.6406,\n",
+            "         20.4375, 17.5156],\n",
+            "        [20.0156, 20.2656, 21.8125, 22.2500, 20.5312, 27.2969, 20.4531, 22.5000,\n",
+            "         18.8281, 19.4219],\n",
+            "        [20.9062, 21.8125, 21.9844, 24.1719, 23.9531, 24.7969, 23.1094, 24.9062,\n",
+            "         21.1719, 20.8438],\n",
+            "        [19.8125, 17.6562, 18.4375, 18.3438, 17.0312, 17.6719, 16.9375, 16.6406,\n",
+            "         25.1250, 15.7734],\n",
+            "        [18.5312, 20.6875, 21.2812, 20.8594, 24.5000, 22.8125, 17.2656, 29.3594,\n",
+            "         18.2969, 19.6406],\n",
+            "        [21.0469, 23.3438, 23.6562, 21.9688, 20.0938, 22.6719, 28.2344, 20.5156,\n",
+            "         22.1250, 21.6875],\n",
+            "        [19.6094, 20.6875, 20.6562, 19.1875, 18.8281, 19.7656, 16.9062, 19.7500,\n",
+            "         27.1562, 21.1094],\n",
+            "        [19.7344, 20.2344, 22.7344, 25.9375, 21.9844, 24.1250, 22.1094, 22.1250,\n",
+            "         21.1094, 19.7812],\n",
+            "        [28.4688, 21.0000, 24.1562, 20.1094, 21.1250, 20.2500, 18.2031, 20.6719,\n",
+            "         23.6875, 21.3125],\n",
+            "        [17.7656, 19.0312, 20.5938, 21.7500, 22.9219, 23.7812, 18.5000, 24.6562,\n",
+            "         17.2188, 18.4844]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[3],\n",
+            "        [9],\n",
+            "        [6],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [5],\n",
+            "        [8],\n",
+            "        [6],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [9],\n",
+            "        [4],\n",
+            "        [4],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [5],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [8],\n",
+            "        [3],\n",
+            "        [0],\n",
+            "        [7]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1;30;43mР’С‹С…РѕРґРЅС‹Рµ РґР°РЅРЅС‹Рµ Р±С‹Р»Рё РѕР±СЂРµР·Р°РЅС‹ РґРѕ РЅРµСЃРєРѕР»СЊРєРёС… РїРѕСЃР»РµРґРЅРёС… СЃС‚СЂРѕРє (5000).\u001b[0m\n",
+            "        [26.9531, 20.5938, 22.8594, 19.4375, 19.9219, 20.0469, 20.4219, 19.1406,\n",
+            "         22.8750, 18.9844],\n",
+            "        [18.3125, 19.7031, 22.2031, 25.4375, 22.4375, 23.5625, 26.0469, 20.9062,\n",
+            "         19.4219, 18.7500],\n",
+            "        [27.2656, 20.6250, 23.4219, 20.2188, 19.0312, 20.0781, 20.3281, 20.1562,\n",
+            "         20.9844, 18.4688],\n",
+            "        [19.0781, 25.3594, 19.1562, 17.3594, 16.9062, 18.1719, 18.3438, 16.8906,\n",
+            "         19.0781, 20.5625],\n",
+            "        [18.7188, 19.5156, 20.6875, 27.0312, 19.9219, 23.1094, 20.1562, 20.5312,\n",
+            "         19.3281, 18.0625],\n",
+            "        [19.9688, 25.5469, 18.6875, 18.2969, 18.6719, 19.0469, 18.1875, 18.6094,\n",
+            "         20.2969, 23.3125],\n",
+            "        [18.8906, 25.3906, 19.5312, 18.8281, 19.5156, 19.3906, 20.0000, 17.7969,\n",
+            "         19.7500, 21.2500],\n",
+            "        [19.7812, 20.3281, 21.7500, 22.7969, 19.8281, 27.9531, 21.5781, 22.6250,\n",
+            "         19.6562, 19.9844],\n",
+            "        [17.2969, 18.6406, 20.9375, 22.0781, 19.9062, 22.2500, 19.9219, 19.3906,\n",
+            "         18.1875, 17.8750],\n",
+            "        [18.9375, 19.2031, 22.8438, 21.1719, 27.5312, 22.7188, 18.6719, 25.2500,\n",
+            "         18.3438, 18.2031],\n",
+            "        [19.8906, 21.4844, 24.0000, 28.4375, 21.7500, 25.8594, 22.3906, 22.6094,\n",
+            "         21.2031, 20.0938],\n",
+            "        [18.8906, 19.7812, 21.8594, 27.4531, 19.3438, 24.3594, 20.0156, 21.6250,\n",
+            "         19.5625, 18.7031],\n",
+            "        [23.1562, 22.7188, 22.0625, 19.6406, 18.6250, 19.8125, 20.4219, 19.5938,\n",
+            "         26.3750, 20.2344],\n",
+            "        [19.9844, 25.4219, 20.0312, 19.3594, 19.0625, 19.0625, 18.2969, 19.5469,\n",
+            "         19.4531, 22.0625],\n",
+            "        [18.7969, 19.3438, 22.0625, 19.4375, 18.3750, 19.6250, 21.7969, 19.6250,\n",
+            "         19.7031, 19.7500],\n",
+            "        [19.7188, 20.7188, 24.0312, 21.7344, 21.0312, 22.6250, 30.1719, 19.5469,\n",
+            "         19.5156, 20.4062],\n",
+            "        [18.5000, 24.8281, 20.1875, 18.6875, 20.7188, 20.0938, 19.4688, 18.9688,\n",
+            "         18.6406, 24.5156],\n",
+            "        [21.1406, 19.6562, 20.4688, 18.1719, 18.8594, 19.4219, 16.7656, 19.6562,\n",
+            "         26.9219, 19.7031]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[2],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [6],\n",
+            "        [8],\n",
+            "        [1],\n",
+            "        [9],\n",
+            "        [6],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [3],\n",
+            "        [0],\n",
+            "        [6],\n",
+            "        [0],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [1],\n",
+            "        [1],\n",
+            "        [5],\n",
+            "        [5],\n",
+            "        [4],\n",
+            "        [3],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [6],\n",
+            "        [1],\n",
+            "        [8]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[2, 3, 2, 1, 3, 8, 6, 8, 1, 9, 6, 7, 6, 3, 0, 6, 0, 1, 3, 1, 1, 5, 5, 4,\n",
+            "         3, 3, 8, 1, 2, 6, 1, 8],\n",
+            "        [0, 5, 6, 9, 5, 0, 4, 2, 9, 1, 2, 4, 4, 5, 8, 3, 2, 9, 5, 9, 9, 3, 3, 7,\n",
+            "         5, 5, 0, 9, 6, 2, 9, 0],\n",
+            "        [6, 6, 3, 7, 7, 5, 2, 1, 2, 8, 4, 2, 2, 7, 2, 5, 8, 2, 2, 8, 6, 7, 2, 2,\n",
+            "         2, 2, 1, 2, 9, 5, 4, 2],\n",
+            "        [8, 8, 5, 2, 2, 1, 3, 7, 4, 2, 5, 5, 5, 4, 1, 4, 1, 8, 7, 0, 8, 2, 6, 5,\n",
+            "         7, 7, 2, 0, 8, 3, 2, 9],\n",
+            "        [5, 2, 7, 5, 8, 2, 5, 5, 8, 7, 3, 1, 3, 2, 6, 2, 6, 0, 6, 5, 2, 6, 4, 3,\n",
+            "         6, 6, 6, 7, 5, 4, 5, 1]], device='cuda:0')\n",
+            "tensor([8, 6, 2, 7, 4, 6, 8, 9, 3, 3, 5, 5, 3, 1, 4, 1, 9, 5, 1, 7, 7, 7, 5, 2,\n",
+            "        3, 3, 6, 6, 6, 4, 0, 7], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[24.4219, 18.9219, 19.0000, 17.4688, 15.5312, 17.6094, 16.1250, 16.7656,\n",
+            "         23.7031, 16.7656],\n",
+            "        [20.0312, 19.8281, 22.8125, 22.8750, 21.5000, 22.2031, 22.9375, 22.1875,\n",
+            "         21.2500, 19.7500],\n",
+            "        [20.6875, 19.4688, 23.4375, 21.8125, 21.9688, 21.8438, 21.4844, 23.2812,\n",
+            "         19.6250, 19.5156],\n",
+            "        [18.6250, 19.0781, 21.4531, 20.0625, 21.8438, 22.2656, 17.4062, 27.7812,\n",
+            "         18.0625, 18.4375],\n",
+            "        [19.6562, 19.5938, 20.9688, 21.2344, 28.8438, 23.0000, 18.2500, 25.4375,\n",
+            "         18.8281, 19.0312],\n",
+            "        [16.4531, 19.6562, 22.0781, 21.7188, 19.9844, 21.0469, 27.5781, 19.8438,\n",
+            "         18.3906, 17.3906],\n",
+            "        [22.3594, 22.0156, 20.5625, 18.1719, 16.2812, 17.9062, 18.9219, 17.9531,\n",
+            "         25.1406, 19.0781],\n",
+            "        [18.9531, 23.3594, 19.8438, 18.4531, 19.6094, 19.2812, 17.9531, 20.7344,\n",
+            "         20.2969, 25.9688],\n",
+            "        [19.8750, 21.1406, 22.1094, 27.7812, 19.9062, 23.2031, 21.6250, 21.9062,\n",
+            "         20.1250, 19.9688],\n",
+            "        [18.5156, 19.6094, 21.7031, 26.5000, 22.1719, 26.1719, 20.7500, 21.9844,\n",
+            "         18.8125, 18.3438],\n",
+            "        [19.3438, 19.8281, 21.4844, 22.2031, 25.3438, 27.5469, 20.7031, 23.3438,\n",
+            "         19.2344, 19.4531],\n",
+            "        [18.4844, 19.9062, 21.6250, 22.4219, 19.9375, 26.8594, 20.6250, 21.7031,\n",
+            "         19.6406, 19.3594],\n",
+            "        [20.1875, 20.4062, 23.1562, 25.6406, 21.9219, 24.4531, 21.9688, 22.3906,\n",
+            "         20.5781, 19.6406],\n",
+            "        [19.0000, 25.8281, 20.0625, 19.7656, 19.0938, 20.0938, 19.0781, 17.9844,\n",
+            "         19.1250, 22.6562],\n",
+            "        [20.5938, 21.4219, 23.3594, 23.7812, 29.9688, 25.7812, 19.6719, 27.0469,\n",
+            "         19.8125, 20.7344],\n",
+            "        [19.9219, 26.0156, 20.9062, 18.8281, 19.2344, 19.6719, 19.2812, 19.6094,\n",
+            "         19.1875, 25.1562],\n",
+            "        [18.1875, 22.7031, 18.9375, 17.7969, 17.6875, 18.0156, 17.4062, 18.3281,\n",
+            "         19.9688, 25.5781],\n",
+            "        [18.2188, 19.1250, 20.8594, 21.7188, 20.2500, 25.3594, 20.4844, 22.1562,\n",
+            "         19.1875, 18.9062],\n",
+            "        [16.6250, 23.9219, 19.4062, 18.5156, 20.1250, 19.7500, 17.4375, 18.5156,\n",
+            "         16.7344, 19.7500],\n",
+            "        [19.6094, 20.3438, 20.7969, 20.6406, 20.7344, 21.2656, 15.3281, 28.5000,\n",
+            "         18.5469, 19.5469],\n",
+            "        [18.8750, 20.0156, 20.8750, 21.5000, 21.0625, 22.0000, 17.2969, 29.2188,\n",
+            "         18.8906, 19.2031],\n",
+            "        [19.2500, 20.0469, 20.6719, 20.2500, 22.6719, 21.4844, 19.0625, 27.3125,\n",
+            "         19.4062, 19.7031],\n",
+            "        [19.2344, 19.2344, 20.8594, 20.9219, 19.9219, 25.7500, 19.8281, 21.3438,\n",
+            "         18.4375, 18.9688],\n",
+            "        [20.0938, 19.7969, 28.7031, 21.0000, 21.2812, 21.1250, 23.6406, 19.1719,\n",
+            "         19.3438, 19.4531],\n",
+            "        [20.1562, 20.3750, 23.9844, 26.8594, 21.5156, 25.0625, 21.6250, 22.9844,\n",
+            "         22.2812, 20.4219],\n",
+            "        [21.1875, 20.7656, 23.9688, 24.4844, 22.9531, 23.4688, 22.5938, 22.5625,\n",
+            "         22.6094, 20.6875],\n",
+            "        [19.7031, 20.0312, 23.9531, 23.9375, 19.8750, 24.7812, 28.2031, 21.7031,\n",
+            "         20.4844, 19.7969],\n",
+            "        [20.5469, 20.5938, 24.7500, 22.6094, 21.1719, 22.5938, 23.7500, 21.6719,\n",
+            "         20.6250, 19.9219],\n",
+            "        [19.1250, 20.6094, 23.5312, 22.9844, 24.4062, 24.5625, 26.3438, 23.2344,\n",
+            "         19.8750, 19.8281],\n",
+            "        [20.3281, 20.2188, 24.0312, 26.5469, 28.3750, 27.1094, 21.7812, 26.3906,\n",
+            "         19.2500, 20.2969],\n",
+            "        [29.3438, 19.8438, 24.4688, 19.8438, 20.5312, 20.2500, 20.6562, 20.5312,\n",
+            "         23.3906, 19.7500],\n",
+            "        [20.9531, 21.1094, 20.4062, 19.9375, 20.4219, 21.1562, 17.2344, 27.1875,\n",
+            "         19.5625, 21.1562]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[0],\n",
+            "        [6],\n",
+            "        [2],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [6],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [3],\n",
+            "        [5],\n",
+            "        [5],\n",
+            "        [3],\n",
+            "        [1],\n",
+            "        [4],\n",
+            "        [1],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [5],\n",
+            "        [2],\n",
+            "        [3],\n",
+            "        [3],\n",
+            "        [6],\n",
+            "        [2],\n",
+            "        [6],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [7]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[0, 6, 2, 7, 4, 6, 8, 9, 3, 3, 5, 5, 3, 1, 4, 1, 9, 5, 1, 7, 7, 7, 5, 2,\n",
+            "         3, 3, 6, 2, 6, 4, 0, 7],\n",
+            "        [8, 3, 7, 5, 7, 2, 0, 1, 5, 5, 4, 3, 5, 9, 7, 9, 1, 7, 4, 5, 5, 4, 7, 6,\n",
+            "         5, 2, 5, 6, 5, 5, 2, 9],\n",
+            "        [2, 2, 4, 4, 5, 3, 1, 7, 2, 4, 7, 7, 2, 5, 5, 2, 8, 3, 5, 2, 3, 5, 3, 4,\n",
+            "         2, 5, 2, 3, 4, 3, 8, 5],\n",
+            "        [1, 5, 5, 2, 3, 5, 2, 8, 7, 7, 3, 2, 7, 2, 3, 0, 2, 2, 9, 4, 4, 2, 2, 5,\n",
+            "         7, 4, 3, 5, 2, 7, 6, 1],\n",
+            "        [5, 7, 3, 3, 2, 4, 9, 2, 6, 2, 2, 6, 6, 3, 2, 5, 7, 6, 2, 3, 2, 3, 4, 3,\n",
+            "         8, 8, 7, 7, 7, 2, 4, 0]], device='cuda:0')\n",
+            "tensor([1, 7, 3, 0, 8, 1, 2, 4, 1, 2, 2, 4, 9, 8, 2, 8, 7, 6, 3, 4, 7, 2, 3, 1,\n",
+            "        5, 3, 0, 4, 3, 4, 9, 4], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[18.9062, 26.2812, 21.2969, 19.0938, 20.8281, 20.6875, 20.5938, 19.7344,\n",
+            "         19.3906, 26.0938],\n",
+            "        [19.7344, 20.5469, 22.3438, 23.6875, 23.3906, 24.8438, 19.7031, 29.1250,\n",
+            "         19.5625, 20.1094],\n",
+            "        [16.2188, 17.8438, 19.7188, 23.5000, 17.5625, 20.7031, 19.1875, 18.7656,\n",
+            "         17.2188, 16.6719],\n",
+            "        [27.4062, 21.7031, 24.0781, 20.2656, 21.3594, 20.2969, 19.2500, 20.5000,\n",
+            "         23.5156, 22.6875],\n",
+            "        [22.7500, 23.2812, 22.1250, 21.3594, 19.8906, 20.9688, 20.1250, 22.1094,\n",
+            "         29.3750, 23.7812],\n",
+            "        [18.0156, 24.0469, 18.5312, 17.7656, 17.1719, 19.0781, 17.4688, 17.9844,\n",
+            "         19.3125, 22.4062],\n",
+            "        [20.1875, 20.3281, 24.0625, 22.5312, 23.5000, 23.2812, 20.7188, 26.5469,\n",
+            "         19.7031, 20.3750],\n",
+            "        [18.8281, 18.0312, 21.7656, 21.4062, 25.2812, 21.6562, 21.7344, 22.3594,\n",
+            "         18.0156, 17.0312],\n",
+            "        [18.4219, 26.0312, 20.4531, 19.4375, 20.0312, 19.3594, 18.6250, 18.0469,\n",
+            "         18.8125, 21.7500],\n",
+            "        [20.5312, 19.2188, 28.3125, 20.2500, 19.7031, 20.8438, 22.2031, 19.1875,\n",
+            "         19.0781, 19.3906],\n",
+            "        [20.9219, 21.0938, 24.4375, 21.9844, 24.6250, 22.6094, 24.1094, 24.2344,\n",
+            "         20.8906, 20.6094],\n",
+            "        [19.2344, 19.4844, 22.0625, 20.1250, 26.5938, 20.8125, 19.1562, 23.2344,\n",
+            "         19.7656, 19.2812],\n",
+            "        [17.8906, 21.2812, 17.7969, 16.6562, 16.9844, 17.4531, 15.6094, 19.1719,\n",
+            "         18.7344, 25.1562],\n",
+            "        [20.4531, 22.7656, 20.7969, 20.1562, 19.0625, 20.7031, 19.5312, 19.6250,\n",
+            "         24.2656, 21.0469],\n",
+            "        [20.2656, 19.7031, 27.7500, 21.4844, 22.0156, 21.1719, 21.7344, 20.9688,\n",
+            "         18.5156, 19.3438],\n",
+            "        [20.8906, 19.7344, 19.7812, 19.2344, 18.6250, 19.3594, 17.4062, 18.6875,\n",
+            "         24.7188, 18.6094],\n",
+            "        [20.3750, 20.7031, 22.7188, 22.0000, 25.8750, 23.7969, 19.7188, 29.3594,\n",
+            "         18.9688, 20.3594],\n",
+            "        [18.5781, 20.1094, 21.8594, 21.7500, 20.5625, 22.3906, 23.1406, 22.0781,\n",
+            "         20.7812, 19.1719],\n",
+            "        [19.0156, 19.7500, 21.6094, 27.0625, 19.6406, 23.2188, 18.6719, 21.2500,\n",
+            "         20.6875, 19.1719],\n",
+            "        [22.2344, 22.5000, 24.0000, 21.6250, 31.3750, 24.5000, 20.4531, 25.6250,\n",
+            "         21.2344, 22.3750],\n",
+            "        [19.6562, 20.4688, 23.0938, 21.7656, 22.4688, 22.8438, 18.5781, 28.8281,\n",
+            "         19.3281, 19.8281],\n",
+            "        [21.6875, 20.2031, 29.3281, 21.8281, 23.5000, 21.9688, 23.8594, 21.4531,\n",
+            "         20.5156, 20.1250],\n",
+            "        [19.0469, 20.9844, 22.0000, 27.8281, 20.9844, 23.9375, 22.1094, 22.3125,\n",
+            "         20.3125, 19.8438],\n",
+            "        [18.7500, 25.0156, 18.7656, 17.8125, 17.6719, 18.7031, 17.6250, 19.5469,\n",
+            "         18.7812, 20.4219],\n",
+            "        [18.8594, 18.2344, 19.6406, 20.9688, 19.8125, 26.5469, 20.3750, 21.2031,\n",
+            "         17.8594, 18.8906],\n",
+            "        [20.1562, 21.4062, 22.9375, 28.2031, 21.2344, 24.3906, 21.2656, 22.2656,\n",
+            "         20.5469, 20.5156],\n",
+            "        [24.2344, 21.5312, 23.6250, 20.0000, 20.5469, 19.7812, 21.6875, 20.6875,\n",
+            "         22.0938, 20.5000],\n",
+            "        [19.8125, 20.9531, 22.6250, 23.2344, 27.3125, 26.1562, 21.5156, 26.7500,\n",
+            "         19.6406, 21.1875],\n",
+            "        [18.3906, 19.6562, 21.0938, 27.3594, 20.0938, 22.8438, 19.7188, 21.6875,\n",
+            "         19.2656, 18.0000],\n",
+            "        [20.0000, 19.7656, 24.0625, 21.3906, 29.8594, 23.8906, 21.3594, 25.8750,\n",
+            "         19.2969, 18.7500],\n",
+            "        [20.8438, 24.0156, 20.9844, 20.5000, 21.6406, 21.4531, 20.4844, 21.1406,\n",
+            "         21.1719, 26.4688],\n",
+            "        [19.7812, 19.7188, 21.3594, 21.1719, 25.2188, 22.0000, 20.4375, 24.4531,\n",
+            "         19.6562, 19.7656]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[1],\n",
+            "        [7],\n",
+            "        [3],\n",
+            "        [0],\n",
+            "        [8],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [4],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [2],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [3],\n",
+            "        [4],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [3],\n",
+            "        [1],\n",
+            "        [5],\n",
+            "        [3],\n",
+            "        [0],\n",
+            "        [4],\n",
+            "        [3],\n",
+            "        [4],\n",
+            "        [9],\n",
+            "        [4]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[1, 7, 3, 0, 8, 1, 7, 4, 1, 2, 4, 4, 9, 8, 2, 8, 7, 6, 3, 4, 7, 2, 3, 1,\n",
+            "         5, 3, 0, 4, 3, 4, 9, 4],\n",
+            "        [9, 5, 5, 2, 9, 9, 2, 7, 9, 6, 2, 7, 1, 1, 4, 0, 4, 5, 5, 7, 2, 6, 5, 9,\n",
+            "         7, 5, 2, 7, 5, 7, 1, 7],\n",
+            "        [2, 3, 2, 8, 1, 8, 4, 2, 2, 5, 7, 2, 7, 9, 6, 2, 5, 7, 2, 5, 5, 4, 7, 7,\n",
+            "         3, 2, 8, 5, 7, 2, 4, 5],\n",
+            "        [4, 4, 6, 9, 0, 5, 5, 6, 4, 0, 6, 5, 8, 2, 3, 1, 2, 2, 7, 2, 4, 5, 6, 8,\n",
+            "         6, 7, 6, 3, 2, 5, 5, 2],\n",
+            "        [5, 2, 7, 1, 2, 2, 3, 5, 3, 3, 5, 3, 0, 5, 5, 5, 3, 3, 8, 1, 3, 3, 2, 2,\n",
+            "         4, 1, 1, 2, 4, 3, 8, 3]], device='cuda:0')\n",
+            "tensor([7, 9, 1, 7, 8, 3, 1, 8, 3, 2, 5, 7, 2, 7, 9, 6, 8, 6, 8, 6, 5, 9, 0, 4,\n",
+            "        8, 5, 6, 4, 3, 8, 0, 4], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[17.5938, 18.2812, 18.7656, 18.0312, 20.4375, 19.7500, 16.7812, 25.1875,\n",
+            "         16.9219, 18.0156],\n",
+            "        [18.7500, 22.8125, 18.8281, 19.6875, 17.6406, 19.5000, 17.2969, 19.2500,\n",
+            "         19.8906, 24.0781],\n",
+            "        [19.1875, 24.2812, 18.7812, 17.5156, 18.7188, 18.6406, 17.5625, 18.4688,\n",
+            "         19.0938, 22.4531],\n",
+            "        [19.4062, 20.8281, 22.7969, 21.8125, 24.0938, 23.3281, 18.5156, 30.2031,\n",
+            "         19.0469, 20.0000],\n",
+            "        [21.3438, 20.7500, 21.3750, 19.8750, 20.1875, 20.0156, 18.8750, 20.9531,\n",
+            "         28.3125, 22.1875],\n",
+            "        [19.5625, 20.2500, 22.0938, 26.9375, 21.3594, 24.8750, 22.3281, 22.5938,\n",
+            "         19.8438, 19.5625],\n",
+            "        [19.2656, 25.2969, 19.9844, 18.1562, 19.1094, 19.7188, 18.1719, 18.3594,\n",
+            "         19.5000, 22.7188],\n",
+            "        [21.0469, 22.9062, 21.7812, 20.1562, 21.1250, 21.3594, 20.1094, 20.5469,\n",
+            "         25.4531, 22.4375],\n",
+            "        [17.9219, 18.7344, 20.3125, 25.6875, 20.2344, 22.9688, 18.8281, 20.7656,\n",
+            "         19.1562, 17.8438],\n",
+            "        [26.0312, 23.5469, 28.1875, 24.1406, 22.6406, 23.8438, 24.5000, 23.4844,\n",
+            "         24.3750, 22.8125],\n",
+            "        [21.5312, 21.2656, 24.8594, 24.6719, 21.3125, 26.7188, 22.4531, 22.8594,\n",
+            "         21.5625, 20.7812],\n",
+            "        [19.5312, 20.7656, 20.9219, 21.3906, 20.9531, 21.8125, 16.5625, 28.8750,\n",
+            "         19.3438, 19.6719],\n",
+            "        [21.3750, 20.0938, 27.1719, 20.6875, 20.6562, 20.9531, 20.6875, 21.8438,\n",
+            "         22.0156, 19.4688],\n",
+            "        [18.9062, 20.7188, 20.3125, 19.6094, 21.2188, 21.6094, 17.6875, 25.2812,\n",
+            "         18.8281, 19.9844],\n",
+            "        [20.0000, 23.5938, 19.3438, 18.9375, 18.7500, 19.3906, 17.2188, 21.0312,\n",
+            "         20.9062, 25.0781],\n",
+            "        [20.2344, 21.0156, 23.5156, 22.4062, 21.2969, 22.7188, 26.2656, 21.6719,\n",
+            "         20.5781, 19.8281],\n",
+            "        [21.9844, 20.9219, 22.0000, 18.5938, 18.5781, 19.7344, 18.7812, 19.6875,\n",
+            "         25.8750, 19.3906],\n",
+            "        [19.2969, 19.6719, 23.2969, 21.6250, 19.3594, 22.5469, 28.0938, 20.2812,\n",
+            "         19.6562, 19.5000],\n",
+            "        [23.0781, 21.5938, 22.2344, 19.9219, 22.0156, 20.6719, 18.7344, 21.0312,\n",
+            "         28.2500, 21.0000],\n",
+            "        [19.9062, 18.2344, 21.0000, 20.2500, 19.1719, 22.5625, 23.5156, 19.9844,\n",
+            "         20.6562, 17.4062],\n",
+            "        [18.3125, 19.0312, 21.8438, 22.5156, 22.0938, 25.6875, 22.2188, 22.9062,\n",
+            "         18.5000, 19.5781],\n",
+            "        [17.3125, 22.0938, 18.0625, 17.4062, 16.3594, 17.2031, 17.2188, 18.5938,\n",
+            "         19.7031, 23.2031],\n",
+            "        [27.6562, 19.5156, 24.0625, 18.8438, 19.8438, 19.7188, 18.0156, 18.9375,\n",
+            "         24.6094, 18.6094],\n",
+            "        [18.4219, 18.5625, 21.6094, 19.7500, 30.1875, 21.5781, 17.1250, 22.7969,\n",
+            "         17.4688, 17.9219],\n",
+            "        [21.6094, 22.1562, 21.3125, 20.9219, 18.7969, 21.2344, 18.5156, 21.3594,\n",
+            "         27.5781, 19.8438],\n",
+            "        [19.1562, 19.5469, 20.7500, 21.7188, 18.3125, 26.3125, 19.5156, 20.7500,\n",
+            "         20.4062, 18.6875],\n",
+            "        [22.0625, 20.9688, 24.4375, 23.2969, 24.5156, 23.2969, 24.7500, 22.8594,\n",
+            "         21.0938, 20.2500],\n",
+            "        [19.1562, 20.0781, 22.5000, 24.0625, 28.2031, 24.1094, 20.2500, 23.6562,\n",
+            "         19.0781, 18.4219],\n",
+            "        [19.1875, 19.3750, 21.3594, 26.2031, 19.9219, 22.7188, 20.7500, 21.0000,\n",
+            "         19.0625, 18.3438],\n",
+            "        [20.2969, 20.9219, 20.8125, 18.5938, 19.2188, 20.1719, 19.8906, 19.7500,\n",
+            "         24.7500, 20.0625],\n",
+            "        [27.9844, 22.7031, 23.7188, 19.0312, 19.2188, 19.5312, 18.5000, 20.2500,\n",
+            "         23.5312, 21.8750],\n",
+            "        [20.8438, 20.6250, 22.5781, 21.3438, 28.9688, 23.8906, 19.5469, 24.3438,\n",
+            "         19.4375, 20.7812]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[7],\n",
+            "        [9],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [3],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [5],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [6],\n",
+            "        [8],\n",
+            "        [6],\n",
+            "        [8],\n",
+            "        [6],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [4],\n",
+            "        [8],\n",
+            "        [5],\n",
+            "        [6],\n",
+            "        [4],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [0],\n",
+            "        [4]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[7, 9, 1, 7, 8, 3, 1, 8, 3, 2, 5, 7, 2, 7, 9, 6, 8, 6, 8, 6, 5, 9, 0, 4,\n",
+            "         8, 5, 6, 4, 3, 8, 0, 4],\n",
+            "        [4, 1, 9, 4, 9, 5, 9, 1, 5, 0, 2, 5, 8, 5, 1, 2, 2, 2, 0, 5, 7, 1, 8, 7,\n",
+            "         1, 3, 4, 5, 5, 1, 2, 7],\n",
+            "        [5, 8, 0, 5, 2, 7, 2, 9, 7, 6, 3, 3, 7, 4, 7, 5, 0, 5, 2, 2, 3, 8, 2, 2,\n",
+            "         0, 7, 2, 3, 2, 2, 8, 5],\n",
+            "        [2, 3, 8, 2, 0, 6, 5, 2, 2, 8, 7, 4, 0, 1, 8, 3, 1, 3, 4, 8, 6, 7, 4, 5,\n",
+            "         7, 2, 5, 7, 7, 0, 1, 2],\n",
+            "        [1, 5, 2, 3, 7, 2, 8, 5, 4, 3, 6, 2, 5, 2, 0, 7, 5, 7, 1, 3, 4, 2, 5, 3,\n",
+            "         2, 8, 3, 2, 6, 5, 9, 3]], device='cuda:0')\n",
+            "tensor([8, 6, 7, 2, 7, 1, 7, 4, 1, 2, 4, 2, 2, 8, 7, 2, 2, 6, 7, 5, 6, 8, 9, 9,\n",
+            "        6, 5, 2, 9, 8, 7, 2, 3], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[20.9844, 20.4219, 20.0469, 18.7812, 17.7969, 18.9688, 18.1406, 18.8125,\n",
+            "         25.2031, 19.7656],\n",
+            "        [17.6406, 18.1562, 18.4219, 18.2188, 17.1875, 18.6562, 18.3594, 18.3281,\n",
+            "         18.8281, 18.7656],\n",
+            "        [18.3438, 20.0938, 19.5781, 20.1094, 20.8594, 21.3125, 16.7031, 27.5938,\n",
+            "         17.6094, 18.6875],\n",
+            "        [21.3594, 19.5938, 27.3906, 20.9531, 19.6406, 20.6875, 21.7188, 20.4062,\n",
+            "         20.5625, 19.0156],\n",
+            "        [19.8438, 21.8125, 21.7344, 21.9375, 22.6094, 23.1406, 19.6562, 28.7812,\n",
+            "         19.6094, 20.8594],\n",
+            "        [19.7969, 27.6719, 21.5469, 20.5781, 19.7500, 20.8906, 20.8750, 19.4688,\n",
+            "         21.4219, 22.7656],\n",
+            "        [19.3906, 19.8906, 21.8438, 21.6094, 22.4688, 22.1094, 17.4219, 28.7656,\n",
+            "         19.2500, 19.5781],\n",
+            "        [20.9531, 20.8594, 22.5156, 21.1562, 28.9219, 22.6719, 20.3594, 23.7500,\n",
+            "         19.9219, 20.3438],\n",
+            "        [18.0625, 23.8438, 18.2812, 16.0156, 16.1562, 17.4688, 15.0312, 17.5938,\n",
+            "         17.8125, 19.7344],\n",
+            "        [20.3594, 19.6875, 28.6250, 22.3750, 21.4062, 22.1094, 23.5156, 20.3438,\n",
+            "         20.9688, 19.0000],\n",
+            "        [21.4062, 20.8125, 22.6719, 22.2656, 28.7500, 24.4062, 20.5625, 24.8750,\n",
+            "         20.4688, 20.5625],\n",
+            "        [23.0938, 20.7188, 29.3281, 23.4688, 19.4062, 22.6719, 22.8750, 21.5938,\n",
+            "         21.6875, 20.8281],\n",
+            "        [20.4375, 18.4375, 28.1562, 19.9062, 19.2188, 19.4844, 21.3281, 19.7500,\n",
+            "         18.7031, 18.5625],\n",
+            "        [21.3125, 21.8906, 21.6250, 18.9219, 19.9531, 20.7031, 19.8281, 20.4062,\n",
+            "         24.5469, 21.8438],\n",
+            "        [18.1562, 20.0938, 21.4219, 21.6094, 23.2031, 22.5000, 16.1875, 29.9062,\n",
+            "         18.5312, 18.5781],\n",
+            "        [21.2812, 20.2344, 30.0469, 22.3594, 22.1562, 21.7188, 22.1250, 21.4531,\n",
+            "         20.2656, 20.2969],\n",
+            "        [24.2656, 19.5156, 28.3750, 20.4844, 19.7500, 20.7344, 21.1719, 21.0156,\n",
+            "         21.0312, 19.2031],\n",
+            "        [18.3281, 19.2031, 24.1562, 22.0781, 22.6562, 23.1719, 25.8750, 21.2188,\n",
+            "         19.8281, 19.3281],\n",
+            "        [19.2188, 19.5781, 20.0000, 20.2500, 21.2188, 21.1562, 18.4062, 27.6719,\n",
+            "         18.2656, 19.1719],\n",
+            "        [20.2031, 20.4688, 23.0469, 22.8750, 27.9375, 27.0469, 19.0312, 27.9531,\n",
+            "         19.8594, 20.2969],\n",
+            "        [18.3594, 21.8281, 22.5469, 22.0469, 21.2500, 23.2812, 23.5781, 21.8438,\n",
+            "         20.9375, 20.5000],\n",
+            "        [21.5000, 22.2656, 21.3594, 19.0781, 18.8438, 20.1719, 18.4062, 20.4531,\n",
+            "         25.5781, 22.4375],\n",
+            "        [21.1406, 23.4688, 21.0781, 19.8125, 18.0781, 19.7812, 18.7031, 19.8906,\n",
+            "         21.3594, 26.2188],\n",
+            "        [22.3125, 25.3438, 22.2656, 21.1406, 20.8750, 21.3438, 20.8750, 22.0312,\n",
+            "         22.7969, 29.1875],\n",
+            "        [20.8438, 21.4531, 22.6094, 23.0000, 21.8906, 27.1719, 24.8750, 23.4062,\n",
+            "         20.6250, 21.2812],\n",
+            "        [19.4375, 19.1562, 20.2656, 21.9531, 21.4844, 26.0781, 20.0781, 20.8750,\n",
+            "         19.0469, 18.8281],\n",
+            "        [20.4219, 18.4062, 27.2812, 19.2344, 21.0312, 19.5625, 20.1094, 20.1094,\n",
+            "         18.0938, 17.8125],\n",
+            "        [21.2656, 25.1094, 21.0781, 20.2188, 19.2031, 20.0469, 19.0469, 20.5000,\n",
+            "         21.6094, 25.9688],\n",
+            "        [21.0781, 21.2500, 20.4531, 18.0469, 18.4062, 18.8906, 18.0625, 18.3281,\n",
+            "         24.0000, 22.1562],\n",
+            "        [18.2812, 20.3438, 21.2969, 21.7031, 21.7031, 25.4219, 18.3281, 26.5469,\n",
+            "         17.8594, 19.8281],\n",
+            "        [20.3750, 21.0312, 25.1562, 23.0469, 24.6094, 22.9688, 21.4062, 26.8594,\n",
+            "         19.7656, 20.5625],\n",
+            "        [21.9844, 21.3125, 24.0469, 26.8750, 23.4688, 25.8750, 22.1719, 24.3594,\n",
+            "         21.7969, 21.1875]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[8],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [7],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [6],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [5],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [3]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[8, 8, 7, 2, 7, 1, 7, 4, 1, 2, 4, 2, 2, 8, 7, 2, 2, 6, 7, 7, 6, 8, 9, 9,\n",
+            "         5, 5, 2, 9, 8, 7, 7, 3],\n",
+            "        [0, 9, 5, 6, 5, 9, 4, 7, 9, 6, 7, 3, 6, 1, 4, 3, 0, 2, 4, 4, 5, 9, 1, 1,\n",
+            "         6, 3, 4, 1, 9, 5, 2, 5],\n",
+            "        [1, 5, 4, 0, 4, 2, 5, 5, 2, 3, 5, 0, 0, 9, 5, 4, 6, 5, 5, 5, 2, 1, 8, 8,\n",
+            "         7, 4, 0, 8, 1, 4, 4, 7],\n",
+            "        [2, 2, 3, 3, 3, 8, 2, 2, 0, 5, 2, 6, 3, 2, 3, 6, 8, 4, 3, 2, 3, 0, 0, 0,\n",
+            "         3, 7, 7, 0, 0, 3, 3, 2],\n",
+            "        [9, 6, 1, 5, 1, 5, 3, 3, 8, 4, 3, 5, 7, 0, 2, 5, 7, 3, 2, 3, 7, 2, 2, 2,\n",
+            "         2, 2, 6, 2, 2, 2, 5, 4]], device='cuda:0')\n",
+            "tensor([3, 7, 4, 3, 2, 1, 9, 0, 1, 5, 3, 2, 6, 5, 9, 1, 5, 4, 3, 6, 2, 8, 9, 7,\n",
+            "        8, 0, 8, 9, 1, 2, 5, 7], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[16.8125, 18.6250, 19.2500, 24.7188, 17.0469, 20.3906, 17.4844, 19.0156,\n",
+            "         17.6719, 17.5781],\n",
+            "        [22.0625, 20.1406, 23.0156, 22.4062, 23.0000, 22.1094, 20.9219, 25.7188,\n",
+            "         20.2500, 19.6562],\n",
+            "        [18.8281, 19.0938, 22.2031, 21.2656, 28.4844, 21.9844, 17.8750, 25.2812,\n",
+            "         18.4688, 18.8594],\n",
+            "        [18.5781, 19.8438, 21.8750, 27.4062, 20.1406, 23.0312, 20.7188, 20.7344,\n",
+            "         19.5469, 18.4219],\n",
+            "        [20.6719, 20.1562, 27.2969, 22.6719, 24.7969, 23.0469, 22.6875, 23.6562,\n",
+            "         19.8750, 19.8906],\n",
+            "        [19.3281, 24.2031, 19.8125, 18.6094, 19.6562, 19.2969, 18.6875, 18.3438,\n",
+            "         18.9688, 23.3281],\n",
+            "        [20.6250, 23.0625, 19.7188, 19.0000, 18.3125, 19.0469, 17.3125, 19.0938,\n",
+            "         21.8438, 26.3281],\n",
+            "        [23.6875, 17.4531, 19.6406, 15.2500, 14.9688, 16.3281, 15.2812, 15.3750,\n",
+            "         18.8438, 15.6250],\n",
+            "        [17.8906, 25.2969, 18.6406, 18.1562, 17.7812, 19.6094, 18.3750, 18.5312,\n",
+            "         18.2344, 25.2344],\n",
+            "        [19.2188, 20.8438, 22.8906, 26.4375, 20.5312, 24.1719, 21.8281, 21.4375,\n",
+            "         21.3438, 19.3906],\n",
+            "        [20.5625, 20.3750, 22.1094, 25.7812, 21.8438, 25.0938, 20.7500, 23.7031,\n",
+            "         21.0469, 19.8594],\n",
+            "        [20.3906, 20.2344, 28.9062, 20.7031, 19.4531, 21.3594, 21.2500, 20.0625,\n",
+            "         19.6562, 20.1250],\n",
+            "        [18.9375, 19.4375, 22.8438, 20.8906, 19.0625, 21.7969, 29.3281, 19.2344,\n",
+            "         18.3594, 18.8281],\n",
+            "        [19.0469, 20.3750, 20.2188, 26.0938, 22.0156, 25.3438, 21.4062, 24.3906,\n",
+            "         20.2812, 19.4062],\n",
+            "        [19.1875, 23.6406, 20.3125, 19.0000, 19.0625, 19.5938, 17.5938, 19.9375,\n",
+            "         20.5000, 27.5000],\n",
+            "        [21.7031, 26.3438, 21.2188, 21.7969, 20.1094, 20.5781, 21.1562, 20.2812,\n",
+            "         21.4219, 22.9688],\n",
+            "        [20.2500, 20.7500, 21.9844, 23.1875, 22.1719, 27.3125, 20.9844, 25.1406,\n",
+            "         19.8125, 20.0625],\n",
+            "        [19.5781, 21.0000, 21.3906, 19.4844, 27.8906, 22.0000, 17.6719, 24.6250,\n",
+            "         19.0469, 19.7500],\n",
+            "        [18.7344, 20.1562, 20.9062, 26.3750, 18.7031, 23.1094, 20.0938, 21.0938,\n",
+            "         19.6094, 18.6875],\n",
+            "        [18.8594, 19.8906, 22.3750, 26.7500, 23.6875, 24.7344, 23.5156, 22.9688,\n",
+            "         19.7188, 18.7344],\n",
+            "        [19.8438, 19.9375, 22.9375, 20.2812, 19.9531, 20.6875, 21.2500, 19.8906,\n",
+            "         19.3594, 19.4844],\n",
+            "        [20.1250, 19.8594, 19.8281, 17.8750, 18.8281, 18.7656, 18.7969, 19.2656,\n",
+            "         22.1094, 18.1094],\n",
+            "        [21.0469, 23.7656, 20.0781, 20.4219, 19.9531, 20.8594, 19.5938, 20.7969,\n",
+            "         22.3125, 24.6250],\n",
+            "        [20.1719, 22.8125, 22.5156, 23.7188, 26.8125, 26.4531, 19.2500, 30.2656,\n",
+            "         20.8750, 20.7500],\n",
+            "        [22.6250, 21.7969, 21.2812, 20.4062, 19.9375, 20.8594, 19.6094, 19.8438,\n",
+            "         25.7188, 20.9531],\n",
+            "        [27.7812, 20.5938, 23.3750, 19.6562, 20.3906, 19.8750, 18.6875, 20.4375,\n",
+            "         23.0469, 20.2344],\n",
+            "        [17.0938, 18.5312, 18.2188, 16.4062, 16.9688, 16.6094, 14.7656, 17.7031,\n",
+            "         25.2969, 19.2969],\n",
+            "        [20.8125, 25.3438, 21.8125, 19.5781, 19.5312, 20.2188, 21.5000, 21.4375,\n",
+            "         22.1250, 27.0938],\n",
+            "        [19.2812, 25.9219, 20.1875, 19.1562, 18.7031, 20.9062, 18.5000, 17.8281,\n",
+            "         19.8438, 23.3281],\n",
+            "        [21.6094, 22.1562, 29.1562, 23.8750, 23.5781, 24.1094, 23.6094, 23.5156,\n",
+            "         21.3438, 21.5000],\n",
+            "        [20.5938, 21.4219, 23.2188, 23.5781, 22.4531, 27.4844, 23.6250, 23.3906,\n",
+            "         20.7500, 20.6562],\n",
+            "        [19.0625, 19.5156, 19.5469, 19.7500, 20.4062, 20.9844, 17.6719, 27.3438,\n",
+            "         18.1562, 18.4531]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[3],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [6],\n",
+            "        [3],\n",
+            "        [9],\n",
+            "        [1],\n",
+            "        [5],\n",
+            "        [4],\n",
+            "        [3],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [0],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [5],\n",
+            "        [7]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[3, 7, 4, 3, 2, 1, 9, 0, 1, 3, 3, 2, 6, 3, 9, 1, 5, 4, 3, 3, 2, 8, 9, 7,\n",
+            "         8, 0, 8, 9, 1, 2, 5, 7],\n",
+            "        [5, 2, 7, 5, 4, 9, 1, 2, 9, 5, 5, 5, 2, 5, 1, 9, 7, 7, 5, 5, 6, 0, 1, 4,\n",
+            "         0, 2, 9, 1, 9, 5, 6, 5],\n",
+            "        [2, 4, 2, 2, 7, 2, 8, 8, 5, 2, 7, 6, 5, 7, 8, 3, 3, 5, 7, 4, 5, 1, 8, 5,\n",
+            "         1, 8, 1, 8, 5, 3, 3, 4],\n",
+            "        [7, 3, 5, 7, 5, 4, 0, 1, 2, 6, 2, 3, 3, 4, 2, 0, 4, 2, 2, 6, 3, 2, 0, 3,\n",
+            "         2, 1, 2, 2, 2, 6, 7, 3],\n",
+            "        [1, 5, 3, 6, 6, 0, 2, 5, 7, 7, 4, 0, 1, 6, 7, 8, 2, 1, 1, 7, 4, 7, 5, 1,\n",
+            "         9, 7, 7, 6, 8, 4, 2, 2]], device='cuda:0')\n",
+            "tensor([2, 0, 6, 6, 1, 6, 3, 1, 5, 0, 3, 7, 4, 6, 5, 6, 4, 8, 3, 4, 5, 5, 0, 6,\n",
+            "        6, 7, 5, 0, 9, 9, 6, 1], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[21.5938, 21.0781, 24.4844, 20.7188, 20.3438, 21.6250, 21.9219, 21.3750,\n",
+            "         21.1406, 20.8281],\n",
+            "        [30.7031, 23.8594, 26.1406, 21.2812, 22.4062, 22.1719, 21.5781, 22.2188,\n",
+            "         24.8438, 22.8438],\n",
+            "        [20.0000, 20.7031, 23.2344, 21.6094, 21.4688, 22.5625, 28.4531, 21.5469,\n",
+            "         19.3750, 19.9375],\n",
+            "        [19.1250, 19.1562, 21.7188, 21.3906, 21.5469, 21.7188, 22.2812, 20.5156,\n",
+            "         19.5938, 18.8906],\n",
+            "        [18.2969, 24.9062, 19.5781, 18.0156, 18.1250, 19.1562, 17.7344, 17.1875,\n",
+            "         18.4219, 20.1719],\n",
+            "        [18.2031, 19.2500, 22.2656, 21.2344, 20.7812, 21.6875, 26.8281, 20.2344,\n",
+            "         18.5469, 18.5938],\n",
+            "        [19.7500, 20.4219, 20.3594, 20.4375, 19.2188, 21.1250, 19.4531, 20.0781,\n",
+            "         20.2656, 20.0625],\n",
+            "        [19.7188, 25.1562, 20.8906, 19.8594, 21.1406, 20.1719, 20.8281, 19.6719,\n",
+            "         19.7500, 22.6562],\n",
+            "        [19.5781, 19.3594, 21.2656, 23.1406, 19.6562, 26.5469, 20.4531, 22.0781,\n",
+            "         19.6250, 19.5000],\n",
+            "        [29.0156, 22.4688, 26.2656, 21.0625, 21.5938, 21.2188, 21.7031, 21.8594,\n",
+            "         24.0156, 21.2656],\n",
+            "        [18.9844, 18.5469, 21.1875, 24.7656, 20.9844, 21.2656, 19.8750, 20.0000,\n",
+            "         20.1406, 18.1875],\n",
+            "        [19.1250, 20.7031, 19.7812, 19.5469, 21.3281, 21.3281, 16.4219, 27.6875,\n",
+            "         18.3438, 20.0312],\n",
+            "        [25.7188, 24.1719, 25.7344, 22.6406, 30.7344, 24.3125, 21.8594, 28.4531,\n",
+            "         23.7812, 24.3438],\n",
+            "        [21.1406, 24.0625, 22.1406, 21.5312, 22.8594, 21.5000, 23.6875, 20.8750,\n",
+            "         21.3750, 22.3750],\n",
+            "        [17.5938, 19.0312, 23.5156, 21.0938, 19.5781, 23.0312, 21.1250, 19.7344,\n",
+            "         19.3281, 18.4062],\n",
+            "        [18.2969, 18.5625, 21.7031, 20.5000, 22.5469, 21.3594, 25.3125, 21.6719,\n",
+            "         18.1875, 17.4219],\n",
+            "        [20.9531, 20.8906, 23.7969, 23.0469, 30.4219, 24.3594, 20.3438, 25.5312,\n",
+            "         19.8750, 20.5312],\n",
+            "        [25.6562, 23.0469, 21.8750, 19.2812, 19.1406, 19.4062, 18.6562, 19.4844,\n",
+            "         25.4219, 24.2188],\n",
+            "        [20.1875, 20.7500, 22.5312, 26.7188, 21.1562, 23.7031, 21.4375, 22.3281,\n",
+            "         20.3438, 19.8281],\n",
+            "        [22.6562, 19.4844, 21.9375, 20.0000, 26.6094, 21.4531, 18.3125, 23.7969,\n",
+            "         20.7500, 20.2812],\n",
+            "        [19.1875, 19.7656, 21.1094, 21.7969, 19.0469, 27.4219, 20.3750, 21.7812,\n",
+            "         18.7812, 18.7812],\n",
+            "        [19.7812, 19.3125, 21.7812, 21.5156, 20.8750, 26.7656, 20.6250, 21.6562,\n",
+            "         18.7812, 19.7031],\n",
+            "        [28.0625, 22.4531, 24.1094, 21.0156, 21.6719, 21.9219, 22.2500, 21.3281,\n",
+            "         23.6250, 20.3594],\n",
+            "        [19.6094, 19.9375, 23.4375, 23.4688, 22.9062, 23.2812, 26.1719, 22.8438,\n",
+            "         19.9062, 18.9375],\n",
+            "        [17.1094, 18.4531, 21.5156, 23.5938, 22.1562, 21.8281, 21.5781, 19.4375,\n",
+            "         18.5312, 17.3438],\n",
+            "        [17.1562, 18.7656, 19.1719, 20.2656, 19.1562, 20.8438, 16.3281, 26.4688,\n",
+            "         17.2812, 18.5312],\n",
+            "        [21.5938, 20.7500, 22.1875, 23.6875, 24.7656, 29.1250, 19.8125, 24.7812,\n",
+            "         20.3750, 19.9375],\n",
+            "        [28.7188, 21.4062, 23.7969, 19.5469, 19.3750, 20.0469, 18.2344, 20.5625,\n",
+            "         23.0625, 20.3438],\n",
+            "        [20.4062, 23.0156, 19.9688, 18.3594, 18.9219, 19.8906, 19.0156, 19.8125,\n",
+            "         21.4844, 25.3906],\n",
+            "        [20.1719, 24.8750, 21.3125, 20.4531, 20.3594, 21.2812, 20.1719, 21.2344,\n",
+            "         21.2969, 27.6875],\n",
+            "        [20.4844, 20.7500, 23.8438, 22.2812, 22.3594, 22.4375, 25.3438, 21.9062,\n",
+            "         21.6562, 20.5000],\n",
+            "        [18.9062, 26.2969, 21.8438, 20.5312, 20.4062, 21.4219, 19.7969, 20.0000,\n",
+            "         20.2500, 23.5625]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[2],\n",
+            "        [0],\n",
+            "        [6],\n",
+            "        [6],\n",
+            "        [1],\n",
+            "        [6],\n",
+            "        [5],\n",
+            "        [1],\n",
+            "        [5],\n",
+            "        [0],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [6],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [3],\n",
+            "        [4],\n",
+            "        [5],\n",
+            "        [5],\n",
+            "        [0],\n",
+            "        [6],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [5],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [9],\n",
+            "        [6],\n",
+            "        [1]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[2, 0, 6, 6, 1, 6, 5, 1, 5, 0, 3, 7, 4, 1, 2, 6, 4, 0, 3, 4, 5, 5, 0, 6,\n",
+            "         3, 7, 5, 0, 9, 9, 6, 1],\n",
+            "        [6, 2, 2, 5, 9, 2, 3, 9, 3, 2, 5, 5, 7, 6, 5, 4, 7, 8, 5, 7, 3, 2, 2, 3,\n",
+            "         4, 5, 7, 2, 1, 1, 2, 9],\n",
+            "        [5, 8, 5, 2, 2, 5, 1, 4, 7, 8, 2, 4, 2, 4, 6, 2, 5, 9, 2, 0, 7, 7, 8, 2,\n",
+            "         5, 3, 4, 8, 8, 2, 5, 2],\n",
+            "        [0, 1, 3, 4, 5, 3, 2, 2, 2, 1, 4, 1, 0, 9, 3, 7, 2, 1, 7, 2, 2, 3, 1, 5,\n",
+            "         6, 2, 3, 1, 0, 8, 4, 5],\n",
+            "        [7, 9, 7, 3, 8, 4, 8, 6, 6, 7, 8, 9, 9, 2, 7, 5, 3, 2, 6, 5, 6, 4, 6, 4,\n",
+            "         2, 4, 2, 7, 2, 5, 3, 3]], device='cuda:0')\n",
+            "tensor([0, 4, 6, 3, 6, 9, 3, 6, 6, 8, 2, 1, 0, 1, 9, 3, 7, 4, 3, 0, 2, 7, 6, 4,\n",
+            "        3, 0, 8, 2, 0, 0, 9, 6], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[27.4688, 22.6875, 28.7188, 22.5156, 21.6406, 21.8750, 23.0000, 23.2812,\n",
+            "         23.8125, 23.3594],\n",
+            "        [20.0000, 19.7188, 21.2344, 19.5156, 28.4375, 22.1406, 17.8906, 23.4531,\n",
+            "         17.9688, 19.9375],\n",
+            "        [18.8906, 19.7656, 20.7656, 23.5781, 20.0938, 20.9375, 22.8438, 20.3906,\n",
+            "         20.7656, 18.7344],\n",
+            "        [20.6875, 21.2812, 22.6875, 22.8125, 21.3906, 23.6719, 22.7969, 22.5625,\n",
+            "         21.0625, 20.8281],\n",
+            "        [21.4688, 20.7031, 27.5469, 22.8906, 23.3438, 23.2812, 24.5156, 21.6250,\n",
+            "         21.3438, 19.3750],\n",
+            "        [18.6250, 23.0312, 18.6875, 16.9219, 17.7344, 18.5781, 16.2812, 18.8594,\n",
+            "         19.7188, 25.1719],\n",
+            "        [20.3125, 19.3750, 22.1875, 23.5312, 21.8281, 22.1719, 22.9375, 22.1562,\n",
+            "         21.1875, 19.2812],\n",
+            "        [19.7031, 19.9062, 24.5312, 22.9219, 21.4688, 22.3906, 26.1719, 20.8750,\n",
+            "         20.0312, 18.9219],\n",
+            "        [19.7812, 21.2031, 24.0312, 24.3594, 22.4219, 23.3281, 24.2188, 22.5938,\n",
+            "         21.5469, 20.1094],\n",
+            "        [19.7812, 20.3281, 19.6875, 19.5781, 18.2656, 19.7969, 18.7812, 18.2969,\n",
+            "         24.4844, 18.1250],\n",
+            "        [20.6719, 19.3438, 28.5156, 20.7969, 20.3594, 20.1562, 22.0781, 18.2500,\n",
+            "         18.7188, 19.1250],\n",
+            "        [22.5938, 26.5781, 20.9844, 20.8281, 19.9219, 20.7656, 21.7344, 20.9062,\n",
+            "         22.0781, 22.2500],\n",
+            "        [28.3281, 21.9062, 25.6094, 20.7812, 20.4375, 20.5938, 21.3750, 21.6875,\n",
+            "         23.1406, 20.3594],\n",
+            "        [19.3125, 25.4688, 19.2500, 17.5312, 17.5469, 19.4219, 18.1406, 18.0469,\n",
+            "         18.6875, 23.8438],\n",
+            "        [17.6250, 22.0312, 17.5312, 16.9844, 16.8281, 17.9375, 16.3750, 18.9375,\n",
+            "         18.7031, 22.4219],\n",
+            "        [19.8438, 21.2500, 23.3125, 28.3438, 20.8125, 24.2188, 22.5625, 22.6406,\n",
+            "         20.1562, 19.9062],\n",
+            "        [18.7656, 20.0781, 21.8906, 21.3125, 22.4062, 22.9375, 16.9062, 29.7344,\n",
+            "         19.4375, 19.0938],\n",
+            "        [21.0625, 19.6562, 23.0000, 22.2969, 27.9531, 23.0781, 19.5000, 25.2031,\n",
+            "         19.2656, 18.9844],\n",
+            "        [19.8281, 20.9219, 21.7812, 26.4688, 20.3750, 24.0469, 22.0156, 21.5000,\n",
+            "         20.2500, 20.1719],\n",
+            "        [29.1562, 22.4062, 26.1562, 21.2188, 21.7812, 21.4688, 21.4688, 21.5156,\n",
+            "         24.7656, 22.3906],\n",
+            "        [19.1562, 18.0781, 28.0000, 18.9688, 19.9531, 19.9531, 19.1719, 18.4219,\n",
+            "         17.7031, 17.6719],\n",
+            "        [19.2656, 20.8906, 22.3750, 21.5938, 23.6875, 23.1406, 16.8281, 29.5469,\n",
+            "         19.8750, 19.5312],\n",
+            "        [22.0312, 20.8438, 22.7812, 24.0000, 23.4844, 23.3906, 22.2812, 22.7969,\n",
+            "         21.5625, 19.6094],\n",
+            "        [22.2344, 20.8281, 23.0625, 19.9844, 29.1406, 22.7031, 19.1250, 25.8594,\n",
+            "         19.6719, 20.7500],\n",
+            "        [20.8438, 21.2500, 23.3125, 28.7500, 20.9219, 25.0156, 21.4375, 22.7344,\n",
+            "         21.4375, 19.5781],\n",
+            "        [24.5938, 22.8281, 20.5938, 18.4375, 19.6250, 19.8125, 20.7969, 20.1406,\n",
+            "         21.6719, 20.3750],\n",
+            "        [19.2344, 18.9375, 20.6406, 18.9844, 19.3906, 18.6562, 17.9531, 19.4375,\n",
+            "         26.5781, 20.2500],\n",
+            "        [21.7031, 20.4688, 28.9375, 22.6094, 22.8906, 23.3750, 21.8906, 23.1875,\n",
+            "         21.5000, 21.5781],\n",
+            "        [26.7656, 21.5312, 23.4219, 20.7188, 21.0000, 20.7812, 19.5312, 20.2188,\n",
+            "         21.8125, 19.6875],\n",
+            "        [28.1094, 20.9844, 24.8594, 18.9375, 19.0156, 19.8281, 16.9844, 19.9219,\n",
+            "         22.5000, 20.6875],\n",
+            "        [16.7969, 21.9688, 18.5000, 16.8125, 16.5469, 17.1875, 17.1406, 18.2656,\n",
+            "         19.4844, 26.2500],\n",
+            "        [21.0312, 21.5781, 24.6406, 24.0469, 25.0469, 24.5312, 29.7969, 22.2656,\n",
+            "         20.9688, 20.3750]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[2],\n",
+            "        [4],\n",
+            "        [3],\n",
+            "        [5],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [6],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [0],\n",
+            "        [1],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [3],\n",
+            "        [0],\n",
+            "        [2],\n",
+            "        [7],\n",
+            "        [3],\n",
+            "        [4],\n",
+            "        [3],\n",
+            "        [0],\n",
+            "        [8],\n",
+            "        [2],\n",
+            "        [0],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [6]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[2, 4, 3, 5, 2, 9, 3, 6, 3, 8, 2, 1, 0, 1, 9, 3, 7, 4, 3, 0, 2, 7, 3, 4,\n",
+            "         3, 0, 8, 2, 0, 0, 9, 6],\n",
+            "        [0, 7, 6, 3, 6, 1, 6, 2, 6, 1, 6, 0, 2, 9, 1, 5, 5, 7, 5, 2, 5, 4, 4, 7,\n",
+            "         5, 1, 2, 5, 2, 2, 1, 4],\n",
+            "        [8, 5, 5, 6, 4, 8, 2, 3, 2, 5, 3, 9, 8, 5, 7, 2, 4, 5, 6, 8, 4, 5, 5, 2,\n",
+            "         2, 8, 9, 7, 8, 8, 8, 2],\n",
+            "        [9, 2, 8, 2, 5, 7, 5, 5, 5, 0, 0, 8, 1, 0, 8, 7, 2, 2, 2, 1, 6, 2, 7, 5,\n",
+            "         7, 6, 7, 4, 1, 1, 2, 5],\n",
+            "        [7, 0, 2, 7, 3, 2, 7, 4, 7, 2, 4, 6, 7, 2, 5, 6, 3, 3, 7, 9, 0, 3, 2, 0,\n",
+            "         6, 2, 4, 3, 4, 9, 7, 3]], device='cuda:0')\n",
+            "tensor([6, 6, 8, 7, 4, 1, 8, 1, 2, 2, 4, 8, 5, 2, 6, 5, 3, 9, 1, 0, 7, 2, 4, 4,\n",
+            "        0, 0, 6, 2, 2, 4, 0, 5], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[19.3281, 20.2188, 21.5000, 22.5000, 21.9531, 23.5781, 21.7188, 22.9844,\n",
+            "         19.8750, 19.2969],\n",
+            "        [19.9062, 20.9375, 23.3281, 24.0781, 24.0312, 24.0781, 25.7344, 22.8281,\n",
+            "         21.1094, 20.2031],\n",
+            "        [22.7031, 21.1406, 21.9375, 18.1406, 20.2656, 20.3125, 18.0938, 20.1250,\n",
+            "         27.6250, 21.2031],\n",
+            "        [21.0000, 21.3281, 19.5156, 20.9531, 22.2031, 23.0312, 19.0781, 27.2031,\n",
+            "         19.7812, 20.7031],\n",
+            "        [22.3594, 21.6250, 25.0156, 22.3906, 29.5625, 24.1875, 22.7188, 24.5000,\n",
+            "         20.5000, 21.4844],\n",
+            "        [18.9375, 25.5469, 19.5625, 19.3125, 19.3594, 19.0312, 18.7812, 18.4375,\n",
+            "         19.7188, 21.9375],\n",
+            "        [20.0469, 19.7500, 18.9531, 18.1094, 17.6875, 18.9688, 17.0781, 18.4531,\n",
+            "         24.7188, 18.2188],\n",
+            "        [18.3438, 25.2188, 21.3125, 20.6406, 18.2969, 20.9375, 19.9531, 18.5469,\n",
+            "         18.5469, 22.0000],\n",
+            "        [19.5781, 20.8594, 23.7500, 24.5312, 22.5625, 24.3906, 23.0000, 23.2344,\n",
+            "         20.1094, 20.0469],\n",
+            "        [24.0938, 20.6406, 26.7969, 20.7188, 20.6406, 21.0938, 23.6875, 22.0312,\n",
+            "         21.7031, 19.0312],\n",
+            "        [23.8594, 20.5469, 25.4531, 22.5156, 28.0000, 22.5781, 22.7188, 22.5938,\n",
+            "         21.0312, 19.6562],\n",
+            "        [19.9062, 22.2188, 21.2500, 20.0781, 20.7344, 21.3750, 19.8906, 20.7031,\n",
+            "         25.1094, 21.8438],\n",
+            "        [19.9844, 19.7969, 20.2500, 21.8906, 18.0312, 26.9688, 20.7031, 21.3750,\n",
+            "         19.4688, 18.9219],\n",
+            "        [19.8281, 18.5938, 27.3750, 20.3281, 19.9688, 20.4219, 22.5625, 19.3750,\n",
+            "         18.3906, 18.7344],\n",
+            "        [18.4844, 19.9688, 22.2969, 25.6875, 22.0938, 23.3281, 27.0625, 20.6719,\n",
+            "         18.9688, 18.4375],\n",
+            "        [17.8594, 18.9062, 20.7500, 22.2031, 23.3281, 26.5469, 19.5938, 23.6094,\n",
+            "         18.4688, 18.7969],\n",
+            "        [19.7812, 20.5469, 23.1719, 28.7031, 20.5000, 25.8125, 21.9062, 23.2188,\n",
+            "         21.3906, 19.7969],\n",
+            "        [18.8906, 23.3281, 20.4375, 19.7812, 17.4375, 19.2969, 18.7344, 20.5625,\n",
+            "         22.1094, 26.0938],\n",
+            "        [20.4844, 26.0938, 20.3906, 19.7812, 19.0938, 20.3750, 18.7656, 20.1719,\n",
+            "         20.6562, 24.8281],\n",
+            "        [26.2969, 18.8438, 23.6562, 17.3125, 18.4219, 18.0000, 16.7812, 17.9219,\n",
+            "         20.6562, 18.4688],\n",
+            "        [18.0781, 19.4531, 20.1406, 19.4375, 20.1562, 20.9531, 16.5625, 27.8438,\n",
+            "         18.0312, 19.4219],\n",
+            "        [19.7656, 18.9375, 27.9062, 19.3438, 19.6875, 20.3906, 20.8906, 19.5469,\n",
+            "         18.8125, 18.4375],\n",
+            "        [19.7656, 18.9688, 20.8125, 18.4375, 20.2344, 19.0312, 18.7812, 19.4219,\n",
+            "         22.9531, 18.2656],\n",
+            "        [20.7031, 20.8750, 23.4062, 24.3594, 25.0781, 24.7031, 22.8906, 23.3906,\n",
+            "         20.6094, 19.7031],\n",
+            "        [28.5469, 21.5469, 24.5312, 20.1094, 21.1562, 20.3594, 21.2344, 20.0312,\n",
+            "         21.7656, 20.2188],\n",
+            "        [27.2344, 23.7969, 23.4062, 20.5469, 21.2344, 21.1406, 20.2812, 23.4531,\n",
+            "         24.2812, 24.8125],\n",
+            "        [20.0938, 20.3125, 23.3125, 26.8750, 27.4531, 25.4062, 22.9219, 23.9062,\n",
+            "         19.7656, 19.0000],\n",
+            "        [20.4062, 20.2031, 26.7812, 20.7969, 23.8750, 21.1094, 18.9844, 22.8594,\n",
+            "         19.7500, 20.0938],\n",
+            "        [21.0938, 19.4531, 29.2969, 21.0312, 21.1094, 21.0469, 20.6406, 19.6250,\n",
+            "         19.3281, 19.4219],\n",
+            "        [20.1875, 19.8906, 24.0781, 23.5469, 28.3125, 25.2344, 21.7500, 23.4688,\n",
+            "         19.5000, 19.9062],\n",
+            "        [29.0781, 21.3438, 24.5156, 20.2188, 21.6875, 21.4531, 20.7656, 21.4531,\n",
+            "         23.7969, 21.3750],\n",
+            "        [19.2969, 19.2188, 20.9219, 21.8438, 21.4531, 27.2812, 20.2969, 21.8594,\n",
+            "         18.5938, 19.9688]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[5],\n",
+            "        [6],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [8],\n",
+            "        [5],\n",
+            "        [2],\n",
+            "        [6],\n",
+            "        [5],\n",
+            "        [3],\n",
+            "        [9],\n",
+            "        [1],\n",
+            "        [0],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [8],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [0],\n",
+            "        [4],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [5]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[5, 6, 8, 7, 4, 1, 8, 1, 3, 2, 4, 8, 5, 2, 6, 5, 3, 9, 1, 0, 7, 2, 8, 4,\n",
+            "         0, 0, 4, 2, 2, 4, 0, 5],\n",
+            "        [7, 5, 0, 5, 2, 9, 0, 9, 5, 0, 2, 1, 3, 6, 3, 7, 5, 1, 9, 2, 5, 6, 2, 5,\n",
+            "         2, 9, 3, 4, 4, 5, 2, 7],\n",
+            "        [3, 3, 2, 4, 7, 8, 1, 2, 2, 6, 0, 9, 7, 5, 5, 4, 7, 8, 8, 8, 4, 5, 4, 3,\n",
+            "         8, 8, 5, 7, 0, 2, 8, 3],\n",
+            "        [4, 4, 9, 1, 5, 2, 5, 5, 7, 7, 6, 5, 6, 3, 2, 3, 2, 7, 0, 1, 2, 0, 0, 2,\n",
+            "         1, 1, 7, 5, 5, 3, 4, 4],\n",
+            "        [6, 2, 1, 0, 6, 4, 2, 3, 6, 8, 7, 2, 2, 4, 4, 2, 6, 2, 2, 9, 1, 4, 7, 7,\n",
+            "         6, 7, 2, 3, 3, 7, 5, 2]], device='cuda:0')\n",
+            "tensor([9, 7, 1, 8, 4, 5, 5, 9, 8, 5, 7, 8, 0, 9, 8, 9, 1, 6, 3, 8, 0, 3, 4, 4,\n",
+            "        8, 4, 8, 9, 8, 6, 0, 0], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[18.5938, 22.5625, 20.3750, 19.0781, 18.4531, 19.2812, 18.5469, 20.0156,\n",
+            "         21.0156, 27.4375],\n",
+            "        [18.6562, 19.3594, 21.6250, 20.0312, 22.0469, 21.9219, 15.9766, 29.3750,\n",
+            "         18.0781, 19.3906],\n",
+            "        [19.6406, 26.3281, 20.9375, 18.8281, 19.7500, 18.4844, 20.2812, 19.0156,\n",
+            "         19.9531, 21.1406],\n",
+            "        [18.8594, 18.6094, 19.3281, 17.7188, 17.4375, 18.3125, 16.2656, 18.3594,\n",
+            "         26.2969, 17.9375],\n",
+            "        [20.6250, 21.2500, 23.1875, 23.8125, 27.3281, 24.5469, 19.4219, 27.1719,\n",
+            "         20.8906, 20.9219],\n",
+            "        [19.7031, 19.8906, 21.8750, 22.3281, 21.8594, 27.5938, 21.7812, 22.1094,\n",
+            "         19.2188, 19.2031],\n",
+            "        [22.0156, 21.5781, 24.7344, 24.0000, 21.4844, 25.5156, 23.6406, 23.2969,\n",
+            "         22.5938, 20.2656],\n",
+            "        [17.9531, 21.7344, 18.6562, 17.3594, 17.4219, 17.6250, 17.3594, 18.2500,\n",
+            "         20.0156, 25.5938],\n",
+            "        [21.7188, 22.1094, 20.8594, 18.2969, 19.5469, 19.7969, 18.4844, 19.7812,\n",
+            "         20.1719, 20.9375],\n",
+            "        [20.2812, 20.5469, 22.9375, 23.6406, 21.3750, 27.4219, 22.0000, 22.1719,\n",
+            "         20.3906, 19.7969],\n",
+            "        [19.2812, 19.6719, 21.7969, 22.7812, 25.2969, 23.7500, 18.3594, 28.9062,\n",
+            "         18.6719, 18.6094],\n",
+            "        [19.7812, 21.3750, 18.9062, 18.4688, 17.6562, 19.2500, 17.5312, 18.8906,\n",
+            "         26.8125, 19.2812],\n",
+            "        [26.4219, 22.6250, 21.9062, 20.1719, 20.5469, 20.2188, 18.5469, 20.6719,\n",
+            "         23.9688, 25.4375],\n",
+            "        [18.7344, 24.4375, 19.3125, 18.2812, 17.3125, 18.2500, 18.5781, 18.1250,\n",
+            "         19.4062, 25.2500],\n",
+            "        [18.3750, 18.5625, 18.1094, 17.2344, 17.7969, 17.8750, 16.3281, 18.0156,\n",
+            "         24.4688, 17.0000],\n",
+            "        [20.7812, 23.8750, 19.5312, 19.0000, 18.4688, 19.4531, 17.5312, 20.3906,\n",
+            "         21.5000, 27.1719],\n",
+            "        [18.8750, 25.1562, 19.2500, 17.8594, 18.1719, 19.4062, 16.7500, 17.3125,\n",
+            "         19.2500, 21.6719],\n",
+            "        [19.2812, 19.8906, 22.5938, 21.4844, 18.6719, 22.2969, 27.5781, 20.0781,\n",
+            "         20.0000, 19.3125],\n",
+            "        [18.5156, 20.0938, 22.9531, 24.9688, 22.2656, 23.3281, 21.0938, 22.5469,\n",
+            "         19.4219, 19.3750],\n",
+            "        [22.0156, 22.5469, 20.9062, 21.3594, 19.2812, 20.7188, 19.3750, 20.2969,\n",
+            "         27.0156, 20.5000],\n",
+            "        [24.6719, 21.7500, 22.6562, 18.8750, 19.3438, 19.4844, 19.0938, 21.9062,\n",
+            "         23.1094, 21.1562],\n",
+            "        [21.2188, 20.9219, 22.8594, 27.9375, 20.8906, 24.8438, 21.8750, 23.2031,\n",
+            "         21.9688, 20.5625],\n",
+            "        [19.0000, 18.5156, 21.4844, 21.2344, 26.9688, 22.2344, 19.2344, 22.5000,\n",
+            "         18.6719, 17.6719],\n",
+            "        [19.0156, 19.9844, 23.3281, 22.0000, 27.6875, 24.8125, 20.6875, 27.7500,\n",
+            "         19.5000, 19.0781],\n",
+            "        [19.3438, 19.8906, 19.3125, 18.5625, 17.2500, 18.3906, 17.2344, 18.2969,\n",
+            "         24.7188, 20.1562],\n",
+            "        [18.7031, 19.4844, 23.5938, 22.7500, 28.7969, 23.9844, 22.1406, 25.6250,\n",
+            "         18.7969, 18.5312],\n",
+            "        [19.2969, 18.5938, 19.2656, 18.2500, 16.6406, 18.5000, 16.7656, 18.0000,\n",
+            "         24.5469, 16.0469],\n",
+            "        [20.3906, 25.7500, 20.3281, 19.1719, 19.1094, 21.3281, 20.8906, 21.1094,\n",
+            "         20.5625, 27.1719],\n",
+            "        [20.8281, 21.5000, 19.7969, 19.5469, 19.2344, 19.7812, 18.3750, 19.5000,\n",
+            "         26.6719, 20.1250],\n",
+            "        [16.5156, 18.9688, 22.3438, 21.1875, 18.4062, 21.0938, 28.6562, 17.3594,\n",
+            "         18.1406, 18.0938],\n",
+            "        [27.3594, 20.8281, 24.8438, 21.3750, 22.2344, 21.3906, 21.3125, 21.7812,\n",
+            "         23.5312, 19.9062],\n",
+            "        [24.1719, 18.8281, 22.8281, 17.4688, 17.0781, 17.8906, 20.1250, 18.5312,\n",
+            "         21.5469, 17.5938]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[9],\n",
+            "        [7],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [4],\n",
+            "        [5],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [1],\n",
+            "        [5],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [1],\n",
+            "        [6],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [0],\n",
+            "        [3],\n",
+            "        [4],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [4],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [6],\n",
+            "        [0],\n",
+            "        [0]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[9, 7, 1, 8, 4, 5, 5, 9, 1, 5, 7, 8, 0, 9, 8, 9, 1, 6, 3, 8, 0, 3, 4, 7,\n",
+            "         8, 4, 8, 9, 8, 6, 0, 0],\n",
+            "        [1, 4, 9, 2, 7, 3, 2, 1, 0, 3, 4, 1, 9, 1, 1, 1, 9, 2, 5, 1, 8, 5, 7, 4,\n",
+            "         9, 7, 0, 1, 1, 2, 2, 2],\n",
+            "        [8, 5, 2, 0, 5, 7, 3, 8, 9, 2, 5, 0, 8, 8, 0, 8, 5, 5, 2, 0, 2, 7, 5, 5,\n",
+            "         1, 5, 2, 5, 0, 3, 8, 8],\n",
+            "        [2, 2, 6, 1, 3, 2, 6, 2, 2, 7, 3, 9, 1, 2, 2, 0, 8, 3, 7, 3, 7, 2, 2, 2,\n",
+            "         0, 2, 1, 7, 9, 5, 4, 6],\n",
+            "        [7, 3, 8, 7, 2, 4, 7, 7, 8, 6, 2, 5, 2, 0, 7, 7, 2, 7, 4, 2, 1, 8, 3, 3,\n",
+            "         2, 3, 5, 6, 2, 1, 7, 1]], device='cuda:0')\n",
+            "tensor([8, 2, 7, 4, 2, 5, 6, 0, 5, 8, 4, 1, 9, 0, 1, 4, 4, 8, 4, 9, 6, 0, 7, 7,\n",
+            "        6, 8, 9, 6, 2, 0, 4, 9], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[19.8750, 19.9375, 21.0625, 19.0625, 17.6250, 18.8906, 17.1875, 19.6406,\n",
+            "         26.8906, 21.0625],\n",
+            "        [21.0469, 21.2812, 28.1562, 23.2656, 23.5938, 23.7500, 22.4688, 24.4688,\n",
+            "         20.2812, 20.4062],\n",
+            "        [19.3125, 18.2812, 21.2031, 20.5000, 20.4531, 21.3594, 19.1094, 25.2656,\n",
+            "         18.2656, 19.4844],\n",
+            "        [20.8438, 20.3906, 24.2031, 25.2812, 27.0469, 25.9531, 25.0156, 23.2969,\n",
+            "         19.9844, 18.3594],\n",
+            "        [23.2812, 23.3906, 26.4219, 24.7656, 25.3438, 25.8906, 23.7188, 25.8281,\n",
+            "         23.2344, 22.5000],\n",
+            "        [18.7969, 19.5312, 20.2500, 23.0000, 22.2812, 26.8750, 19.7500, 23.1562,\n",
+            "         18.6562, 18.2656],\n",
+            "        [20.0781, 20.7344, 22.0000, 22.5156, 21.6562, 23.0000, 22.2031, 21.5938,\n",
+            "         21.0469, 19.7656],\n",
+            "        [24.0312, 21.5625, 24.0156, 21.0938, 19.4375, 19.8438, 21.4219, 19.6250,\n",
+            "         21.4531, 20.1875],\n",
+            "        [19.6094, 20.5938, 20.9375, 21.6250, 18.5625, 27.1406, 19.9219, 20.6094,\n",
+            "         19.3750, 19.4375],\n",
+            "        [17.8125, 18.2188, 18.7188, 16.6406, 17.1562, 17.4688, 15.8984, 17.6562,\n",
+            "         25.0938, 17.8906],\n",
+            "        [20.3438, 19.6250, 23.7656, 24.8906, 27.7812, 24.1406, 20.0000, 26.8438,\n",
+            "         19.2344, 19.0781],\n",
+            "        [20.1250, 27.0781, 22.1094, 20.4531, 21.4844, 21.7969, 22.1719, 21.1094,\n",
+            "         20.4375, 24.7656],\n",
+            "        [18.8906, 22.6406, 19.7188, 18.5781, 18.8750, 18.8281, 17.0781, 19.7656,\n",
+            "         20.3125, 25.7812],\n",
+            "        [27.2500, 19.6250, 23.5625, 18.3594, 19.0781, 18.8594, 17.9062, 19.1406,\n",
+            "         22.6719, 18.2969],\n",
+            "        [18.2500, 23.9688, 19.6250, 17.9531, 17.2969, 18.8438, 17.7188, 18.5938,\n",
+            "         17.7500, 20.4375],\n",
+            "        [19.0781, 19.1875, 20.5156, 24.6094, 25.5000, 23.8594, 19.4219, 24.0781,\n",
+            "         19.8750, 18.3438],\n",
+            "        [19.0625, 20.0312, 21.3281, 22.1406, 28.4531, 22.3750, 18.9688, 25.0781,\n",
+            "         18.5938, 18.7812],\n",
+            "        [22.6875, 20.0000, 23.0781, 20.5312, 19.8438, 20.7656, 21.0000, 20.1719,\n",
+            "         24.5469, 18.5625],\n",
+            "        [18.3750, 20.1875, 22.4688, 20.2344, 30.0000, 21.9219, 19.2656, 22.8594,\n",
+            "         18.6875, 18.5625],\n",
+            "        [19.4531, 24.1406, 20.9062, 19.9531, 19.0312, 19.7812, 19.8594, 20.4844,\n",
+            "         19.6562, 26.3438],\n",
+            "        [20.0000, 21.0156, 24.1875, 22.3281, 21.4375, 22.4219, 28.2500, 21.3750,\n",
+            "         19.7656, 19.1094],\n",
+            "        [28.7188, 23.5469, 23.2812, 20.7969, 20.0312, 21.6719, 19.8750, 21.2969,\n",
+            "         24.0312, 22.1719],\n",
+            "        [18.5469, 19.6875, 20.2031, 20.1875, 20.7969, 21.7031, 17.6094, 28.1406,\n",
+            "         18.9844, 19.3281],\n",
+            "        [19.8594, 20.0938, 21.3594, 20.2500, 20.8594, 22.2188, 17.0312, 29.1406,\n",
+            "         19.2969, 19.8594],\n",
+            "        [18.1562, 18.4531, 22.6250, 21.7812, 21.7656, 20.9219, 23.0938, 21.3438,\n",
+            "         19.4531, 17.8125],\n",
+            "        [21.5781, 20.8750, 22.0469, 19.5938, 19.0156, 19.1719, 18.4375, 20.6562,\n",
+            "         29.1719, 21.0312],\n",
+            "        [17.8281, 22.2344, 18.7344, 17.7188, 18.1719, 18.5781, 16.9688, 19.6875,\n",
+            "         19.4844, 25.1406],\n",
+            "        [19.9062, 20.5469, 23.5625, 23.8438, 21.9062, 22.7969, 26.0156, 21.8281,\n",
+            "         20.1406, 19.3750],\n",
+            "        [19.6094, 20.3125, 25.4688, 24.1875, 27.3750, 24.7812, 21.5938, 26.4375,\n",
+            "         19.2812, 20.2656],\n",
+            "        [25.4062, 19.2969, 23.1094, 19.6719, 19.9688, 18.8594, 20.1250, 19.4219,\n",
+            "         22.0781, 17.5469],\n",
+            "        [19.4688, 20.4219, 22.3750, 19.7656, 29.8438, 21.8906, 19.5312, 22.7812,\n",
+            "         18.5312, 19.7031],\n",
+            "        [17.8438, 22.8750, 19.0469, 17.7656, 18.1562, 18.8125, 17.7500, 18.7969,\n",
+            "         19.3906, 26.6562]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[8],\n",
+            "        [2],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [2],\n",
+            "        [5],\n",
+            "        [5],\n",
+            "        [0],\n",
+            "        [5],\n",
+            "        [8],\n",
+            "        [4],\n",
+            "        [1],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [1],\n",
+            "        [4],\n",
+            "        [4],\n",
+            "        [8],\n",
+            "        [4],\n",
+            "        [9],\n",
+            "        [6],\n",
+            "        [0],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [6],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [4],\n",
+            "        [9]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[8, 2, 7, 4, 2, 5, 5, 0, 5, 8, 4, 1, 9, 0, 1, 4, 4, 8, 4, 9, 6, 0, 7, 7,\n",
+            "         6, 8, 9, 6, 4, 0, 4, 9],\n",
+            "        [9, 7, 5, 5, 5, 7, 3, 2, 3, 2, 7, 9, 1, 2, 9, 3, 7, 2, 7, 1, 2, 8, 5, 5,\n",
+            "         2, 2, 1, 3, 7, 2, 7, 1],\n",
+            "        [2, 5, 2, 3, 7, 3, 6, 1, 2, 1, 3, 6, 8, 8, 2, 7, 5, 0, 2, 2, 5, 1, 4, 2,\n",
+            "         3, 0, 7, 2, 2, 8, 2, 8],\n",
+            "        [1, 4, 3, 6, 4, 4, 2, 8, 7, 9, 5, 2, 7, 1, 5, 5, 3, 6, 5, 7, 3, 2, 2, 4,\n",
+            "         4, 9, 8, 5, 5, 6, 5, 2],\n",
+            "        [0, 3, 4, 2, 3, 2, 4, 6, 1, 0, 2, 5, 2, 7, 7, 2, 2, 5, 3, 3, 4, 9, 3, 3,\n",
+            "         7, 1, 2, 4, 3, 4, 1, 5]], device='cuda:0')\n",
+            "tensor([4, 9, 3, 9, 6, 6, 7, 0, 9, 7, 1, 8, 6, 0, 6, 7, 4, 1, 9, 4, 6, 7, 9, 8,\n",
+            "        3, 9, 2, 1, 2, 7, 6, 1], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[20.4688, 18.6094, 23.5938, 25.2500, 24.8281, 23.1406, 19.6406, 22.8750,\n",
+            "         21.0156, 18.2188],\n",
+            "        [18.3125, 23.3906, 19.7344, 17.8594, 18.0938, 18.5781, 17.6250, 19.2812,\n",
+            "         19.6094, 27.7812],\n",
+            "        [19.9219, 19.7344, 21.3906, 22.5000, 20.3281, 21.9375, 20.4375, 20.3594,\n",
+            "         20.1562, 19.2500],\n",
+            "        [17.7969, 22.2500, 18.5781, 17.2344, 17.2500, 18.0781, 16.4688, 18.7188,\n",
+            "         19.4844, 26.3438],\n",
+            "        [21.9844, 22.5625, 25.4219, 24.5625, 23.6406, 24.3125, 25.3750, 23.7500,\n",
+            "         22.7031, 21.8438],\n",
+            "        [19.8438, 20.0000, 22.9062, 23.0312, 20.8750, 22.2969, 26.8281, 20.7969,\n",
+            "         20.0938, 19.3438],\n",
+            "        [20.8594, 21.6875, 22.8750, 23.5625, 24.1094, 24.7500, 19.0625, 30.3438,\n",
+            "         20.6562, 20.7031],\n",
+            "        [28.4219, 21.5469, 24.7188, 19.8438, 20.7969, 21.0625, 20.2188, 21.0469,\n",
+            "         23.2812, 20.8750],\n",
+            "        [19.7812, 24.6406, 21.7188, 21.0625, 20.5625, 21.8594, 20.5781, 21.6250,\n",
+            "         21.1250, 26.2969],\n",
+            "        [19.1250, 18.9531, 20.5469, 20.6406, 22.6406, 22.7969, 18.3125, 28.2812,\n",
+            "         18.7188, 18.5000],\n",
+            "        [20.4688, 26.3594, 19.5312, 18.5312, 17.3281, 19.2656, 19.4531, 17.6719,\n",
+            "         20.4688, 22.3125],\n",
+            "        [21.6719, 21.5312, 22.3438, 19.9219, 21.3125, 21.3906, 21.1875, 20.5000,\n",
+            "         25.2188, 20.1562],\n",
+            "        [17.7969, 18.5312, 23.0938, 19.4062, 17.4688, 20.2344, 28.2188, 17.5469,\n",
+            "         17.3906, 18.2656],\n",
+            "        [24.7188, 19.2969, 22.5469, 19.7344, 20.0156, 20.0156, 19.8594, 20.6406,\n",
+            "         21.9062, 18.2812],\n",
+            "        [21.1875, 22.1562, 24.5625, 22.5938, 21.5938, 22.5625, 26.3125, 22.0156,\n",
+            "         22.4219, 20.6719],\n",
+            "        [19.7188, 19.4531, 21.9531, 23.4844, 28.1875, 25.8594, 19.7969, 28.0312,\n",
+            "         19.4531, 18.5469],\n",
+            "        [22.2812, 22.8594, 23.8594, 23.6406, 24.4375, 25.1719, 22.9844, 27.8750,\n",
+            "         22.0781, 21.0938],\n",
+            "        [17.4531, 24.6875, 18.6562, 18.1719, 17.8750, 18.1719, 18.2188, 17.3125,\n",
+            "         17.6719, 19.9844],\n",
+            "        [21.9531, 21.3125, 20.9531, 21.8750, 20.2344, 20.8906, 19.8906, 21.9844,\n",
+            "         23.0469, 21.6875],\n",
+            "        [22.9531, 21.3594, 26.5781, 23.9375, 28.2188, 24.5781, 23.6250, 24.7969,\n",
+            "         21.4062, 20.7812],\n",
+            "        [19.8438, 20.2969, 23.3281, 23.9688, 22.7500, 24.7969, 26.3906, 22.8438,\n",
+            "         20.0312, 19.3438],\n",
+            "        [19.1094, 19.4062, 21.0938, 20.2656, 21.6719, 21.6562, 17.3906, 28.0469,\n",
+            "         18.8594, 19.1719],\n",
+            "        [18.2031, 22.4531, 18.6719, 17.5625, 18.1875, 18.6406, 16.9844, 19.8281,\n",
+            "         19.6719, 26.8906],\n",
+            "        [18.9844, 19.3750, 19.0781, 17.2500, 17.1094, 18.2812, 16.7500, 17.7812,\n",
+            "         22.8438, 17.6875],\n",
+            "        [22.8125, 22.4688, 24.5781, 25.4219, 24.0469, 24.7500, 24.7188, 22.8594,\n",
+            "         22.8750, 22.0625],\n",
+            "        [18.1875, 22.6562, 19.0625, 17.7344, 17.1875, 18.3594, 16.5938, 18.6875,\n",
+            "         19.9062, 26.3594],\n",
+            "        [20.5781, 18.6406, 28.3906, 21.7656, 20.2969, 21.9688, 20.9219, 19.3125,\n",
+            "         19.0312, 17.9062],\n",
+            "        [19.4219, 25.7969, 21.1562, 18.8750, 19.5000, 19.8594, 18.7188, 19.0938,\n",
+            "         19.4219, 22.3594],\n",
+            "        [19.3906, 19.5156, 25.1875, 18.7344, 21.3281, 19.4531, 18.7031, 21.9844,\n",
+            "         18.3906, 18.2031],\n",
+            "        [17.3125, 18.7031, 20.4219, 19.8906, 20.8906, 20.2031, 17.4375, 27.5312,\n",
+            "         17.6875, 17.7500],\n",
+            "        [22.2344, 19.8750, 23.5000, 21.3281, 23.5000, 21.9688, 22.4531, 22.8906,\n",
+            "         20.2656, 19.3906],\n",
+            "        [21.4531, 26.3281, 21.6094, 18.9531, 20.2656, 20.2188, 19.0938, 21.0312,\n",
+            "         21.7812, 25.0781]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[3],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [9],\n",
+            "        [2],\n",
+            "        [6],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [6],\n",
+            "        [0],\n",
+            "        [6],\n",
+            "        [4],\n",
+            "        [7],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [4],\n",
+            "        [6],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [3],\n",
+            "        [9],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [1]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[3, 9, 3, 9, 2, 6, 7, 0, 9, 7, 1, 8, 6, 0, 6, 4, 7, 1, 8, 4, 6, 7, 9, 8,\n",
+            "         3, 9, 2, 1, 2, 7, 4, 1],\n",
+            "        [4, 1, 5, 1, 6, 3, 5, 2, 1, 5, 9, 2, 2, 2, 2, 7, 5, 9, 7, 2, 5, 4, 1, 1,\n",
+            "         5, 1, 5, 9, 7, 4, 2, 9],\n",
+            "        [2, 2, 2, 8, 3, 2, 4, 8, 5, 4, 8, 0, 5, 8, 3, 5, 4, 2, 0, 7, 3, 5, 7, 2,\n",
+            "         6, 8, 3, 2, 4, 2, 7, 8],\n",
+            "        [5, 8, 6, 7, 5, 5, 3, 1, 2, 3, 0, 1, 3, 7, 5, 3, 2, 6, 3, 5, 2, 2, 8, 0,\n",
+            "         2, 2, 6, 5, 1, 5, 6, 2],\n",
+            "        [7, 7, 7, 2, 7, 4, 2, 5, 7, 2, 2, 5, 1, 4, 8, 2, 3, 3, 9, 3, 7, 3, 2, 5,\n",
+            "         4, 7, 0, 4, 5, 3, 0, 0]], device='cuda:0')\n",
+            "tensor([0, 0, 5, 6, 0, 4, 3, 2, 8, 8, 0, 6, 9, 5, 2, 8, 7, 0, 6, 5, 9, 7, 2, 3,\n",
+            "        6, 9, 6, 2, 2, 4, 1, 0], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[21.1094, 19.1094, 21.2344, 18.8281, 19.2188, 18.7344, 20.1406, 19.1250,\n",
+            "         19.9531, 18.9531],\n",
+            "        [27.5000, 21.7969, 23.7344, 20.0625, 18.9375, 20.5000, 19.7188, 19.5781,\n",
+            "         21.1094, 21.4688],\n",
+            "        [18.3594, 18.3750, 19.5312, 20.0625, 19.3750, 25.6094, 18.6250, 20.7656,\n",
+            "         17.7031, 18.4531],\n",
+            "        [18.4062, 19.0000, 21.9375, 20.8438, 21.2031, 20.2812, 24.8750, 19.9531,\n",
+            "         18.8438, 17.2188],\n",
+            "        [28.6094, 21.3438, 24.2969, 20.5312, 19.8594, 20.8594, 19.7500, 20.2500,\n",
+            "         23.1875, 19.6406],\n",
+            "        [19.2031, 19.0781, 21.5625, 19.6406, 27.7500, 21.7188, 19.0938, 22.6875,\n",
+            "         18.1562, 19.2969],\n",
+            "        [21.6719, 23.0781, 22.9375, 26.0469, 23.0938, 24.1719, 22.8594, 23.2344,\n",
+            "         22.1406, 21.8906],\n",
+            "        [20.3438, 20.6250, 28.0312, 20.8750, 22.1406, 22.2031, 22.3906, 22.2969,\n",
+            "         20.0938, 20.9688],\n",
+            "        [20.2500, 20.6250, 20.9688, 19.0469, 19.2500, 19.6719, 17.6719, 19.9688,\n",
+            "         27.2188, 20.4688],\n",
+            "        [19.4688, 18.8750, 19.8750, 17.6250, 17.3594, 18.1406, 16.4062, 17.8750,\n",
+            "         26.0469, 16.9375],\n",
+            "        [27.9062, 22.4844, 24.4062, 18.7656, 19.8906, 19.9062, 17.3594, 20.8750,\n",
+            "         22.3594, 22.9375],\n",
+            "        [21.4219, 21.7188, 23.1250, 22.7812, 23.7656, 23.1875, 25.1875, 22.1719,\n",
+            "         21.3438, 20.3750],\n",
+            "        [20.3438, 20.9688, 20.3281, 18.8750, 18.0938, 19.2188, 20.5625, 18.9375,\n",
+            "         20.5000, 20.7188],\n",
+            "        [19.9844, 20.1875, 21.2500, 22.9219, 20.8750, 26.4062, 20.9531, 21.9375,\n",
+            "         19.6875, 19.4062],\n",
+            "        [19.6562, 19.3438, 28.2500, 20.1250, 19.4688, 19.8750, 21.2812, 18.5625,\n",
+            "         18.6875, 18.5938],\n",
+            "        [18.9219, 18.9844, 19.2969, 17.9844, 17.5781, 18.3594, 16.7344, 17.8906,\n",
+            "         24.7344, 18.5156],\n",
+            "        [17.5938, 20.0000, 19.8438, 18.4375, 19.6719, 20.5000, 16.3125, 25.4062,\n",
+            "         19.1406, 19.2969],\n",
+            "        [22.2188, 18.2656, 19.9375, 17.2031, 16.8906, 18.0312, 16.6719, 17.9062,\n",
+            "         24.5312, 17.9688],\n",
+            "        [20.0625, 20.5781, 22.6719, 22.7812, 22.3125, 22.9531, 26.8125, 22.2344,\n",
+            "         20.1719, 19.3438],\n",
+            "        [19.1094, 20.5156, 21.3438, 22.5000, 19.2188, 27.2812, 20.8750, 21.0625,\n",
+            "         19.4219, 18.4219],\n",
+            "        [22.2656, 26.0312, 21.3750, 20.9531, 20.2344, 21.0312, 20.0312, 21.6094,\n",
+            "         23.4844, 26.7031],\n",
+            "        [19.0156, 19.4375, 20.7812, 21.6250, 26.5312, 23.5156, 19.6719, 27.5625,\n",
+            "         18.2188, 17.7969],\n",
+            "        [21.7188, 23.6094, 25.5938, 23.9688, 23.5781, 23.6250, 23.8750, 24.5625,\n",
+            "         22.0000, 22.4844],\n",
+            "        [18.0000, 19.2656, 22.1562, 27.0781, 25.7656, 23.0469, 19.3906, 23.0312,\n",
+            "         18.2656, 18.3438],\n",
+            "        [19.6094, 19.6094, 22.7656, 21.5312, 24.4688, 21.1562, 21.0781, 21.8125,\n",
+            "         18.8438, 18.6719],\n",
+            "        [21.7656, 24.9219, 21.9844, 20.2969, 20.0625, 20.5781, 20.3281, 21.9062,\n",
+            "         22.9688, 28.7188],\n",
+            "        [19.5000, 21.2500, 21.9062, 21.5156, 21.1094, 21.8906, 24.4062, 21.0625,\n",
+            "         21.0000, 19.9219],\n",
+            "        [21.3438, 21.9219, 22.9531, 21.6875, 20.1875, 22.8594, 21.4219, 22.3438,\n",
+            "         20.7969, 20.5000],\n",
+            "        [19.0156, 18.4219, 27.3281, 19.2812, 16.5156, 19.5312, 20.6875, 17.2188,\n",
+            "         17.7188, 17.7656],\n",
+            "        [19.0469, 20.1719, 22.0000, 20.7188, 29.9219, 22.3906, 19.6406, 22.8125,\n",
+            "         18.6719, 19.4062],\n",
+            "        [18.2812, 25.6250, 20.2812, 19.4375, 19.2500, 19.2188, 19.1875, 18.2812,\n",
+            "         19.2188, 20.7812],\n",
+            "        [26.2812, 27.2500, 23.4375, 20.7969, 20.6875, 21.5625, 21.4531, 22.0000,\n",
+            "         25.6250, 27.3125]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[2],\n",
+            "        [0],\n",
+            "        [5],\n",
+            "        [6],\n",
+            "        [0],\n",
+            "        [4],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [0],\n",
+            "        [6],\n",
+            "        [1],\n",
+            "        [5],\n",
+            "        [2],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [6],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [3],\n",
+            "        [4],\n",
+            "        [9],\n",
+            "        [6],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [1],\n",
+            "        [9]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[2, 0, 5, 6, 0, 4, 3, 2, 8, 8, 0, 6, 1, 5, 2, 8, 7, 8, 6, 5, 9, 7, 2, 3,\n",
+            "         4, 9, 6, 2, 2, 4, 1, 9],\n",
+            "        [0, 2, 7, 2, 2, 7, 5, 6, 2, 2, 2, 4, 9, 3, 6, 2, 5, 0, 5, 3, 1, 4, 7, 4,\n",
+            "         2, 1, 2, 5, 6, 7, 9, 1],\n",
+            "        [6, 1, 3, 4, 8, 5, 7, 7, 1, 0, 9, 5, 6, 7, 3, 1, 1, 2, 3, 2, 8, 5, 3, 5,\n",
+            "         7, 8, 5, 7, 5, 5, 2, 0],\n",
+            "        [8, 9, 2, 3, 1, 2, 4, 5, 9, 1, 1, 2, 8, 2, 5, 0, 2, 1, 2, 7, 0, 3, 6, 7,\n",
+            "         3, 2, 3, 1, 3, 2, 3, 8],\n",
+            "        [4, 8, 4, 5, 5, 3, 1, 4, 0, 5, 8, 3, 0, 6, 0, 9, 4, 5, 4, 6, 7, 2, 5, 2,\n",
+            "         5, 7, 1, 3, 0, 3, 4, 2]], device='cuda:0')\n",
+            "tensor([5, 0, 8, 9, 3, 5, 9, 3, 8, 1, 6, 3, 7, 5, 6, 2, 0, 2, 8, 2, 8, 7, 7, 8,\n",
+            "        1, 0, 8, 9, 7, 0, 3, 8], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[19.3125, 21.0312, 22.0469, 24.1406, 22.3906, 27.6719, 22.8281, 24.4219,\n",
+            "         19.9375, 19.7812],\n",
+            "        [27.2656, 23.7031, 22.2031, 19.2812, 18.5469, 20.4219, 18.3281, 20.6250,\n",
+            "         22.0469, 22.2500],\n",
+            "        [19.0625, 19.7500, 19.2656, 17.2656, 17.5938, 17.9688, 16.6719, 18.8438,\n",
+            "         27.2969, 20.0625],\n",
+            "        [22.0469, 22.4531, 18.8438, 17.5312, 18.3750, 19.3438, 17.4531, 19.0781,\n",
+            "         21.7344, 24.3750],\n",
+            "        [17.5625, 18.2031, 19.1875, 24.8906, 21.6875, 24.4375, 17.3438, 22.0469,\n",
+            "         18.0781, 17.0469],\n",
+            "        [20.4531, 19.2188, 21.5469, 22.3594, 21.0938, 27.1719, 21.5781, 22.2812,\n",
+            "         19.4531, 19.0625],\n",
+            "        [19.0156, 23.2031, 19.2500, 19.0312, 18.2500, 19.1562, 19.4844, 20.2188,\n",
+            "         20.2969, 24.7656],\n",
+            "        [18.4688, 19.5781, 21.6562, 25.6875, 19.0781, 22.0781, 20.9219, 20.7969,\n",
+            "         19.5469, 18.6406],\n",
+            "        [20.2344, 19.1562, 20.5938, 19.2500, 18.8438, 18.9688, 17.5625, 18.6875,\n",
+            "         25.0781, 17.9062],\n",
+            "        [18.9375, 25.5625, 19.9844, 18.7812, 18.9062, 19.9375, 18.3594, 18.4688,\n",
+            "         19.1719, 21.5156],\n",
+            "        [20.6562, 21.2969, 26.0781, 23.8438, 24.7969, 23.3594, 25.8906, 22.3125,\n",
+            "         19.8906, 19.8594],\n",
+            "        [19.4688, 19.8438, 21.4531, 25.6562, 20.7656, 21.9844, 21.2188, 21.2500,\n",
+            "         19.0000, 18.9375],\n",
+            "        [18.7344, 20.7969, 20.7188, 20.3281, 21.3438, 21.1406, 17.7500, 27.2656,\n",
+            "         19.2344, 19.6875],\n",
+            "        [21.1094, 20.7344, 23.2188, 24.6562, 24.1250, 25.1875, 23.9375, 23.1562,\n",
+            "         20.7031, 20.5469],\n",
+            "        [20.8281, 22.2031, 23.4062, 22.7344, 22.7344, 23.3125, 26.2969, 22.3906,\n",
+            "         21.3750, 20.7031],\n",
+            "        [21.9062, 23.3125, 24.1406, 22.5469, 20.4062, 22.5938, 22.7500, 22.4688,\n",
+            "         21.1406, 21.2344],\n",
+            "        [28.6562, 23.2344, 25.1719, 22.6875, 22.0781, 24.3281, 20.1250, 24.9219,\n",
+            "         23.8906, 23.6406],\n",
+            "        [19.0000, 19.8281, 24.6875, 20.5938, 23.1250, 22.0469, 23.3438, 21.8125,\n",
+            "         18.3281, 19.1719],\n",
+            "        [21.0938, 22.1094, 20.4844, 18.4844, 18.9688, 19.4375, 18.7188, 18.8750,\n",
+            "         23.7812, 20.7344],\n",
+            "        [18.8750, 19.2344, 27.6250, 21.2812, 19.5312, 21.0625, 22.1094, 20.1562,\n",
+            "         19.0625, 18.8125],\n",
+            "        [24.3438, 24.7812, 23.2344, 20.9531, 19.7500, 21.5938, 22.7188, 19.7500,\n",
+            "         23.3594, 21.6719],\n",
+            "        [20.7500, 20.6562, 22.7500, 21.0312, 22.2031, 22.3594, 19.6094, 25.2500,\n",
+            "         20.0469, 20.6250],\n",
+            "        [19.6250, 20.8125, 21.0156, 20.7031, 20.5156, 21.9844, 21.2812, 23.3125,\n",
+            "         20.7188, 19.4531],\n",
+            "        [19.7344, 20.4844, 20.2344, 17.6094, 19.9531, 18.9688, 17.7188, 18.7188,\n",
+            "         23.2031, 18.2188],\n",
+            "        [21.6250, 27.0156, 21.5469, 19.8281, 21.3906, 21.2500, 21.4062, 20.1094,\n",
+            "         21.0156, 25.9062],\n",
+            "        [22.4688, 20.7344, 21.8438, 18.8281, 19.7656, 19.8281, 20.2812, 20.4219,\n",
+            "         22.3125, 20.1562],\n",
+            "        [23.9844, 19.6406, 20.7969, 18.4062, 18.3281, 18.9688, 19.0625, 18.3906,\n",
+            "         25.6719, 18.3750],\n",
+            "        [20.2656, 23.0469, 20.0625, 18.5312, 17.0312, 19.4844, 18.4531, 19.5156,\n",
+            "         21.7188, 26.5000],\n",
+            "        [16.7969, 19.6562, 18.6875, 18.7969, 19.3438, 20.2344, 16.4531, 25.6094,\n",
+            "         17.5781, 17.5625],\n",
+            "        [26.0156, 20.2031, 22.2969, 19.1406, 19.2656, 19.1562, 22.3594, 19.0156,\n",
+            "         21.9844, 17.2969],\n",
+            "        [17.3906, 18.5156, 20.5312, 26.2812, 18.7812, 23.3125, 18.7656, 20.4375,\n",
+            "         18.5625, 17.0000],\n",
+            "        [21.4688, 20.8750, 21.3281, 19.3438, 19.5781, 20.4219, 20.0938, 19.9219,\n",
+            "         25.4688, 20.7812]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[5],\n",
+            "        [0],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [5],\n",
+            "        [6],\n",
+            "        [2],\n",
+            "        [0],\n",
+            "        [2],\n",
+            "        [8],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [1],\n",
+            "        [0],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [3],\n",
+            "        [8]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[5, 0, 8, 9, 3, 5, 9, 3, 8, 1, 2, 3, 7, 5, 6, 2, 0, 2, 8, 2, 1, 7, 7, 8,\n",
+            "         1, 0, 8, 9, 7, 0, 3, 8],\n",
+            "        [7, 1, 9, 1, 5, 3, 1, 5, 2, 9, 6, 5, 4, 3, 2, 1, 2, 6, 1, 6, 0, 2, 5, 1,\n",
+            "         9, 8, 0, 1, 5, 6, 5, 0],\n",
+            "        [3, 9, 1, 0, 7, 7, 8, 2, 0, 2, 4, 2, 5, 4, 5, 6, 7, 4, 0, 3, 8, 5, 6, 2,\n",
+            "         0, 2, 2, 8, 1, 2, 2, 2],\n",
+            "        [6, 2, 2, 8, 4, 6, 7, 6, 3, 5, 3, 7, 1, 6, 4, 5, 5, 5, 9, 5, 2, 4, 2, 4,\n",
+            "         2, 1, 1, 0, 4, 8, 7, 1],\n",
+            "        [4, 8, 0, 5, 2, 2, 6, 7, 1, 8, 5, 6, 2, 2, 3, 3, 8, 7, 2, 7, 6, 3, 1, 0,\n",
+            "         6, 7, 6, 2, 3, 1, 4, 9]], device='cuda:0')\n",
+            "tensor([0, 5, 9, 5, 8, 4, 2, 0, 9, 2, 2, 4, 4, 9, 2, 2, 2, 5, 1, 3, 2, 0, 0, 4,\n",
+            "        0, 6, 5, 8, 0, 5, 8, 6], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[26.5781, 22.6719, 28.0469, 21.3281, 21.3594, 21.4062, 23.0938, 21.7812,\n",
+            "         23.8594, 21.3750],\n",
+            "        [19.1094, 18.7500, 21.9844, 21.7031, 23.7656, 26.7812, 21.6094, 22.5469,\n",
+            "         18.1562, 18.8438],\n",
+            "        [19.0156, 22.5000, 19.4688, 18.0938, 18.1094, 19.2969, 17.2812, 19.4062,\n",
+            "         19.7031, 25.6406],\n",
+            "        [17.6250, 17.8281, 19.9688, 20.6094, 17.7812, 24.8594, 19.9531, 18.7031,\n",
+            "         17.6406, 17.8594],\n",
+            "        [21.9844, 21.0156, 21.8906, 20.3438, 20.3438, 20.2969, 20.0312, 19.7188,\n",
+            "         24.7500, 20.0625],\n",
+            "        [17.0781, 18.5312, 18.9375, 18.5156, 19.2188, 19.3281, 17.0156, 20.7969,\n",
+            "         17.0469, 19.5000],\n",
+            "        [21.1719, 19.8594, 28.4844, 19.4219, 17.8125, 20.0938, 21.7344, 18.2969,\n",
+            "         19.7500, 17.8594],\n",
+            "        [27.6719, 18.7344, 23.0469, 18.3750, 18.5156, 18.8125, 20.0781, 19.2812,\n",
+            "         22.7656, 18.5625],\n",
+            "        [20.2812, 23.4062, 19.0312, 18.8594, 18.5156, 19.7188, 18.0938, 20.3750,\n",
+            "         20.9375, 25.9844],\n",
+            "        [21.7188, 21.4688, 29.0625, 22.7500, 20.8438, 22.6562, 24.2812, 21.2344,\n",
+            "         20.5000, 20.5938],\n",
+            "        [20.7344, 20.0156, 24.8438, 22.6406, 23.4375, 22.0781, 22.7188, 22.5000,\n",
+            "         20.6406, 19.5000],\n",
+            "        [19.0625, 19.0469, 21.6719, 20.0938, 28.0625, 21.5312, 18.0312, 23.5625,\n",
+            "         17.9844, 17.4688],\n",
+            "        [19.1094, 19.8438, 21.7344, 21.8125, 26.4219, 24.7500, 19.7344, 27.1562,\n",
+            "         18.4688, 19.5625],\n",
+            "        [18.9531, 23.0938, 19.1562, 18.2812, 18.2969, 18.3125, 17.2969, 18.5000,\n",
+            "         20.0625, 27.5000],\n",
+            "        [21.1094, 19.2656, 28.1250, 21.8594, 21.2969, 21.5781, 21.7656, 20.6875,\n",
+            "         19.4531, 19.1406],\n",
+            "        [20.8906, 20.4375, 26.9688, 23.8281, 24.4375, 23.5312, 23.8438, 22.4375,\n",
+            "         20.0156, 20.2500],\n",
+            "        [21.3594, 20.6250, 27.4375, 21.7344, 22.1562, 23.1875, 22.5625, 22.5312,\n",
+            "         21.2969, 20.6562],\n",
+            "        [18.6719, 17.6562, 20.0938, 21.6250, 18.8125, 24.9219, 19.4844, 21.3125,\n",
+            "         17.7656, 17.0938],\n",
+            "        [18.8594, 26.2812, 19.9219, 19.3906, 19.2656, 19.9688, 18.8750, 18.9062,\n",
+            "         19.3906, 22.5156],\n",
+            "        [18.7969, 20.1094, 20.5938, 25.4219, 17.7500, 22.5156, 21.7500, 19.1250,\n",
+            "         19.5000, 18.7969],\n",
+            "        [20.8438, 17.9531, 27.7031, 19.7656, 19.7188, 19.8125, 20.6562, 18.1250,\n",
+            "         18.6406, 18.1719],\n",
+            "        [20.7969, 19.3594, 22.0625, 18.3906, 19.0781, 18.5781, 19.0469, 18.6719,\n",
+            "         21.2188, 18.2656],\n",
+            "        [27.5156, 21.6406, 21.8125, 19.3906, 18.9688, 19.4219, 18.1250, 19.2344,\n",
+            "         22.4219, 20.1094],\n",
+            "        [20.0625, 19.2812, 22.2344, 20.2812, 29.0625, 22.5000, 18.9219, 23.3438,\n",
+            "         18.2188, 19.3125],\n",
+            "        [27.5312, 21.6094, 23.8906, 20.0000, 20.0312, 19.8438, 18.4062, 20.4375,\n",
+            "         22.8594, 22.7812],\n",
+            "        [20.1719, 21.4062, 24.7031, 22.6875, 22.3438, 23.5312, 28.5000, 22.5000,\n",
+            "         20.5312, 20.7812],\n",
+            "        [19.7344, 19.7812, 22.3438, 22.7500, 21.1719, 26.1094, 22.0156, 22.5469,\n",
+            "         19.4688, 19.9531],\n",
+            "        [19.6250, 19.5781, 20.2969, 18.6406, 18.6250, 18.8438, 17.0469, 19.2188,\n",
+            "         27.3438, 20.5781],\n",
+            "        [23.0156, 21.2188, 22.4062, 20.1094, 18.1875, 19.9844, 21.6719, 19.8438,\n",
+            "         23.4531, 20.0312],\n",
+            "        [19.7031, 20.7500, 23.7969, 26.2188, 20.8281, 23.7969, 22.4062, 22.0000,\n",
+            "         21.5469, 19.3906],\n",
+            "        [19.8750, 20.3125, 19.8750, 17.2344, 17.3750, 18.5312, 16.9219, 18.6250,\n",
+            "         24.1562, 19.4062],\n",
+            "        [18.5156, 19.3750, 22.7812, 21.5938, 20.1875, 22.6406, 29.1094, 19.7344,\n",
+            "         18.2188, 18.4219]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[2],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [5],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [0],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [6],\n",
+            "        [5],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [6]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[2, 5, 9, 5, 8, 7, 2, 0, 9, 2, 2, 4, 7, 9, 2, 2, 2, 5, 1, 3, 2, 2, 0, 4,\n",
+            "         0, 6, 5, 8, 8, 3, 8, 6],\n",
+            "        [0, 4, 1, 3, 0, 9, 6, 2, 1, 6, 4, 7, 4, 1, 3, 4, 5, 3, 9, 5, 0, 8, 8, 7,\n",
+            "         2, 2, 3, 9, 0, 5, 1, 2],\n",
+            "        [8, 7, 8, 2, 2, 5, 0, 8, 8, 3, 6, 2, 5, 8, 6, 6, 6, 7, 5, 6, 6, 0, 2, 5,\n",
+            "         8, 5, 7, 2, 2, 2, 2, 5],\n",
+            "        [6, 2, 2, 6, 1, 4, 5, 6, 7, 5, 3, 5, 3, 2, 5, 3, 7, 2, 2, 2, 5, 1, 1, 2,\n",
+            "         9, 3, 2, 0, 6, 6, 0, 3],\n",
+            "        [1, 3, 7, 7, 3, 2, 1, 7, 0, 0, 7, 3, 2, 0, 4, 5, 4, 6, 3, 1, 3, 4, 9, 3,\n",
+            "         1, 7, 6, 1, 1, 7, 9, 4]], device='cuda:0')\n",
+            "tensor([4, 8, 5, 2, 9, 7, 9, 7, 1, 0, 1, 9, 6, 9, 2, 7, 9, 4, 4, 0, 6, 2, 4, 1,\n",
+            "        3, 7, 2, 8, 5, 9, 0, 3], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[20.8906, 20.0156, 23.7812, 23.6094, 26.9844, 27.4531, 20.3438, 25.5312,\n",
+            "         20.1562, 19.6719],\n",
+            "        [20.5312, 19.5781, 21.2812, 18.6406, 17.5938, 19.2500, 17.7188, 19.1094,\n",
+            "         25.0000, 17.3125],\n",
+            "        [20.6875, 19.6562, 21.3438, 22.6562, 21.4688, 27.0469, 20.7500, 24.3438,\n",
+            "         19.3125, 20.0312],\n",
+            "        [21.0469, 20.7031, 27.6250, 22.0312, 24.2969, 22.8750, 21.5781, 22.5156,\n",
+            "         20.3125, 20.5938],\n",
+            "        [19.5469, 23.7656, 19.9531, 18.8594, 19.0312, 20.1406, 18.1562, 20.2188,\n",
+            "         19.9219, 26.1406],\n",
+            "        [17.8438, 19.5781, 20.4219, 19.5938, 23.9062, 20.6719, 17.3438, 27.3750,\n",
+            "         17.7031, 17.7188],\n",
+            "        [19.0625, 23.9844, 18.9375, 18.3281, 18.1562, 19.1875, 18.7969, 18.9062,\n",
+            "         20.5938, 25.1094],\n",
+            "        [19.8438, 20.2812, 21.6250, 20.9531, 20.4219, 21.8125, 16.4844, 29.0938,\n",
+            "         19.1875, 19.6719],\n",
+            "        [20.7969, 26.8906, 21.8281, 20.2969, 20.2812, 21.3906, 20.5625, 20.5312,\n",
+            "         20.2031, 24.9062],\n",
+            "        [21.1875, 20.8438, 22.0156, 20.0781, 19.2344, 20.1094, 21.0781, 20.9844,\n",
+            "         20.3125, 20.5938],\n",
+            "        [21.1250, 28.4688, 22.2031, 20.2656, 19.5156, 20.8125, 20.7656, 20.5312,\n",
+            "         22.3594, 25.2812],\n",
+            "        [18.3906, 24.4062, 20.8594, 18.3594, 18.3125, 19.5625, 19.0625, 20.2969,\n",
+            "         20.8594, 26.9062],\n",
+            "        [19.0000, 18.2812, 20.7031, 21.0938, 21.2500, 20.7812, 20.6562, 20.1094,\n",
+            "         19.7812, 18.3125],\n",
+            "        [19.3438, 23.6562, 20.1406, 19.8438, 19.1094, 20.2344, 19.1250, 20.3594,\n",
+            "         21.7344, 26.6094],\n",
+            "        [21.0938, 19.7500, 28.2031, 20.7656, 20.7656, 20.9219, 23.0469, 20.3438,\n",
+            "         19.1094, 19.1875],\n",
+            "        [19.1406, 21.5312, 20.3594, 20.7188, 19.9375, 21.6094, 16.1875, 28.5156,\n",
+            "         19.5312, 19.9219],\n",
+            "        [20.2344, 24.0625, 21.6406, 20.3906, 20.0625, 20.7656, 19.0312, 20.1562,\n",
+            "         21.3906, 27.0000],\n",
+            "        [17.9531, 19.3281, 20.1094, 20.9375, 26.2969, 22.5312, 20.2969, 23.0625,\n",
+            "         17.5000, 19.2969],\n",
+            "        [19.0312, 19.6719, 22.4375, 20.5156, 28.9062, 22.4062, 19.3438, 22.3281,\n",
+            "         18.6875, 18.6719],\n",
+            "        [27.2344, 21.1719, 24.3750, 19.5156, 19.4219, 19.7344, 19.7344, 19.7656,\n",
+            "         21.7344, 19.5312],\n",
+            "        [18.5312, 19.7969, 22.5312, 22.4375, 21.6406, 22.0156, 24.2031, 21.2500,\n",
+            "         19.0625, 18.9375],\n",
+            "        [20.2969, 20.1719, 28.6562, 22.0938, 20.2031, 22.1406, 22.5781, 19.9844,\n",
+            "         19.8750, 19.4688],\n",
+            "        [19.7188, 19.5938, 22.8906, 21.4219, 28.8906, 22.5625, 19.9062, 25.0625,\n",
+            "         18.9688, 19.8125],\n",
+            "        [19.0469, 25.3594, 18.3438, 17.9531, 18.4844, 18.3750, 17.2656, 19.2812,\n",
+            "         20.0000, 20.9531],\n",
+            "        [21.2500, 21.6875, 23.5781, 28.1094, 22.2969, 24.8906, 23.5156, 23.9219,\n",
+            "         21.7656, 20.2344],\n",
+            "        [19.9062, 20.0000, 21.0312, 19.9062, 21.3281, 21.5625, 17.4688, 28.1875,\n",
+            "         18.3438, 19.8281],\n",
+            "        [22.1094, 20.9375, 28.0000, 24.0000, 23.4688, 23.0469, 22.1094, 22.7656,\n",
+            "         21.0625, 19.7812],\n",
+            "        [24.9219, 22.9062, 23.5000, 20.7188, 21.5625, 21.1094, 20.7188, 21.7812,\n",
+            "         27.8438, 22.9062],\n",
+            "        [19.3594, 20.7031, 23.6719, 26.2500, 20.5156, 26.8281, 24.5938, 22.6562,\n",
+            "         21.2188, 18.8594],\n",
+            "        [20.2500, 23.6406, 20.7500, 19.6250, 20.3750, 20.3281, 18.3594, 20.4375,\n",
+            "         22.0625, 27.1719],\n",
+            "        [24.7188, 19.6406, 21.5156, 17.6719, 16.9844, 17.2188, 14.4219, 18.3594,\n",
+            "         20.6250, 18.4531],\n",
+            "        [18.6562, 19.3281, 21.4688, 25.9375, 21.2969, 22.3125, 20.4688, 21.8750,\n",
+            "         19.9844, 18.9375]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[5],\n",
+            "        [8],\n",
+            "        [5],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [9],\n",
+            "        [4],\n",
+            "        [9],\n",
+            "        [2],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [4],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [6],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [8],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [3]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[5, 8, 5, 2, 9, 7, 9, 7, 1, 2, 1, 9, 4, 9, 2, 7, 9, 4, 4, 0, 6, 2, 4, 1,\n",
+            "         3, 7, 2, 8, 5, 9, 0, 3],\n",
+            "        [4, 2, 7, 4, 1, 4, 1, 5, 9, 0, 9, 1, 3, 1, 6, 5, 1, 7, 2, 2, 2, 6, 7, 9,\n",
+            "         5, 5, 3, 0, 3, 1, 2, 5],\n",
+            "        [7, 0, 3, 5, 7, 5, 8, 2, 2, 6, 8, 8, 5, 8, 0, 1, 2, 5, 5, 8, 3, 5, 2, 8,\n",
+            "         7, 4, 4, 2, 6, 8, 8, 7],\n",
+            "        [2, 1, 4, 7, 5, 2, 5, 3, 5, 7, 2, 2, 2, 7, 5, 3, 8, 3, 7, 1, 5, 3, 5, 7,\n",
+            "         2, 2, 5, 9, 2, 2, 1, 2],\n",
+            "        [3, 5, 2, 3, 2, 3, 0, 4, 0, 1, 0, 7, 6, 5, 3, 2, 5, 6, 3, 7, 4, 0, 3, 0,\n",
+            "         6, 1, 7, 1, 7, 7, 9, 4]], device='cuda:0')\n",
+            "tensor([2, 3, 2, 7, 6, 3, 2, 5, 9, 0, 5, 9, 9, 8, 7, 7, 4, 8, 6, 5, 2, 3, 1, 0,\n",
+            "        4, 1, 8, 8, 4, 9, 4, 4], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[19.3438, 20.1719, 27.3438, 23.1094, 21.6875, 22.1875, 24.9219, 20.8750,\n",
+            "         20.1562, 19.2812],\n",
+            "        [20.7656, 21.5156, 23.1875, 29.4375, 24.0938, 27.3125, 23.1562, 24.3438,\n",
+            "         21.0312, 20.7344],\n",
+            "        [18.2812, 18.7188, 26.3906, 20.5625, 22.1875, 21.2500, 20.7188, 22.7188,\n",
+            "         18.7500, 17.2188],\n",
+            "        [18.7812, 19.8125, 20.3594, 19.3438, 19.9531, 21.1094, 15.0078, 28.4688,\n",
+            "         18.1875, 19.4531],\n",
+            "        [18.5000, 20.4375, 23.1875, 21.8281, 21.6562, 22.7656, 29.8438, 19.8594,\n",
+            "         19.4688, 19.1094],\n",
+            "        [19.5938, 20.1094, 21.3750, 26.3594, 20.9375, 23.7812, 20.0156, 21.7188,\n",
+            "         20.1719, 18.7969],\n",
+            "        [20.6094, 19.7656, 27.7812, 22.8125, 20.2344, 22.4062, 21.7344, 20.0625,\n",
+            "         19.5781, 19.5781],\n",
+            "        [20.6250, 21.2969, 23.2656, 24.3438, 22.4844, 26.5156, 22.3594, 22.8438,\n",
+            "         21.2812, 20.8281],\n",
+            "        [19.3438, 23.3594, 19.2969, 18.6250, 18.8438, 19.3438, 16.8750, 20.2344,\n",
+            "         20.0156, 25.6719],\n",
+            "        [24.0469, 22.9375, 21.4062, 19.1562, 19.0938, 19.3281, 20.6875, 19.6250,\n",
+            "         21.5625, 20.8594],\n",
+            "        [18.6250, 18.8906, 20.7969, 21.6719, 21.5156, 26.9062, 20.8750, 20.7812,\n",
+            "         19.0938, 19.2344],\n",
+            "        [20.5938, 24.6406, 19.5781, 18.2188, 17.9062, 19.7031, 17.8594, 19.7500,\n",
+            "         21.3906, 25.3750],\n",
+            "        [19.7031, 25.6250, 19.1719, 18.9844, 17.6094, 19.6094, 18.0312, 20.0781,\n",
+            "         20.9219, 24.7188],\n",
+            "        [19.1562, 19.8125, 19.5000, 17.7500, 17.0156, 18.0469, 16.1719, 18.4688,\n",
+            "         27.2188, 19.1406],\n",
+            "        [19.4844, 21.0156, 22.4062, 22.2500, 24.0781, 23.3438, 19.0000, 29.1562,\n",
+            "         19.3281, 20.7969],\n",
+            "        [21.0312, 21.3594, 23.3594, 28.1562, 25.6875, 27.0000, 21.7344, 26.5156,\n",
+            "         20.9531, 21.3594],\n",
+            "        [20.0938, 21.0000, 23.6250, 20.9531, 29.7969, 22.6250, 20.8750, 23.4062,\n",
+            "         19.5469, 20.1406],\n",
+            "        [25.8906, 24.7969, 22.0781, 20.6250, 20.1875, 20.4219, 21.4062, 20.4062,\n",
+            "         25.7031, 24.7812],\n",
+            "        [19.0625, 19.6875, 22.8750, 25.3750, 26.0938, 23.5938, 23.5156, 22.6562,\n",
+            "         19.1875, 18.4219],\n",
+            "        [19.8125, 19.8906, 21.7812, 22.7031, 21.2500, 25.8281, 21.8438, 24.1562,\n",
+            "         19.4531, 19.8438],\n",
+            "        [19.9062, 19.0156, 28.2188, 20.7500, 19.8594, 20.4219, 22.2656, 18.8281,\n",
+            "         17.8125, 18.0000],\n",
+            "        [19.1250, 19.9219, 22.8281, 27.2969, 22.4531, 23.7188, 21.0938, 21.3750,\n",
+            "         19.7344, 18.7344],\n",
+            "        [17.1875, 25.6406, 19.4688, 17.3125, 17.2031, 19.4531, 17.6094, 17.7656,\n",
+            "         17.5781, 24.5781],\n",
+            "        [25.5000, 20.4375, 23.1094, 19.6250, 20.5469, 19.6406, 19.7812, 20.1250,\n",
+            "         21.4844, 20.0312],\n",
+            "        [18.2188, 18.8750, 20.8438, 18.6406, 27.1562, 21.1875, 16.7812, 24.6719,\n",
+            "         17.5781, 18.4219],\n",
+            "        [19.3281, 25.7812, 20.7969, 19.6250, 20.1562, 19.9531, 19.6562, 19.2031,\n",
+            "         19.5312, 22.6094],\n",
+            "        [20.4531, 22.5469, 21.0781, 18.4531, 19.2500, 19.4219, 19.7812, 20.2500,\n",
+            "         25.3125, 24.0312],\n",
+            "        [19.7344, 18.9531, 20.0625, 18.7500, 18.0312, 18.8438, 15.9375, 19.1406,\n",
+            "         26.7031, 19.3750],\n",
+            "        [20.5938, 20.8125, 21.9844, 21.3438, 27.2969, 23.1875, 21.0781, 23.7344,\n",
+            "         19.7188, 19.5625],\n",
+            "        [20.3750, 24.8906, 20.8906, 19.5000, 19.4062, 21.2344, 18.5938, 21.1719,\n",
+            "         21.9844, 27.1562],\n",
+            "        [21.0938, 20.7031, 23.1250, 22.9062, 30.4375, 24.9219, 20.9844, 23.0312,\n",
+            "         20.1562, 20.8750],\n",
+            "        [20.6875, 20.9219, 22.7500, 21.8281, 30.5312, 23.2188, 20.1094, 24.1875,\n",
+            "         19.7656, 19.9688]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[2],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [3],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [4],\n",
+            "        [5],\n",
+            "        [2],\n",
+            "        [3],\n",
+            "        [1],\n",
+            "        [0],\n",
+            "        [4],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [4],\n",
+            "        [9],\n",
+            "        [4],\n",
+            "        [4]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[2, 3, 2, 7, 6, 3, 2, 5, 9, 0, 5, 9, 1, 8, 7, 3, 4, 0, 4, 5, 2, 3, 1, 0,\n",
+            "         4, 1, 8, 8, 4, 9, 4, 4],\n",
+            "        [6, 5, 7, 5, 2, 5, 3, 3, 1, 1, 3, 1, 9, 1, 4, 5, 2, 8, 3, 7, 6, 5, 9, 2,\n",
+            "         7, 9, 9, 2, 7, 1, 5, 7],\n",
+            "        [3, 7, 4, 2, 5, 7, 5, 2, 7, 8, 4, 8, 8, 2, 5, 7, 7, 1, 5, 3, 3, 2, 2, 8,\n",
+            "         5, 2, 1, 0, 5, 8, 2, 5],\n",
+            "        [5, 4, 5, 4, 3, 2, 6, 7, 8, 2, 6, 0, 7, 0, 2, 4, 5, 9, 6, 6, 5, 4, 5, 4,\n",
+            "         2, 4, 2, 9, 2, 5, 7, 2],\n",
+            "        [4, 2, 6, 1, 4, 4, 0, 4, 0, 9, 2, 7, 0, 9, 3, 2, 1, 2, 2, 2, 0, 7, 7, 1,\n",
+            "         1, 5, 0, 7, 3, 7, 3, 3]], device='cuda:0')\n",
+            "tensor([3, 3, 9, 2, 0, 1, 1, 8, 4, 4, 8, 3, 2, 9, 5, 7, 6, 2, 5, 4, 7, 3, 3, 9,\n",
+            "        0, 1, 5, 9, 3, 7, 6, 0], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[19.6562, 19.8594, 21.4375, 26.0469, 19.6406, 22.2344, 21.1875, 19.5156,\n",
+            "         19.8594, 19.0156],\n",
+            "        [19.1719, 20.1250, 22.2656, 27.3906, 21.1875, 24.0469, 22.0781, 20.8594,\n",
+            "         20.6875, 18.9219],\n",
+            "        [20.0156, 24.0781, 19.6094, 19.9688, 19.5469, 20.2344, 18.7656, 19.8906,\n",
+            "         20.6719, 26.0781],\n",
+            "        [20.8906, 22.2344, 26.2656, 23.7188, 22.4844, 24.6094, 23.4375, 24.0000,\n",
+            "         21.3906, 21.3125],\n",
+            "        [27.7969, 20.3750, 24.3594, 19.5938, 20.7344, 19.9375, 21.4688, 19.9062,\n",
+            "         22.3125, 20.2344],\n",
+            "        [20.4531, 27.1719, 21.7812, 19.6875, 20.6094, 20.8438, 18.5469, 19.2031,\n",
+            "         19.8281, 23.2031],\n",
+            "        [21.7344, 27.1406, 21.1406, 20.9688, 20.3438, 21.5625, 21.4062, 20.2812,\n",
+            "         21.5156, 24.3125],\n",
+            "        [21.5469, 21.3281, 19.8750, 19.4375, 18.0625, 19.1562, 18.5000, 19.0000,\n",
+            "         25.5000, 21.8125],\n",
+            "        [20.0469, 19.3906, 21.6562, 19.0312, 29.7188, 21.0625, 20.5000, 21.0625,\n",
+            "         18.0000, 19.9375],\n",
+            "        [21.7031, 21.6250, 25.0625, 23.4375, 28.0156, 25.1406, 22.8125, 26.9844,\n",
+            "         21.7812, 20.8750],\n",
+            "        [22.0312, 22.9531, 21.1719, 19.7188, 17.1094, 19.4844, 17.7344, 19.6406,\n",
+            "         27.8594, 20.3438],\n",
+            "        [18.8438, 19.5000, 20.6875, 26.9531, 19.2188, 23.2031, 20.6562, 20.5938,\n",
+            "         19.2969, 18.5469],\n",
+            "        [20.7969, 22.1875, 27.0000, 25.2812, 23.8125, 25.2188, 24.8438, 23.4688,\n",
+            "         21.7188, 21.0000],\n",
+            "        [20.8594, 23.5938, 19.8594, 18.7188, 18.7812, 19.0469, 17.9062, 19.0625,\n",
+            "         22.0938, 25.7500],\n",
+            "        [20.4375, 19.8594, 22.4219, 23.0938, 21.3125, 27.7969, 21.3594, 23.0312,\n",
+            "         19.7656, 20.1250],\n",
+            "        [18.4375, 19.9062, 20.5625, 20.6875, 22.6406, 21.2500, 17.6875, 29.1250,\n",
+            "         17.9688, 18.6406],\n",
+            "        [20.6406, 21.3438, 24.1562, 23.0469, 22.6875, 22.7812, 26.8438, 23.0000,\n",
+            "         21.2031, 19.6094],\n",
+            "        [20.3750, 18.9219, 27.9531, 20.8594, 20.4375, 20.8906, 21.7656, 20.6406,\n",
+            "         18.8906, 19.2188],\n",
+            "        [19.7031, 20.0469, 21.4062, 22.0000, 18.8594, 27.1875, 19.9844, 21.9688,\n",
+            "         19.3594, 19.3125],\n",
+            "        [19.7031, 20.2500, 22.6094, 22.0156, 29.7969, 24.2656, 21.0938, 23.0000,\n",
+            "         18.1250, 18.7656],\n",
+            "        [18.8125, 20.1250, 18.9219, 20.7969, 20.7969, 21.9375, 14.9609, 27.4844,\n",
+            "         19.0000, 19.8125],\n",
+            "        [20.1250, 21.4375, 22.8125, 27.5781, 21.3281, 23.7031, 21.5938, 21.9062,\n",
+            "         20.6250, 20.0938],\n",
+            "        [19.2969, 19.5312, 21.7812, 26.3281, 21.4688, 26.0938, 22.4062, 20.8594,\n",
+            "         20.5469, 19.1875],\n",
+            "        [20.9062, 25.3281, 20.8906, 19.6562, 20.2188, 20.6875, 20.1094, 21.3281,\n",
+            "         21.2656, 27.5781],\n",
+            "        [28.1406, 23.7344, 24.2188, 19.8750, 20.5156, 20.5000, 18.5625, 19.9219,\n",
+            "         23.6875, 23.8750],\n",
+            "        [20.5625, 26.5625, 21.2344, 19.3281, 19.1875, 20.7344, 20.9844, 20.1875,\n",
+            "         20.2344, 26.8750],\n",
+            "        [20.9062, 20.3750, 21.9375, 23.3281, 21.7812, 26.4844, 21.6719, 22.0625,\n",
+            "         20.5938, 19.9219],\n",
+            "        [21.2500, 26.1875, 21.3438, 20.7031, 20.7031, 21.8438, 19.0156, 21.4531,\n",
+            "         20.9844, 26.0625],\n",
+            "        [19.9219, 19.6094, 22.1250, 26.7656, 21.2188, 24.8906, 23.2188, 22.0156,\n",
+            "         20.1406, 19.0000],\n",
+            "        [17.9375, 20.6719, 20.4688, 20.7656, 20.7500, 22.8281, 16.2188, 28.7344,\n",
+            "         19.2344, 19.3594],\n",
+            "        [18.4375, 20.1562, 21.9844, 20.5938, 20.3594, 20.5938, 26.0938, 19.9219,\n",
+            "         19.6719, 19.0625],\n",
+            "        [28.6406, 22.2969, 25.1719, 19.8125, 21.6094, 20.5938, 20.8281, 21.3594,\n",
+            "         24.1719, 22.5312]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[3],\n",
+            "        [3],\n",
+            "        [9],\n",
+            "        [2],\n",
+            "        [0],\n",
+            "        [1],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [4],\n",
+            "        [4],\n",
+            "        [8],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [2],\n",
+            "        [5],\n",
+            "        [4],\n",
+            "        [7],\n",
+            "        [3],\n",
+            "        [3],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [0]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[3, 3, 9, 2, 0, 1, 1, 8, 4, 4, 8, 3, 2, 9, 5, 7, 6, 2, 5, 4, 7, 3, 3, 9,\n",
+            "         0, 9, 5, 1, 3, 7, 6, 0],\n",
+            "        [5, 5, 1, 5, 2, 9, 9, 9, 2, 7, 1, 5, 3, 1, 3, 4, 2, 6, 3, 5, 5, 5, 5, 1,\n",
+            "         2, 1, 3, 9, 5, 5, 2, 2],\n",
+            "        [2, 2, 8, 7, 8, 2, 0, 0, 5, 5, 0, 2, 5, 8, 7, 5, 3, 5, 7, 7, 4, 2, 6, 7,\n",
+            "         9, 2, 7, 5, 6, 3, 5, 8],\n",
+            "        [6, 6, 5, 3, 6, 5, 5, 1, 7, 2, 2, 6, 6, 0, 2, 3, 7, 3, 2, 2, 3, 7, 2, 8,\n",
+            "         1, 6, 2, 7, 2, 4, 3, 9],\n",
+            "        [1, 4, 0, 6, 4, 4, 8, 2, 6, 3, 9, 7, 4, 2, 6, 2, 5, 7, 1, 3, 1, 6, 4, 0,\n",
+            "         8, 5, 4, 2, 7, 1, 4, 1]], device='cuda:0')\n",
+            "tensor([4, 2, 2, 5, 6, 3, 8, 9, 5, 6, 1, 4, 5, 4, 6, 7, 2, 1, 0, 2, 0, 4, 9, 8,\n",
+            "        8, 9, 1, 1, 5, 0, 0, 8], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[20.5938, 20.5938, 22.4531, 21.3438, 29.8125, 22.1250, 19.6562, 23.4375,\n",
+            "         19.4375, 20.1562],\n",
+            "        [21.2812, 20.2812, 28.3125, 22.3594, 20.0000, 21.3125, 24.3594, 21.1719,\n",
+            "         19.7812, 19.6406],\n",
+            "        [23.6875, 19.3281, 29.0625, 20.8906, 20.3438, 20.8594, 21.7344, 21.1406,\n",
+            "         21.1719, 20.3594],\n",
+            "        [18.2344, 18.6094, 20.5938, 21.1875, 20.2969, 27.0469, 20.6250, 20.8438,\n",
+            "         18.0938, 18.9219],\n",
+            "        [20.4219, 21.1406, 23.5625, 22.1719, 22.2656, 22.3438, 25.5000, 21.7031,\n",
+            "         22.0312, 20.3281],\n",
+            "        [19.6875, 20.3438, 21.9375, 28.0312, 20.5156, 24.3906, 20.3281, 22.2344,\n",
+            "         20.6719, 18.8906],\n",
+            "        [20.3125, 22.0000, 19.8281, 18.1719, 18.2031, 19.1875, 18.7656, 19.1406,\n",
+            "         25.9688, 21.5312],\n",
+            "        [17.3125, 21.1875, 18.0625, 17.7812, 17.5781, 18.1562, 16.2031, 18.7344,\n",
+            "         17.8125, 23.9375],\n",
+            "        [17.1562, 17.7031, 20.8750, 24.1250, 22.6406, 23.2969, 22.5938, 19.3281,\n",
+            "         17.6875, 16.5000],\n",
+            "        [20.1250, 20.3750, 22.4219, 21.8125, 22.5625, 22.1719, 23.9062, 21.8281,\n",
+            "         20.0000, 19.5000],\n",
+            "        [19.4531, 26.8438, 20.9844, 19.3906, 20.0938, 20.4375, 20.3438, 18.8906,\n",
+            "         19.8438, 21.6094],\n",
+            "        [19.1875, 19.9531, 21.7031, 19.6562, 29.0469, 21.8906, 19.1406, 22.1250,\n",
+            "         18.5469, 19.0312],\n",
+            "        [19.5156, 19.9688, 22.6875, 23.4844, 21.6250, 27.8750, 21.8281, 22.8125,\n",
+            "         19.7969, 19.2344],\n",
+            "        [17.5312, 18.5781, 21.2812, 19.9531, 27.2969, 20.9688, 16.4375, 23.0000,\n",
+            "         18.8281, 17.5312],\n",
+            "        [16.6406, 17.6719, 19.3438, 17.7812, 16.0781, 18.0312, 23.7969, 17.5938,\n",
+            "         17.8281, 17.6250],\n",
+            "        [19.2656, 19.2188, 21.9062, 20.5625, 21.9844, 21.8438, 17.5000, 28.7656,\n",
+            "         18.9219, 19.3750],\n",
+            "        [22.5938, 19.5781, 28.9688, 19.8750, 20.8750, 19.6406, 20.3750, 20.7188,\n",
+            "         20.0312, 19.1875],\n",
+            "        [21.6406, 26.9062, 21.4844, 21.5312, 20.9375, 21.9375, 20.2812, 21.4375,\n",
+            "         22.2031, 24.7656],\n",
+            "        [28.3750, 22.9375, 23.8750, 21.1719, 20.4219, 21.0938, 19.3906, 22.9219,\n",
+            "         23.1719, 22.2500],\n",
+            "        [22.3594, 22.8125, 29.4062, 22.2188, 26.2812, 23.8125, 24.3438, 23.8750,\n",
+            "         21.5156, 22.2812],\n",
+            "        [27.7969, 21.1250, 24.1250, 18.4844, 19.6719, 19.6875, 17.3750, 21.1094,\n",
+            "         21.8438, 21.2656],\n",
+            "        [19.2812, 20.1250, 20.9688, 17.6719, 27.3281, 20.8438, 16.8594, 22.8906,\n",
+            "         18.6875, 19.1406],\n",
+            "        [21.5625, 25.2812, 21.7188, 21.1094, 22.8125, 22.4531, 20.8281, 22.8438,\n",
+            "         21.3438, 27.1562],\n",
+            "        [21.8125, 20.9375, 19.8438, 18.3281, 16.9531, 18.6094, 18.2031, 17.4375,\n",
+            "         26.3438, 17.8281],\n",
+            "        [23.6406, 21.1094, 23.6875, 21.0000, 21.2656, 21.2969, 19.8594, 20.8906,\n",
+            "         26.0312, 20.7188],\n",
+            "        [20.5156, 23.9062, 20.3438, 18.3750, 18.8906, 19.9844, 19.4375, 20.0938,\n",
+            "         20.9062, 27.7500],\n",
+            "        [16.8750, 24.3906, 18.1250, 16.7031, 16.4219, 18.4844, 17.2656, 17.7656,\n",
+            "         18.0938, 23.9531],\n",
+            "        [19.2031, 26.4688, 19.8281, 18.4531, 18.0781, 19.2500, 18.7344, 19.2031,\n",
+            "         19.2812, 21.9062],\n",
+            "        [19.7500, 19.4375, 22.7500, 23.5781, 23.0000, 26.2031, 21.8750, 22.7031,\n",
+            "         19.4844, 19.5000],\n",
+            "        [29.0000, 18.9375, 24.4531, 17.4688, 19.5000, 18.7656, 16.7031, 18.2656,\n",
+            "         21.4375, 18.5312],\n",
+            "        [28.5781, 20.2969, 23.8750, 19.0781, 20.3281, 19.2969, 19.4375, 20.3125,\n",
+            "         22.0625, 20.2812],\n",
+            "        [20.4688, 20.0625, 20.0312, 19.6250, 18.5625, 19.8750, 17.4688, 18.4844,\n",
+            "         26.0156, 19.3594]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[4],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [5],\n",
+            "        [6],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [6],\n",
+            "        [1],\n",
+            "        [4],\n",
+            "        [5],\n",
+            "        [4],\n",
+            "        [6],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [0],\n",
+            "        [2],\n",
+            "        [0],\n",
+            "        [4],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [1],\n",
+            "        [1],\n",
+            "        [5],\n",
+            "        [0],\n",
+            "        [0],\n",
+            "        [8]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[4, 2, 2, 5, 6, 3, 8, 9, 3, 6, 1, 4, 5, 4, 6, 7, 2, 1, 0, 2, 0, 4, 9, 8,\n",
+            "         8, 9, 1, 1, 5, 0, 0, 8],\n",
+            "        [7, 6, 0, 3, 2, 5, 1, 1, 5, 4, 9, 7, 3, 7, 2, 4, 0, 9, 2, 4, 2, 7, 1, 0,\n",
+            "         2, 1, 9, 9, 3, 2, 2, 0],\n",
+            "        [2, 3, 6, 7, 5, 7, 9, 7, 4, 2, 2, 5, 7, 2, 5, 2, 4, 8, 8, 6, 8, 2, 7, 1,\n",
+            "         0, 8, 5, 2, 4, 8, 8, 1],\n",
+            "        [5, 5, 8, 6, 4, 2, 0, 5, 6, 5, 5, 2, 2, 5, 8, 5, 7, 5, 1, 7, 9, 5, 4, 2,\n",
+            "         5, 0, 2, 8, 2, 4, 4, 2],\n",
+            "        [3, 0, 7, 2, 3, 8, 2, 2, 2, 7, 6, 1, 6, 3, 3, 3, 6, 0, 7, 5, 1, 1, 5, 5,\n",
+            "         4, 2, 8, 5, 7, 1, 7, 5]], device='cuda:0')\n",
+            "tensor([7, 1, 7, 4, 5, 4, 3, 3, 2, 0, 6, 6, 0, 1, 3, 9, 8, 3, 7, 8, 9, 4, 8, 9,\n",
+            "        0, 9, 7, 1, 6, 2, 9, 5], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[19.3281, 19.8125, 22.4062, 23.1094, 25.9375, 25.3750, 18.6719, 26.6250,\n",
+            "         18.3750, 19.8125],\n",
+            "        [21.0938, 26.3438, 21.1250, 20.9844, 18.7969, 21.5156, 20.6719, 21.0000,\n",
+            "         22.1094, 23.6406],\n",
+            "        [20.0000, 21.0781, 23.1250, 21.4375, 24.0781, 23.3906, 20.4062, 25.5312,\n",
+            "         19.8750, 20.2969],\n",
+            "        [20.2188, 20.1562, 22.5469, 22.4688, 31.2031, 23.7344, 21.7031, 24.5625,\n",
+            "         18.8125, 19.9062],\n",
+            "        [19.6875, 19.5781, 21.2812, 21.4062, 19.8125, 26.0625, 20.5312, 21.5156,\n",
+            "         18.7031, 19.5156],\n",
+            "        [19.7812, 19.6719, 21.4375, 20.7812, 29.1562, 23.7969, 19.6562, 22.6719,\n",
+            "         18.2188, 19.2656],\n",
+            "        [19.6719, 19.7500, 21.3281, 27.4219, 21.3281, 23.5156, 20.5469, 23.4219,\n",
+            "         18.9844, 18.5312],\n",
+            "        [20.4531, 21.4688, 23.0469, 27.3750, 22.1250, 26.8906, 23.7812, 23.1250,\n",
+            "         20.8438, 20.8906],\n",
+            "        [20.6406, 19.9844, 29.2656, 21.5938, 19.6094, 20.9531, 21.1875, 19.3281,\n",
+            "         18.8906, 19.4531],\n",
+            "        [29.4688, 22.3438, 26.5469, 21.5781, 23.7656, 23.9375, 24.0781, 21.8906,\n",
+            "         23.7656, 21.5312],\n",
+            "        [18.7656, 19.4062, 21.9062, 21.9375, 22.1250, 22.2500, 23.9844, 21.1719,\n",
+            "         20.2344, 19.2812],\n",
+            "        [19.4531, 20.3594, 22.7656, 21.1562, 20.1094, 21.2031, 25.9062, 20.2969,\n",
+            "         19.8438, 18.9375],\n",
+            "        [24.9844, 18.3438, 26.2969, 18.9219, 19.1406, 18.9844, 18.8750, 18.7188,\n",
+            "         21.2969, 18.1875],\n",
+            "        [17.0312, 24.1406, 20.1719, 18.3750, 18.1562, 18.4531, 15.4219, 18.3438,\n",
+            "         17.1875, 20.9062],\n",
+            "        [20.4375, 19.8750, 22.1094, 27.5625, 20.8750, 24.1875, 22.0000, 21.8906,\n",
+            "         20.0938, 18.9375],\n",
+            "        [18.1875, 22.5469, 18.8750, 17.8281, 17.5469, 18.3281, 17.8594, 18.4062,\n",
+            "         20.1250, 24.6875],\n",
+            "        [18.4375, 18.0469, 18.6250, 17.7656, 18.6250, 16.8750, 16.1094, 18.0469,\n",
+            "         25.3594, 16.5781],\n",
+            "        [21.5625, 20.4062, 23.5156, 27.3750, 22.5625, 23.7812, 21.5156, 22.9062,\n",
+            "         21.8125, 18.9219],\n",
+            "        [19.9062, 19.4688, 20.5625, 20.2031, 21.1875, 20.9062, 17.4531, 27.6250,\n",
+            "         18.9375, 19.8438],\n",
+            "        [18.8906, 19.8906, 19.4531, 18.8750, 18.5938, 19.0781, 17.9531, 19.2188,\n",
+            "         25.5156, 19.5625],\n",
+            "        [18.8906, 23.0156, 19.9531, 19.3594, 18.6094, 19.5469, 19.9531, 19.8750,\n",
+            "         21.5000, 26.3125],\n",
+            "        [19.1562, 19.5781, 22.7500, 20.5000, 29.3594, 21.9688, 19.5781, 22.7031,\n",
+            "         18.5156, 17.8906],\n",
+            "        [19.8438, 19.2656, 20.0312, 17.7500, 18.2812, 18.4375, 16.5469, 18.4688,\n",
+            "         26.4219, 18.2344],\n",
+            "        [18.1875, 22.4062, 17.7031, 16.6562, 16.6406, 17.4688, 16.8438, 18.0469,\n",
+            "         18.3438, 24.9375],\n",
+            "        [29.0469, 21.0156, 25.1875, 19.6406, 20.5156, 20.4219, 19.3906, 20.3906,\n",
+            "         22.7656, 20.5469],\n",
+            "        [20.2500, 26.5312, 20.5469, 20.2031, 18.9688, 20.5625, 18.8125, 19.8594,\n",
+            "         20.8906, 26.0156],\n",
+            "        [20.0469, 20.5625, 22.0000, 22.4375, 23.2812, 23.9375, 18.5938, 28.1719,\n",
+            "         20.2188, 19.7656],\n",
+            "        [18.8750, 26.2031, 20.1406, 19.2656, 19.0781, 20.1250, 18.6562, 18.3438,\n",
+            "         19.6250, 20.9531],\n",
+            "        [18.8594, 20.0625, 23.4375, 22.9844, 22.7344, 22.7031, 26.8594, 21.8281,\n",
+            "         19.7188, 19.5625],\n",
+            "        [20.5312, 20.5469, 26.3281, 22.5156, 22.0469, 22.8906, 21.6562, 23.8125,\n",
+            "         20.5469, 20.5469],\n",
+            "        [21.5625, 23.2500, 20.0469, 19.6875, 19.1250, 19.5781, 19.4844, 21.1719,\n",
+            "         23.0938, 24.5469],\n",
+            "        [20.6406, 19.9219, 20.7344, 22.3750, 22.8906, 28.0156, 21.1875, 22.8125,\n",
+            "         19.0938, 20.7031]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[7],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [5],\n",
+            "        [4],\n",
+            "        [3],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [0],\n",
+            "        [6],\n",
+            "        [6],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [4],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [1],\n",
+            "        [6],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [5]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[7, 1, 7, 4, 5, 4, 3, 3, 2, 0, 6, 6, 2, 1, 3, 9, 8, 3, 7, 8, 9, 4, 8, 9,\n",
+            "         0, 1, 7, 1, 6, 2, 9, 5],\n",
+            "        [4, 9, 4, 7, 7, 5, 5, 5, 3, 2, 5, 2, 0, 9, 5, 1, 4, 5, 4, 1, 1, 2, 2, 1,\n",
+            "         2, 9, 5, 9, 2, 7, 1, 4],\n",
+            "        [5, 8, 5, 5, 3, 7, 7, 6, 6, 6, 4, 5, 8, 2, 2, 8, 2, 2, 5, 9, 8, 7, 0, 8,\n",
+            "         8, 8, 4, 2, 3, 5, 8, 7],\n",
+            "        [3, 5, 2, 2, 2, 2, 4, 7, 5, 5, 3, 3, 4, 5, 6, 2, 0, 7, 2, 2, 6, 5, 1, 0,\n",
+            "         1, 5, 3, 5, 4, 3, 0, 3],\n",
+            "        [2, 2, 3, 3, 6, 3, 2, 2, 0, 4, 2, 1, 5, 3, 7, 7, 1, 4, 3, 7, 2, 3, 7, 7,\n",
+            "         9, 2, 2, 8, 5, 4, 7, 6]], device='cuda:0')\n",
+            "tensor([6, 9, 3, 9, 8, 7, 7, 1, 6, 5, 3, 1, 3, 1, 2, 7, 1, 8, 2, 0, 9, 7, 9, 8,\n",
+            "        8, 6, 7, 3, 7, 1, 3, 9], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[20.8125, 20.8750, 24.2969, 24.2969, 21.1250, 22.6719, 22.7656, 21.9375,\n",
+            "         20.6719, 20.1562],\n",
+            "        [17.6406, 20.8438, 17.0469, 16.4688, 15.5078, 17.7344, 16.0156, 17.1250,\n",
+            "         19.2656, 21.6094],\n",
+            "        [20.2812, 20.0625, 24.3438, 23.5000, 24.1250, 23.9531, 22.3125, 24.0938,\n",
+            "         20.2969, 20.6250],\n",
+            "        [20.2188, 23.9375, 20.1562, 19.1562, 19.4688, 19.9219, 18.8906, 19.9688,\n",
+            "         20.6406, 26.4688],\n",
+            "        [22.0938, 21.3281, 20.8906, 19.0156, 19.1250, 19.4375, 19.3281, 18.8750,\n",
+            "         23.9844, 19.8906],\n",
+            "        [17.5469, 20.2812, 20.5156, 20.0625, 20.3750, 21.2188, 16.7969, 27.2500,\n",
+            "         18.3594, 19.2500],\n",
+            "        [18.6719, 19.7031, 19.9531, 19.9531, 21.0312, 20.9219, 17.9531, 27.0938,\n",
+            "         18.4062, 19.0625],\n",
+            "        [20.2500, 26.4375, 21.5938, 20.8750, 19.5469, 21.6875, 18.2812, 20.6875,\n",
+            "         20.5469, 24.7188],\n",
+            "        [19.5156, 20.0781, 23.6250, 24.4375, 22.8750, 23.4688, 24.1094, 22.1250,\n",
+            "         20.1250, 19.6406],\n",
+            "        [20.4375, 20.7656, 21.5312, 23.6406, 22.3906, 26.3750, 21.3125, 23.8594,\n",
+            "         20.7188, 20.7031],\n",
+            "        [18.9531, 19.6562, 20.1094, 26.5312, 19.6562, 23.1250, 19.4844, 20.2656,\n",
+            "         19.5938, 17.8594],\n",
+            "        [22.2031, 25.8438, 20.5938, 19.8438, 19.8281, 20.8594, 20.1250, 20.0312,\n",
+            "         21.5781, 26.8438],\n",
+            "        [18.9844, 18.8906, 22.5000, 27.3125, 21.1562, 23.7656, 21.5469, 22.3125,\n",
+            "         20.1719, 17.8594],\n",
+            "        [18.9688, 25.3125, 19.8594, 19.1875, 19.5000, 19.7344, 18.0469, 18.3594,\n",
+            "         19.3594, 22.2031],\n",
+            "        [22.4844, 21.9531, 26.7031, 21.7656, 23.5156, 23.2188, 22.9375, 22.8906,\n",
+            "         21.5781, 21.1875],\n",
+            "        [19.7969, 21.3594, 23.0312, 20.2969, 23.1094, 23.1719, 18.8438, 29.7188,\n",
+            "         18.8125, 20.9531],\n",
+            "        [19.1875, 24.9844, 18.7031, 18.0156, 16.4531, 18.6094, 16.8125, 17.6406,\n",
+            "         18.8906, 20.0781],\n",
+            "        [21.0156, 21.9219, 18.8281, 18.5938, 17.0469, 18.6406, 16.8125, 19.2500,\n",
+            "         24.4062, 22.3750],\n",
+            "        [21.1562, 20.4375, 30.0938, 21.3281, 19.3594, 22.4844, 23.7031, 21.1875,\n",
+            "         19.5312, 19.9375],\n",
+            "        [28.6094, 23.2656, 27.9062, 22.9531, 22.8125, 23.4688, 24.1875, 23.8125,\n",
+            "         24.4062, 22.4688],\n",
+            "        [18.3906, 23.2031, 20.7188, 18.8906, 19.1250, 19.6094, 18.2031, 20.1719,\n",
+            "         20.4219, 26.7656],\n",
+            "        [20.0625, 21.0625, 23.8438, 23.0000, 24.3906, 24.7031, 19.8750, 28.3281,\n",
+            "         19.5469, 19.7500],\n",
+            "        [17.0469, 23.2344, 18.1406, 17.1562, 16.9062, 17.9531, 16.7656, 18.2031,\n",
+            "         18.1406, 25.3438],\n",
+            "        [19.7500, 18.7812, 18.8750, 18.4219, 18.7344, 18.2969, 17.2188, 18.9375,\n",
+            "         26.0781, 19.2031],\n",
+            "        [20.5625, 18.3906, 19.9531, 19.2344, 18.9531, 18.7812, 18.1875, 18.4219,\n",
+            "         25.2188, 18.3281],\n",
+            "        [19.9219, 20.1562, 24.6719, 21.7031, 19.7969, 22.3438, 28.5781, 20.2500,\n",
+            "         19.8281, 19.7188],\n",
+            "        [19.4219, 21.4375, 20.6406, 19.8750, 21.4844, 21.8906, 17.0625, 27.7031,\n",
+            "         19.6250, 20.2969],\n",
+            "        [19.1094, 20.4844, 22.0469, 26.7031, 21.0469, 23.3438, 21.9844, 22.1250,\n",
+            "         19.7969, 19.3906],\n",
+            "        [17.6875, 19.1875, 20.0781, 19.7500, 21.8125, 21.2188, 16.1562, 28.7969,\n",
+            "         17.2812, 18.6562],\n",
+            "        [19.4219, 26.4062, 21.2344, 20.2031, 19.8125, 20.6250, 20.4531, 19.7500,\n",
+            "         19.7344, 22.3281],\n",
+            "        [19.0312, 18.3438, 19.3281, 18.4531, 19.3594, 18.4062, 20.0156, 19.4062,\n",
+            "         18.5469, 18.4688],\n",
+            "        [16.9062, 21.7188, 17.7812, 16.3438, 16.0625, 16.9062, 15.1094, 17.8438,\n",
+            "         17.9219, 26.3906]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[2],\n",
+            "        [9],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [5],\n",
+            "        [3],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [7],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [2],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [6],\n",
+            "        [7],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [1],\n",
+            "        [6],\n",
+            "        [9]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[3, 9, 2, 9, 8, 7, 7, 1, 3, 5, 3, 9, 3, 1, 2, 7, 1, 8, 2, 0, 9, 7, 9, 8,\n",
+            "         8, 6, 7, 3, 7, 1, 6, 9],\n",
+            "        [2, 1, 4, 1, 0, 5, 4, 9, 6, 7, 5, 1, 5, 9, 4, 5, 9, 9, 6, 2, 1, 5, 1, 0,\n",
+            "         0, 2, 5, 5, 4, 9, 7, 1],\n",
+            "        [6, 8, 7, 8, 1, 2, 5, 5, 2, 3, 7, 0, 2, 2, 5, 4, 0, 1, 5, 8, 2, 4, 7, 9,\n",
+            "         2, 5, 4, 7, 5, 2, 4, 8],\n",
+            "        [5, 5, 5, 0, 2, 4, 3, 2, 5, 4, 2, 8, 7, 5, 6, 2, 8, 0, 3, 6, 8, 2, 8, 7,\n",
+            "         3, 3, 1, 2, 2, 5, 2, 7],\n",
+            "        [7, 0, 3, 2, 9, 1, 2, 3, 4, 2, 1, 5, 6, 4, 7, 1, 2, 7, 7, 7, 7, 3, 2, 2,\n",
+            "         4, 7, 2, 6, 3, 6, 0, 2]], device='cuda:0')\n",
+            "tensor([0, 9, 3, 6, 7, 2, 7, 3, 0, 5, 9, 7, 5, 5, 0, 6, 5, 1, 8, 2, 7, 5, 9, 0,\n",
+            "        0, 0, 8, 8, 7, 3, 7, 8], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[28.0000, 21.5312, 22.9531, 18.6250, 18.9531, 19.2188, 17.7031, 19.3750,\n",
+            "         23.0781, 20.4219],\n",
+            "        [20.5938, 23.2188, 19.4219, 18.9219, 18.3906, 19.4219, 18.6562, 20.4688,\n",
+            "         21.2969, 24.4688],\n",
+            "        [20.2031, 20.7812, 21.8125, 27.3281, 19.4688, 23.3750, 20.4844, 21.9688,\n",
+            "         19.8906, 19.5781],\n",
+            "        [18.7344, 21.1562, 23.6250, 24.1406, 21.2969, 23.5000, 29.6719, 19.9219,\n",
+            "         20.6875, 19.4375],\n",
+            "        [18.7031, 20.5781, 21.5625, 22.0781, 24.6094, 23.1094, 17.2188, 29.3125,\n",
+            "         19.3125, 19.2969],\n",
+            "        [22.6875, 22.0625, 22.7031, 20.5781, 21.7500, 21.1250, 21.3438, 21.9219,\n",
+            "         21.9531, 21.3438],\n",
+            "        [19.6250, 19.6562, 24.4844, 23.0938, 21.8750, 24.6562, 19.8125, 26.5781,\n",
+            "         19.7188, 18.6250],\n",
+            "        [22.9219, 21.7656, 22.2969, 23.9375, 21.3125, 23.1562, 21.9688, 22.5938,\n",
+            "         22.1094, 20.4062],\n",
+            "        [29.2500, 19.7656, 25.5625, 20.3750, 20.6719, 20.7812, 19.0625, 20.4531,\n",
+            "         22.1875, 20.0312],\n",
+            "        [21.2656, 20.8281, 22.6875, 25.1719, 21.7812, 28.5625, 23.5312, 23.4375,\n",
+            "         21.0625, 20.5156],\n",
+            "        [18.4375, 23.1562, 19.2969, 18.6406, 17.5156, 19.5469, 16.7500, 19.6875,\n",
+            "         19.6094, 25.2812],\n",
+            "        [19.6719, 21.0938, 22.0938, 20.6875, 23.0625, 22.1875, 17.7969, 29.2812,\n",
+            "         19.5625, 20.0781],\n",
+            "        [21.3281, 21.9375, 24.1875, 28.7969, 21.9062, 25.6562, 23.6250, 22.3750,\n",
+            "         21.4062, 20.5312],\n",
+            "        [19.6250, 20.3594, 21.2812, 21.9531, 18.0312, 27.4062, 20.5312, 21.9844,\n",
+            "         19.5469, 19.1250],\n",
+            "        [21.5469, 18.7188, 22.2969, 19.4219, 19.3438, 19.4219, 20.0781, 20.1094,\n",
+            "         20.3281, 18.5312],\n",
+            "        [18.8125, 19.7500, 20.6562, 20.0000, 19.5938, 19.7656, 20.9375, 18.7656,\n",
+            "         19.9375, 18.6406],\n",
+            "        [21.4531, 21.5469, 22.8281, 24.8906, 21.7344, 25.1250, 21.6250, 23.2500,\n",
+            "         22.0156, 20.2812],\n",
+            "        [17.8281, 25.3594, 18.9531, 17.2344, 18.0312, 18.5000, 17.8438, 16.7656,\n",
+            "         18.4844, 19.4375],\n",
+            "        [20.4531, 20.6562, 20.4375, 19.2812, 19.2031, 19.1719, 17.0625, 19.5469,\n",
+            "         25.2812, 20.4688],\n",
+            "        [19.8750, 19.6250, 26.2969, 19.3906, 18.6250, 19.7031, 21.1406, 19.3750,\n",
+            "         18.9219, 18.0312],\n",
+            "        [19.1406, 20.2188, 21.7500, 20.9219, 22.0312, 22.1250, 17.8281, 29.5156,\n",
+            "         18.5469, 19.2031],\n",
+            "        [19.9688, 19.2031, 21.5625, 23.5000, 20.5156, 26.7188, 22.3281, 21.0625,\n",
+            "         19.5625, 19.0781],\n",
+            "        [18.5781, 23.5156, 20.0000, 19.5625, 19.2500, 19.9375, 18.3594, 19.7812,\n",
+            "         20.4062, 26.5312],\n",
+            "        [29.6250, 21.5469, 24.7656, 19.4219, 20.4062, 20.8281, 20.8906, 21.0469,\n",
+            "         24.7188, 21.6562],\n",
+            "        [28.8438, 20.9375, 25.3438, 18.4375, 19.0625, 19.5469, 17.8125, 19.0781,\n",
+            "         23.1250, 19.7031],\n",
+            "        [25.3125, 20.4844, 21.8438, 18.5625, 19.4375, 19.0938, 18.7812, 19.5312,\n",
+            "         21.3906, 18.6562],\n",
+            "        [21.2656, 20.7031, 20.6406, 18.4062, 17.6875, 18.2812, 17.2969, 19.1875,\n",
+            "         27.4531, 19.9688],\n",
+            "        [21.0781, 21.5938, 20.3125, 19.0625, 19.0938, 19.8125, 18.7344, 18.7812,\n",
+            "         24.3750, 21.4688],\n",
+            "        [19.4531, 19.3750, 20.9375, 22.8438, 23.0469, 24.8906, 19.4219, 27.4375,\n",
+            "         18.5000, 19.3125],\n",
+            "        [18.8906, 19.8750, 22.3125, 25.9531, 21.8438, 24.2500, 22.0781, 21.7344,\n",
+            "         19.2031, 18.6250],\n",
+            "        [18.4688, 19.7031, 19.8438, 19.8281, 25.4219, 21.3594, 15.4922, 25.9219,\n",
+            "         17.9062, 18.4844],\n",
+            "        [18.6875, 20.2656, 19.9219, 18.4531, 17.7031, 19.5781, 18.0938, 19.2344,\n",
+            "         25.3750, 18.6094]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[0],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [6],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [7],\n",
+            "        [3],\n",
+            "        [0],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [3],\n",
+            "        [5],\n",
+            "        [2],\n",
+            "        [6],\n",
+            "        [5],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [2],\n",
+            "        [7],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [0],\n",
+            "        [0],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [8]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[0, 9, 3, 6, 7, 2, 7, 3, 0, 5, 9, 7, 3, 5, 2, 6, 5, 1, 8, 2, 7, 5, 9, 0,\n",
+            "         0, 0, 8, 8, 7, 3, 7, 8],\n",
+            "        [8, 1, 5, 3, 4, 0, 5, 5, 2, 3, 1, 4, 5, 7, 0, 2, 3, 9, 1, 6, 5, 3, 1, 2,\n",
+            "         2, 2, 0, 1, 5, 5, 4, 1],\n",
+            "        [2, 8, 7, 2, 5, 1, 2, 0, 8, 6, 7, 5, 2, 3, 8, 3, 7, 2, 9, 0, 4, 6, 8, 8,\n",
+            "         8, 8, 1, 9, 4, 2, 5, 2],\n",
+            "        [1, 0, 2, 5, 3, 8, 3, 7, 5, 7, 8, 2, 6, 2, 7, 8, 2, 5, 0, 5, 2, 2, 2, 9,\n",
+            "         1, 1, 2, 0, 3, 6, 2, 5],\n",
+            "        [9, 7, 1, 4, 2, 7, 4, 2, 4, 2, 5, 1, 7, 6, 6, 5, 8, 8, 2, 1, 3, 7, 5, 1,\n",
+            "         9, 7, 9, 2, 2, 4, 3, 7]], device='cuda:0')\n",
+            "tensor([9, 3, 7, 9, 7, 8, 7, 9, 8, 5, 4, 8, 3, 7, 6, 3, 8, 2, 1, 9, 5, 7, 3, 9,\n",
+            "        5, 5, 8, 7, 3, 5, 3, 5], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[18.9688, 22.8906, 21.1250, 18.9375, 18.2812, 19.8906, 19.9531, 19.7344,\n",
+            "         20.5625, 26.8438],\n",
+            "        [22.1094, 22.6250, 23.4375, 24.8594, 22.8438, 23.6250, 22.9375, 22.6875,\n",
+            "         23.2500, 22.6875],\n",
+            "        [18.6094, 20.2188, 21.7500, 21.3125, 21.7031, 22.2812, 17.7656, 28.9375,\n",
+            "         19.2812, 19.1094],\n",
+            "        [20.3906, 23.5000, 19.4844, 18.7969, 17.9219, 19.0781, 18.2344, 19.8438,\n",
+            "         21.0156, 23.6875],\n",
+            "        [20.2656, 21.2031, 21.2656, 20.7344, 20.3750, 22.2969, 17.1875, 28.8906,\n",
+            "         20.6406, 20.2188],\n",
+            "        [19.7500, 21.0312, 19.3438, 19.0469, 16.0938, 19.1250, 17.4062, 18.0938,\n",
+            "         26.5469, 19.4844],\n",
+            "        [21.1719, 21.0469, 21.9062, 21.4844, 22.1250, 22.5781, 19.5938, 28.2500,\n",
+            "         19.7500, 19.4062],\n",
+            "        [18.1875, 23.1875, 19.2500, 16.2812, 16.4688, 18.0938, 18.3906, 19.4062,\n",
+            "         18.3125, 27.6094],\n",
+            "        [21.3906, 23.4375, 21.5625, 20.8438, 19.8125, 21.2188, 18.0156, 21.0312,\n",
+            "         28.2188, 22.5000],\n",
+            "        [21.4688, 21.2812, 27.6094, 23.2969, 20.4375, 23.5625, 25.1406, 20.9062,\n",
+            "         20.9531, 20.6406],\n",
+            "        [19.9844, 20.6562, 22.7656, 22.9531, 29.1094, 24.0781, 20.0625, 26.5312,\n",
+            "         19.6562, 19.7812],\n",
+            "        [27.2500, 19.8750, 23.2188, 18.1562, 19.4375, 19.2656, 18.2031, 19.0781,\n",
+            "         22.0469, 19.6250],\n",
+            "        [22.4531, 20.6094, 24.2656, 26.2344, 23.1250, 23.9375, 23.9375, 22.8594,\n",
+            "         21.3750, 19.9688],\n",
+            "        [19.3125, 21.2188, 21.2656, 20.8906, 21.6719, 23.0781, 16.6406, 29.8281,\n",
+            "         19.2344, 20.5312],\n",
+            "        [20.1250, 20.0312, 23.2500, 21.4375, 21.2812, 21.8594, 27.0938, 20.6719,\n",
+            "         20.7188, 19.4062],\n",
+            "        [17.0312, 18.4062, 19.2656, 25.5000, 17.4375, 21.8438, 19.2031, 19.2344,\n",
+            "         18.2344, 16.9219],\n",
+            "        [20.2969, 21.3750, 20.6094, 18.6719, 16.9062, 18.7031, 16.6250, 19.3750,\n",
+            "         27.1719, 19.5156],\n",
+            "        [21.1562, 19.7656, 27.1406, 21.5156, 19.5781, 20.4844, 21.0781, 20.3750,\n",
+            "         20.5156, 19.2969],\n",
+            "        [19.0469, 25.3438, 20.0156, 18.5781, 19.4531, 19.5312, 18.3594, 19.4688,\n",
+            "         18.9219, 21.4531],\n",
+            "        [19.0938, 23.6875, 19.4844, 18.9531, 19.0000, 19.8125, 19.0312, 21.1875,\n",
+            "         20.5938, 25.5312],\n",
+            "        [18.8125, 18.6719, 21.3906, 26.3281, 22.4844, 25.7969, 20.9375, 21.6250,\n",
+            "         18.7969, 18.1406],\n",
+            "        [21.4844, 21.7344, 24.5469, 26.9375, 23.5469, 25.5938, 24.3281, 24.7500,\n",
+            "         22.2188, 20.9688],\n",
+            "        [21.0000, 21.5469, 21.8281, 22.4844, 20.5312, 22.4375, 20.1719, 21.1406,\n",
+            "         22.0156, 20.7031],\n",
+            "        [16.8281, 22.4375, 18.9375, 17.5781, 17.0469, 18.1250, 17.3281, 17.7188,\n",
+            "         18.7812, 25.1562],\n",
+            "        [20.0312, 20.2344, 21.9688, 22.3750, 21.8125, 27.9531, 21.5781, 22.7969,\n",
+            "         19.5469, 20.0000],\n",
+            "        [20.0625, 19.9062, 21.3906, 24.8125, 23.5938, 27.0312, 20.1562, 23.6406,\n",
+            "         19.1250, 20.1094],\n",
+            "        [21.6250, 21.6875, 21.5469, 20.2500, 20.0938, 20.5312, 18.7344, 21.0000,\n",
+            "         27.5156, 20.7500],\n",
+            "        [20.4062, 20.6250, 21.8281, 21.7656, 21.9375, 22.3281, 18.4844, 28.8281,\n",
+            "         19.3281, 19.7812],\n",
+            "        [20.9531, 22.1250, 23.5000, 28.7031, 22.2031, 25.1094, 23.0781, 23.0312,\n",
+            "         22.0625, 20.8906],\n",
+            "        [20.0000, 21.2969, 22.3906, 23.8125, 20.3125, 26.6250, 24.5625, 22.9688,\n",
+            "         20.8906, 20.3906],\n",
+            "        [19.1250, 19.8594, 21.7812, 26.7500, 19.5781, 22.5156, 20.2031, 21.4375,\n",
+            "         19.7812, 18.7344],\n",
+            "        [20.3125, 21.0000, 21.8438, 22.8125, 21.9375, 27.8125, 21.9062, 21.7344,\n",
+            "         20.0625, 19.7812]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[9],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [3],\n",
+            "        [3],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [5],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [3],\n",
+            "        [5],\n",
+            "        [3],\n",
+            "        [5]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[9, 3, 7, 9, 7, 8, 7, 9, 8, 2, 4, 0, 3, 7, 6, 3, 8, 2, 1, 9, 3, 3, 3, 9,\n",
+            "         5, 5, 8, 7, 3, 5, 3, 5],\n",
+            "        [1, 5, 5, 1, 5, 1, 5, 1, 1, 6, 7, 2, 2, 5, 2, 5, 1, 3, 9, 1, 5, 5, 5, 1,\n",
+            "         7, 3, 1, 5, 5, 6, 5, 3],\n",
+            "        [2, 2, 2, 8, 2, 0, 4, 7, 9, 5, 5, 8, 5, 4, 5, 2, 2, 0, 2, 7, 4, 7, 8, 2,\n",
+            "         3, 7, 0, 4, 2, 3, 2, 4],\n",
+            "        [8, 8, 4, 0, 1, 9, 2, 2, 2, 3, 3, 1, 6, 2, 3, 7, 0, 6, 5, 8, 7, 2, 2, 8,\n",
+            "         2, 4, 2, 2, 6, 7, 7, 6],\n",
+            "        [6, 6, 3, 7, 3, 2, 3, 6, 0, 0, 2, 9, 4, 1, 4, 6, 9, 8, 7, 5, 2, 6, 1, 5,\n",
+            "         4, 2, 7, 3, 7, 2, 6, 2]], device='cuda:0')\n",
+            "tensor([9, 7, 6, 7, 3, 6, 4, 3, 9, 4, 2, 1, 9, 6, 0, 2, 6, 7, 4, 7, 9, 0, 7, 4,\n",
+            "        3, 5, 3, 1, 1, 2, 6, 8], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[18.5625, 23.8750, 19.9219, 18.4062, 18.6562, 18.8906, 17.9531, 19.4688,\n",
+            "         20.1719, 28.4531],\n",
+            "        [18.5938, 19.6406, 19.9062, 19.3906, 20.8750, 20.5312, 17.0156, 25.6094,\n",
+            "         18.9844, 19.8281],\n",
+            "        [18.3750, 20.2188, 23.0938, 21.7031, 20.9531, 21.6875, 24.9531, 20.2500,\n",
+            "         19.2656, 19.2344],\n",
+            "        [19.4375, 19.5156, 20.8750, 20.4375, 22.8125, 21.9531, 18.3594, 27.1562,\n",
+            "         18.4375, 19.5156],\n",
+            "        [17.3594, 18.5938, 22.3750, 24.3906, 26.1875, 23.1875, 21.2500, 22.7500,\n",
+            "         18.4531, 18.9844],\n",
+            "        [20.0625, 19.8281, 21.5469, 21.4844, 19.7344, 22.2344, 24.3906, 21.0469,\n",
+            "         20.2188, 18.9844],\n",
+            "        [21.5781, 21.1875, 23.7969, 22.3281, 28.1562, 23.2969, 23.4062, 23.2812,\n",
+            "         20.3750, 20.6094],\n",
+            "        [17.8906, 18.0156, 18.7500, 22.4062, 17.9688, 21.0625, 18.8281, 19.5312,\n",
+            "         18.2500, 17.4688],\n",
+            "        [19.1719, 23.0312, 18.4844, 17.7031, 17.7031, 18.2188, 16.5469, 18.2812,\n",
+            "         20.2500, 26.5000],\n",
+            "        [20.6406, 19.5781, 22.2969, 20.7031, 27.6875, 22.4062, 20.7031, 23.3594,\n",
+            "         19.1719, 18.7969],\n",
+            "        [22.1250, 19.7969, 28.6406, 20.7031, 20.3906, 20.5625, 21.7500, 19.7188,\n",
+            "         19.3750, 18.8125],\n",
+            "        [21.4531, 27.9062, 21.8906, 21.2656, 20.6094, 21.6719, 21.8281, 20.8125,\n",
+            "         22.0938, 24.0781],\n",
+            "        [19.1406, 22.5625, 19.1094, 18.2812, 17.9844, 18.1406, 18.6406, 19.5156,\n",
+            "         20.1406, 23.3906],\n",
+            "        [20.9375, 21.7969, 24.7500, 23.6875, 22.5625, 24.0312, 26.3438, 24.0781,\n",
+            "         21.7812, 20.6250],\n",
+            "        [29.2812, 23.0781, 24.9531, 20.8906, 21.8125, 21.6562, 19.9375, 21.7812,\n",
+            "         23.6719, 22.7344],\n",
+            "        [20.3438, 19.7500, 29.1250, 21.4844, 19.9375, 21.5625, 22.3750, 19.5625,\n",
+            "         19.6250, 19.1250],\n",
+            "        [17.0938, 19.7500, 23.8438, 22.9062, 21.1875, 22.4688, 24.3438, 20.9531,\n",
+            "         20.4062, 19.0000],\n",
+            "        [17.2812, 20.5625, 20.6875, 21.2188, 23.8125, 23.1719, 17.0312, 30.2969,\n",
+            "         18.1406, 19.9688],\n",
+            "        [19.6406, 20.1406, 22.0000, 21.5625, 24.4375, 23.0625, 19.1094, 24.8281,\n",
+            "         19.5312, 20.1094],\n",
+            "        [19.6094, 19.9375, 20.7656, 21.2812, 22.9688, 22.5000, 17.9688, 28.5312,\n",
+            "         18.2188, 19.0781],\n",
+            "        [17.9688, 21.8281, 19.4375, 18.5938, 18.5625, 18.5781, 16.5156, 18.8750,\n",
+            "         20.2344, 26.6250],\n",
+            "        [28.0781, 21.7656, 23.3125, 19.4688, 20.5781, 19.9844, 18.2656, 20.0469,\n",
+            "         21.8750, 20.4531],\n",
+            "        [18.9219, 20.3281, 21.4844, 21.0781, 20.6250, 23.3281, 16.2969, 29.4844,\n",
+            "         18.3438, 21.1250],\n",
+            "        [21.1094, 19.6875, 21.7969, 19.2188, 27.6094, 20.5156, 18.3594, 24.0469,\n",
+            "         19.8125, 19.6875],\n",
+            "        [19.5312, 20.0938, 21.6250, 27.0781, 21.4688, 25.0156, 21.2969, 21.8750,\n",
+            "         19.9844, 18.8125],\n",
+            "        [21.1562, 21.1562, 22.4219, 23.8594, 21.8594, 28.6562, 22.6250, 23.5938,\n",
+            "         19.9844, 20.9219],\n",
+            "        [20.9062, 21.2500, 21.6250, 26.7812, 20.4688, 23.0625, 21.3594, 21.3906,\n",
+            "         21.9688, 20.3594],\n",
+            "        [19.1094, 26.6719, 20.0781, 19.2500, 18.7969, 19.7500, 20.1250, 18.0625,\n",
+            "         20.0312, 22.3125],\n",
+            "        [17.4688, 24.3125, 19.0156, 17.5781, 17.1094, 18.3438, 16.6719, 16.4844,\n",
+            "         18.4062, 21.1406],\n",
+            "        [22.2188, 22.1719, 28.5156, 23.0312, 23.5000, 23.9688, 22.7656, 24.2031,\n",
+            "         22.3125, 21.8594],\n",
+            "        [18.0781, 20.1250, 22.4531, 19.8594, 18.8438, 21.5625, 30.0156, 17.8125,\n",
+            "         18.7969, 18.0625],\n",
+            "        [19.5156, 21.4219, 19.4844, 17.1250, 18.6719, 18.7656, 17.2500, 18.6250,\n",
+            "         23.0938, 21.7031]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[9],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [6],\n",
+            "        [4],\n",
+            "        [3],\n",
+            "        [9],\n",
+            "        [4],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [9],\n",
+            "        [6],\n",
+            "        [0],\n",
+            "        [2],\n",
+            "        [6],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [3],\n",
+            "        [5],\n",
+            "        [3],\n",
+            "        [1],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [6],\n",
+            "        [8]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[9, 7, 6, 7, 4, 6, 4, 3, 9, 4, 2, 1, 9, 6, 0, 2, 6, 7, 7, 7, 9, 0, 7, 4,\n",
+            "         3, 5, 3, 1, 1, 2, 6, 8],\n",
+            "        [1, 4, 2, 4, 3, 5, 2, 5, 1, 7, 0, 9, 1, 2, 2, 6, 2, 4, 4, 4, 1, 2, 5, 7,\n",
+            "         5, 3, 5, 9, 9, 7, 2, 9],\n",
+            "        [8, 5, 3, 5, 5, 2, 6, 7, 8, 5, 6, 8, 8, 7, 8, 5, 3, 5, 5, 5, 8, 8, 2, 2,\n",
+            "         7, 7, 8, 6, 2, 5, 5, 1],\n",
+            "        [2, 2, 5, 2, 7, 3, 5, 6, 0, 2, 3, 2, 7, 5, 1, 3, 5, 3, 2, 3, 2, 1, 9, 0,\n",
+            "         2, 6, 2, 2, 8, 4, 1, 0],\n",
+            "        [7, 9, 4, 3, 2, 7, 7, 2, 2, 3, 5, 6, 0, 3, 9, 0, 4, 2, 3, 2, 7, 4, 3, 5,\n",
+            "         4, 2, 7, 8, 5, 3, 3, 2]], device='cuda:0')\n",
+            "tensor([2, 1, 7, 8, 5, 9, 6, 1, 1, 5, 0, 6, 0, 9, 2, 6, 5, 8, 9, 5, 5, 6, 2, 9,\n",
+            "        1, 5, 8, 8, 7, 1, 7, 3], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[20.9688, 20.3594, 27.9531, 21.8906, 24.7812, 22.7188, 23.7812, 23.6094,\n",
+            "         20.4062, 20.3750],\n",
+            "        [20.7969, 24.6094, 20.2344, 18.5625, 18.6094, 19.5156, 18.6406, 19.4375,\n",
+            "         20.7188, 24.9375],\n",
+            "        [18.5781, 20.1406, 21.3750, 21.1562, 22.0625, 22.7969, 18.0312, 27.6562,\n",
+            "         18.8281, 19.3125],\n",
+            "        [21.4062, 20.3281, 21.5469, 19.6406, 19.9844, 20.2344, 18.1406, 20.1250,\n",
+            "         27.2344, 21.1562],\n",
+            "        [20.2188, 20.9531, 22.8438, 22.6094, 20.2031, 27.7031, 21.0469, 23.4531,\n",
+            "         19.8281, 20.1719],\n",
+            "        [20.7500, 23.7344, 20.4219, 18.7969, 18.9844, 19.0938, 19.7188, 19.8281,\n",
+            "         21.0781, 27.2969],\n",
+            "        [18.1406, 19.5000, 21.7031, 23.8750, 21.9062, 23.1094, 24.1094, 20.7188,\n",
+            "         18.9375, 18.3750],\n",
+            "        [18.6094, 24.9062, 19.0469, 17.3906, 17.4844, 19.2031, 18.2188, 17.7344,\n",
+            "         18.9531, 25.5469],\n",
+            "        [20.3594, 24.9688, 20.8125, 20.1562, 19.8594, 20.8438, 19.6406, 20.0625,\n",
+            "         21.6719, 24.8750],\n",
+            "        [21.2344, 21.5781, 24.5156, 24.1094, 23.7500, 25.8438, 23.4062, 25.0781,\n",
+            "         21.7031, 20.6250],\n",
+            "        [23.7188, 21.2031, 24.5938, 19.4062, 20.5625, 19.2969, 21.2500, 20.9219,\n",
+            "         21.8594, 22.0312],\n",
+            "        [17.9062, 18.8438, 20.6406, 21.3281, 19.7188, 21.7656, 26.7344, 19.2031,\n",
+            "         20.0625, 18.2969],\n",
+            "        [27.3438, 20.1562, 25.1250, 19.5000, 18.4688, 18.7188, 19.1562, 19.7500,\n",
+            "         23.5781, 19.2031],\n",
+            "        [17.0625, 22.8125, 19.0938, 17.7344, 16.8750, 17.9219, 18.0781, 18.3438,\n",
+            "         19.0625, 26.8281],\n",
+            "        [22.7500, 20.7344, 27.5156, 22.0469, 19.8750, 21.4531, 22.4219, 20.8125,\n",
+            "         21.3125, 20.3438],\n",
+            "        [19.0469, 19.0156, 23.5625, 20.9219, 18.1875, 22.8594, 30.5312, 19.1250,\n",
+            "         20.2500, 18.5781],\n",
+            "        [19.2656, 19.2188, 20.8750, 22.8125, 22.5938, 26.7031, 21.1250, 20.8906,\n",
+            "         18.9219, 18.5156],\n",
+            "        [20.4844, 22.5000, 19.7500, 18.8438, 18.2812, 18.9062, 18.3125, 18.7656,\n",
+            "         23.9531, 19.7656],\n",
+            "        [21.2500, 23.5625, 20.5000, 19.4531, 17.9844, 19.6562, 18.2500, 19.4062,\n",
+            "         23.0469, 27.0312],\n",
+            "        [19.5938, 19.0469, 21.5625, 23.2969, 21.7188, 26.5938, 21.7500, 22.3125,\n",
+            "         19.1406, 18.1719],\n",
+            "        [18.1094, 19.0781, 21.0469, 22.4844, 20.8281, 26.0625, 20.0938, 22.9688,\n",
+            "         18.6406, 17.2969],\n",
+            "        [20.0156, 18.2344, 21.6094, 19.9375, 18.8750, 19.3281, 24.7031, 18.9062,\n",
+            "         21.3125, 17.5000],\n",
+            "        [21.6719, 20.2031, 26.8438, 21.4531, 21.2188, 23.8438, 22.0312, 22.2031,\n",
+            "         22.0781, 20.4062],\n",
+            "        [18.0312, 22.4062, 18.1875, 17.1875, 16.8594, 17.6406, 17.4375, 19.1250,\n",
+            "         19.3906, 24.7188],\n",
+            "        [16.5469, 24.2500, 17.7031, 16.3125, 17.2500, 17.4219, 15.8203, 15.5469,\n",
+            "         17.4688, 20.4531],\n",
+            "        [19.5625, 20.5781, 22.6406, 23.6250, 23.8750, 23.9062, 21.5938, 23.9062,\n",
+            "         20.4531, 19.8281],\n",
+            "        [22.7500, 24.4531, 21.6875, 21.6719, 20.0312, 21.7812, 20.3594, 20.6406,\n",
+            "         26.3906, 22.2500],\n",
+            "        [20.2656, 19.9062, 20.9531, 18.9375, 18.3438, 18.8125, 16.6875, 19.3750,\n",
+            "         27.4844, 19.2500],\n",
+            "        [19.9844, 21.6406, 22.7500, 22.1719, 23.5469, 22.3281, 18.2656, 29.7969,\n",
+            "         19.9688, 19.7500],\n",
+            "        [20.2344, 26.1562, 21.6719, 20.1094, 20.4219, 20.4531, 19.4688, 20.7656,\n",
+            "         20.4219, 23.0625],\n",
+            "        [17.5938, 19.5625, 19.8281, 19.1250, 20.1875, 19.3750, 17.3125, 26.0625,\n",
+            "         18.3281, 18.1875],\n",
+            "        [19.5625, 19.5938, 23.5781, 27.7500, 21.9219, 23.7656, 21.8125, 22.3438,\n",
+            "         19.8594, 18.7656]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[2],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [6],\n",
+            "        [9],\n",
+            "        [1],\n",
+            "        [5],\n",
+            "        [2],\n",
+            "        [6],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [2],\n",
+            "        [6],\n",
+            "        [5],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [5],\n",
+            "        [6],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [1],\n",
+            "        [5],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [3]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[2, 9, 7, 8, 5, 9, 6, 9, 1, 5, 2, 6, 0, 9, 2, 6, 5, 8, 9, 5, 5, 6, 2, 9,\n",
+            "         1, 5, 8, 8, 7, 1, 7, 3],\n",
+            "        [4, 1, 5, 2, 7, 1, 3, 1, 9, 7, 0, 5, 2, 1, 0, 2, 3, 1, 1, 3, 7, 2, 5, 1,\n",
+            "         9, 7, 1, 2, 4, 9, 4, 5],\n",
+            "        [6, 0, 4, 0, 2, 8, 5, 5, 8, 2, 9, 3, 8, 2, 6, 5, 4, 0, 8, 7, 3, 8, 7, 8,\n",
+            "         2, 4, 0, 0, 2, 2, 2, 2],\n",
+            "        [7, 8, 2, 9, 3, 0, 4, 2, 5, 3, 8, 2, 1, 8, 3, 3, 6, 9, 0, 6, 2, 0, 8, 7,\n",
+            "         8, 3, 9, 1, 5, 7, 1, 7],\n",
+            "        [5, 2, 3, 1, 6, 2, 2, 8, 2, 4, 6, 8, 7, 7, 5, 8, 7, 2, 2, 4, 4, 3, 6, 2,\n",
+            "         5, 2, 5, 7, 3, 5, 5, 4]], device='cuda:0')\n",
+            "tensor([5, 4, 9, 7, 5, 2, 9, 9, 4, 7, 4, 1, 3, 8, 7, 9, 0, 4, 5, 7, 5, 2, 8, 7,\n",
+            "        6, 9, 6, 9, 3, 8, 5, 6], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[19.3281, 19.2188, 21.5312, 23.3750, 19.5938, 26.4531, 21.5156, 22.3125,\n",
+            "         18.9531, 19.0312],\n",
+            "        [17.6875, 18.3125, 21.2031, 20.6562, 26.1406, 22.4062, 16.8281, 25.9219,\n",
+            "         17.8125, 18.1875],\n",
+            "        [18.1094, 22.5625, 18.0781, 18.1875, 16.9062, 18.7031, 15.7812, 18.9375,\n",
+            "         19.9062, 24.5625],\n",
+            "        [20.5312, 21.4688, 22.7500, 21.7656, 23.0000, 23.1094, 18.1719, 29.1406,\n",
+            "         19.6250, 19.8281],\n",
+            "        [19.4375, 21.4844, 22.6250, 28.3125, 20.2031, 26.1094, 21.9688, 22.0312,\n",
+            "         20.9375, 19.0938],\n",
+            "        [21.3906, 22.3438, 24.5156, 21.7969, 24.4062, 22.5625, 22.8594, 24.5312,\n",
+            "         20.6094, 22.4375],\n",
+            "        [19.6406, 23.0625, 19.1406, 18.3906, 16.7656, 18.6562, 18.2344, 19.6875,\n",
+            "         19.6875, 23.8281],\n",
+            "        [18.1875, 22.3125, 20.2500, 18.5312, 18.4844, 18.6719, 18.6562, 19.4688,\n",
+            "         20.0625, 25.8125],\n",
+            "        [19.7656, 19.0469, 20.5469, 19.4688, 23.5000, 20.7812, 17.1250, 23.7188,\n",
+            "         18.6719, 19.3438],\n",
+            "        [22.7344, 22.7031, 23.1562, 23.6094, 22.4062, 24.1406, 22.1719, 26.3438,\n",
+            "         23.1406, 21.1719],\n",
+            "        [23.8750, 21.8906, 27.4688, 24.0781, 25.0156, 24.3281, 25.4219, 23.8750,\n",
+            "         21.7188, 20.9531],\n",
+            "        [19.1875, 26.4062, 20.3594, 21.3750, 18.8594, 20.8281, 20.1719, 20.5469,\n",
+            "         19.6406, 21.1562],\n",
+            "        [20.3438, 21.0000, 22.4219, 28.4688, 20.4688, 24.3594, 22.5469, 22.2500,\n",
+            "         20.5625, 19.6562],\n",
+            "        [25.7031, 20.2344, 21.5469, 19.5312, 18.7500, 19.7344, 18.9688, 19.2969,\n",
+            "         25.6562, 18.0000],\n",
+            "        [19.3594, 20.5938, 21.6719, 20.1406, 22.3125, 21.3281, 17.4688, 28.6094,\n",
+            "         18.1562, 19.2812],\n",
+            "        [17.6875, 22.4375, 19.1250, 18.8594, 17.3906, 18.3281, 17.7188, 18.0781,\n",
+            "         18.3750, 24.6875],\n",
+            "        [28.8438, 22.9531, 26.5625, 22.5781, 22.8906, 22.8750, 23.8281, 22.9688,\n",
+            "         25.3438, 22.1406],\n",
+            "        [17.4688, 17.1875, 18.7344, 19.9688, 24.8594, 20.6406, 18.2500, 21.2812,\n",
+            "         17.1875, 16.5781],\n",
+            "        [18.7500, 19.0000, 20.1719, 20.9219, 18.6875, 25.9844, 17.9375, 21.3906,\n",
+            "         18.8594, 18.6406],\n",
+            "        [19.9375, 21.2812, 21.2812, 21.0938, 21.3438, 22.0781, 17.2812, 28.8281,\n",
+            "         19.5156, 20.0000],\n",
+            "        [18.3750, 17.9844, 20.6719, 21.5781, 22.0938, 25.8438, 20.7344, 21.5625,\n",
+            "         19.1719, 19.6875],\n",
+            "        [20.7188, 19.9688, 28.1406, 20.3594, 20.2500, 21.0000, 21.8594, 19.6406,\n",
+            "         19.4375, 19.2812],\n",
+            "        [21.9219, 22.6562, 21.3281, 20.7812, 19.1250, 21.1094, 20.3125, 21.3438,\n",
+            "         25.0312, 22.0312],\n",
+            "        [22.0156, 21.1875, 21.3281, 20.0312, 22.4375, 22.1250, 19.4844, 27.0625,\n",
+            "         19.8750, 20.3281],\n",
+            "        [22.3281, 20.9844, 25.0000, 21.8281, 22.8750, 22.4375, 25.3125, 21.7500,\n",
+            "         21.2344, 20.2188],\n",
+            "        [22.0000, 25.4688, 21.3750, 20.3594, 20.5938, 21.0156, 20.9688, 21.7031,\n",
+            "         22.8438, 28.7344],\n",
+            "        [21.0938, 20.5000, 27.9062, 23.6406, 22.7500, 22.9219, 23.9375, 21.6562,\n",
+            "         19.9375, 19.7969],\n",
+            "        [21.8438, 23.0312, 21.0312, 19.5625, 19.1250, 19.5625, 18.5938, 20.6875,\n",
+            "         21.9375, 26.4375],\n",
+            "        [19.8438, 20.4375, 23.1562, 27.4062, 22.3906, 23.5938, 21.8281, 21.8438,\n",
+            "         21.3906, 19.2812],\n",
+            "        [18.6875, 18.0625, 18.0781, 16.9688, 16.8594, 17.0938, 15.3984, 17.0312,\n",
+            "         23.1094, 17.4688],\n",
+            "        [20.6094, 20.3906, 22.4688, 22.8906, 20.8594, 28.2188, 21.5312, 22.1094,\n",
+            "         20.5625, 21.0000],\n",
+            "        [19.0156, 18.0938, 20.9688, 19.3906, 20.1875, 19.3438, 23.2656, 19.8906,\n",
+            "         19.2969, 18.0938]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[5],\n",
+            "        [4],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [0],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [4],\n",
+            "        [5],\n",
+            "        [7],\n",
+            "        [5],\n",
+            "        [2],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [9],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [5],\n",
+            "        [6]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[5, 4, 9, 7, 3, 7, 9, 9, 7, 7, 2, 1, 3, 0, 7, 9, 0, 4, 5, 7, 5, 2, 8, 7,\n",
+            "         6, 9, 2, 9, 3, 8, 5, 6],\n",
+            "        [3, 7, 1, 5, 5, 2, 1, 1, 4, 5, 6, 3, 5, 8, 4, 1, 2, 7, 7, 5, 4, 6, 1, 4,\n",
+            "         2, 1, 6, 1, 5, 0, 3, 2],\n",
+            "        [7, 5, 8, 4, 2, 4, 8, 2, 5, 3, 4, 9, 6, 2, 2, 2, 8, 5, 3, 4, 3, 5, 9, 5,\n",
+            "         4, 8, 3, 8, 2, 2, 2, 4],\n",
+            "        [2, 2, 7, 2, 7, 6, 7, 8, 2, 2, 5, 5, 2, 1, 5, 3, 6, 3, 2, 2, 7, 0, 0, 0,\n",
+            "         5, 0, 5, 0, 4, 1, 7, 7],\n",
+            "        [6, 3, 5, 3, 6, 5, 0, 7, 0, 8, 3, 7, 7, 5, 1, 8, 7, 2, 1, 1, 6, 3, 7, 2,\n",
+            "         0, 7, 4, 2, 7, 9, 6, 3]], device='cuda:0')\n",
+            "tensor([6, 9, 5, 7, 8, 0, 5, 0, 7, 4, 8, 2, 5, 1, 3, 2, 2, 6, 2, 1, 7, 4, 6, 3,\n",
+            "        1, 3, 7, 2, 1, 3, 7, 0], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[19.7031, 19.8125, 21.2188, 20.7188, 20.0156, 19.8750, 21.3750, 19.9531,\n",
+            "         22.6562, 18.7344],\n",
+            "        [18.8750, 22.7969, 20.3281, 17.9688, 17.1719, 18.1562, 18.2500, 18.7500,\n",
+            "         20.2656, 25.8750],\n",
+            "        [20.1094, 20.5469, 22.5938, 23.3438, 20.3594, 26.9688, 22.8438, 21.1250,\n",
+            "         20.4844, 19.2344],\n",
+            "        [18.6562, 18.7969, 19.7500, 19.2812, 21.1719, 21.7812, 15.4219, 26.3438,\n",
+            "         18.8594, 18.9375],\n",
+            "        [19.8438, 19.4688, 20.6250, 19.5156, 18.5781, 19.5469, 18.0312, 19.3906,\n",
+            "         26.4219, 18.6875],\n",
+            "        [29.6562, 21.7969, 24.5625, 18.6719, 20.2031, 19.9688, 18.2812, 19.3125,\n",
+            "         23.4062, 20.4531],\n",
+            "        [20.6094, 20.7344, 23.7188, 25.9219, 22.3125, 27.2344, 22.7344, 24.3750,\n",
+            "         20.7031, 19.7656],\n",
+            "        [27.9375, 24.1562, 25.4375, 22.7031, 23.2500, 23.4219, 24.1562, 22.4688,\n",
+            "         25.0781, 22.6406],\n",
+            "        [17.5312, 18.8438, 18.7656, 19.3125, 19.9062, 20.5000, 16.5781, 26.0625,\n",
+            "         17.1719, 17.7188],\n",
+            "        [21.5625, 22.2188, 23.0625, 22.9062, 29.1250, 24.5625, 21.4375, 24.3438,\n",
+            "         21.6094, 21.7969],\n",
+            "        [20.2500, 20.0000, 18.9219, 19.1406, 17.0469, 19.1094, 16.9219, 18.3125,\n",
+            "         25.4688, 20.4062],\n",
+            "        [20.7656, 21.3750, 25.1719, 24.3594, 23.3281, 26.6094, 24.0938, 23.2812,\n",
+            "         20.6875, 20.6250],\n",
+            "        [19.1562, 19.5000, 20.4062, 21.1250, 20.0938, 26.1250, 20.7344, 21.4062,\n",
+            "         18.2188, 18.6250],\n",
+            "        [18.7031, 20.9688, 18.1875, 18.5781, 17.9062, 18.7188, 17.4844, 18.0469,\n",
+            "         19.0625, 21.3906],\n",
+            "        [20.1875, 21.2969, 22.1719, 22.5625, 21.2969, 23.2812, 22.1562, 22.0938,\n",
+            "         21.2969, 20.2812],\n",
+            "        [23.8906, 22.4062, 28.9844, 22.8594, 26.5781, 24.0469, 24.1719, 25.2656,\n",
+            "         22.5781, 21.4688],\n",
+            "        [19.8906, 18.1719, 25.6562, 19.3594, 17.2031, 18.9531, 21.9219, 18.0625,\n",
+            "         18.2188, 18.4375],\n",
+            "        [19.0000, 19.7031, 23.2969, 24.2500, 26.2812, 22.6094, 25.3281, 20.9062,\n",
+            "         19.5156, 17.9688],\n",
+            "        [19.9219, 20.5469, 25.2812, 23.0781, 27.1250, 23.2031, 20.5625, 24.3594,\n",
+            "         19.8125, 18.9219],\n",
+            "        [20.5625, 27.7188, 22.4688, 21.0000, 22.5000, 22.6094, 20.8750, 21.0312,\n",
+            "         20.9375, 24.4219],\n",
+            "        [21.1562, 20.6406, 21.4688, 25.7031, 23.5469, 25.1719, 19.9062, 28.3438,\n",
+            "         20.9219, 19.5469],\n",
+            "        [19.8438, 20.9219, 23.3125, 22.9531, 27.5312, 24.7031, 19.7344, 28.3438,\n",
+            "         19.6094, 20.5312],\n",
+            "        [19.8281, 19.8281, 22.2812, 22.9844, 24.2500, 22.2812, 26.0781, 22.8438,\n",
+            "         19.2812, 18.6719],\n",
+            "        [20.7500, 20.8281, 22.8438, 27.2969, 22.3438, 26.5781, 22.6719, 23.5938,\n",
+            "         21.1094, 20.2656],\n",
+            "        [21.1406, 26.3750, 20.1094, 19.2031, 17.7188, 19.7656, 18.6406, 18.9531,\n",
+            "         20.5156, 23.7344],\n",
+            "        [19.6719, 19.9375, 22.3906, 26.6406, 22.2188, 24.4688, 22.1562, 22.7812,\n",
+            "         20.0625, 19.2188],\n",
+            "        [19.4375, 19.0156, 22.3125, 23.7031, 21.0469, 21.5625, 19.6406, 21.5312,\n",
+            "         19.6094, 17.9219],\n",
+            "        [19.2344, 19.4688, 25.9375, 20.2188, 23.3438, 21.7344, 21.0000, 23.5938,\n",
+            "         18.9375, 18.9531],\n",
+            "        [19.2656, 28.2812, 21.4219, 20.2344, 18.8438, 21.5156, 20.5000, 19.8594,\n",
+            "         19.9375, 24.8594],\n",
+            "        [21.7031, 21.3125, 23.6094, 28.6094, 23.4219, 26.8750, 21.9844, 24.5000,\n",
+            "         21.5312, 20.5000],\n",
+            "        [18.1094, 20.4375, 20.7656, 21.0000, 23.5156, 21.6094, 15.5312, 28.9688,\n",
+            "         18.5312, 19.1406],\n",
+            "        [24.4844, 21.7031, 21.7031, 19.7969, 20.6406, 20.0781, 19.5625, 20.1875,\n",
+            "         23.4531, 21.5781]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[8],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [0],\n",
+            "        [5],\n",
+            "        [0],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [8],\n",
+            "        [5],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [4],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [3],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [0]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[8, 9, 5, 7, 8, 0, 5, 0, 7, 4, 8, 5, 5, 9, 5, 2, 2, 4, 4, 1, 7, 7, 6, 3,\n",
+            "         1, 3, 3, 2, 1, 3, 7, 0],\n",
+            "        [6, 1, 3, 5, 2, 2, 3, 2, 5, 5, 9, 2, 7, 1, 3, 4, 6, 6, 2, 9, 3, 4, 4, 5,\n",
+            "         9, 5, 2, 7, 9, 5, 4, 8],\n",
+            "        [2, 2, 6, 4, 0, 8, 7, 8, 4, 7, 0, 3, 3, 8, 2, 7, 0, 3, 7, 5, 5, 5, 3, 7,\n",
+            "         0, 7, 5, 4, 5, 7, 5, 2],\n",
+            "        [3, 8, 2, 2, 5, 1, 2, 6, 3, 2, 1, 6, 6, 5, 6, 6, 3, 2, 5, 4, 4, 2, 7, 2,\n",
+            "         8, 2, 7, 5, 2, 2, 3, 1],\n",
+            "        [4, 0, 7, 3, 3, 9, 6, 1, 1, 3, 3, 4, 2, 0, 7, 5, 5, 5, 3, 2, 2, 3, 2, 6,\n",
+            "         2, 4, 4, 6, 6, 4, 2, 9]], device='cuda:0')\n",
+            "tensor([8, 4, 4, 5, 7, 9, 5, 4, 3, 9, 6, 8, 2, 3, 3, 1, 6, 1, 7, 0, 3, 4, 2, 9,\n",
+            "        4, 5, 8, 2, 7, 0, 9, 6], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[21.8594, 19.6094, 19.7031, 18.4531, 18.0469, 18.7812, 17.7812, 19.0000,\n",
+            "         26.0625, 18.9688],\n",
+            "        [20.5938, 20.8750, 24.2344, 22.8438, 30.4062, 23.9688, 20.0938, 25.6094,\n",
+            "         20.0000, 20.6250],\n",
+            "        [22.0156, 21.1875, 23.0000, 24.7500, 27.5469, 26.2656, 19.5000, 26.1250,\n",
+            "         20.4688, 21.1562],\n",
+            "        [19.4688, 18.7031, 20.5312, 24.1094, 19.7188, 26.9531, 21.1094, 20.7812,\n",
+            "         19.1562, 19.1719],\n",
+            "        [18.3594, 18.2188, 19.9062, 19.4688, 18.0469, 20.0000, 14.9688, 28.0781,\n",
+            "         18.0781, 17.5469],\n",
+            "        [18.4688, 23.5469, 20.2969, 19.8906, 18.7969, 19.3594, 20.7344, 19.4688,\n",
+            "         19.1562, 24.7344],\n",
+            "        [20.8906, 20.9531, 21.7656, 22.4688, 21.0469, 27.6094, 22.2656, 23.3281,\n",
+            "         19.8906, 19.6094],\n",
+            "        [20.4062, 20.9531, 22.5625, 22.6562, 25.8906, 24.5938, 19.4531, 27.6094,\n",
+            "         20.0469, 20.2344],\n",
+            "        [20.5625, 20.7500, 21.3750, 25.9062, 20.0156, 24.1875, 19.7969, 22.4062,\n",
+            "         20.9688, 20.8125],\n",
+            "        [20.2812, 23.5781, 20.0625, 18.6875, 17.8906, 20.0000, 18.8125, 19.7969,\n",
+            "         21.5781, 24.9688],\n",
+            "        [18.7969, 18.8594, 20.8125, 21.4531, 22.8125, 23.4688, 21.8906, 21.1562,\n",
+            "         18.6250, 17.6406],\n",
+            "        [18.4531, 20.0938, 18.9219, 17.2344, 15.9844, 17.9844, 17.5781, 17.0625,\n",
+            "         22.2188, 17.3438],\n",
+            "        [23.0469, 20.7969, 28.1094, 21.2500, 23.5781, 21.9062, 24.5781, 21.9844,\n",
+            "         22.7969, 19.7812],\n",
+            "        [19.3281, 20.4219, 21.7500, 27.4688, 20.7969, 23.2031, 21.5469, 21.3281,\n",
+            "         19.6719, 19.2656],\n",
+            "        [18.9688, 20.1875, 21.9375, 27.1562, 19.1719, 23.1406, 19.6406, 22.0156,\n",
+            "         19.6719, 18.7656],\n",
+            "        [19.5938, 26.1250, 20.6562, 21.1562, 20.2656, 21.5000, 21.3750, 20.3281,\n",
+            "         19.9062, 22.2188],\n",
+            "        [18.6719, 19.5312, 23.8906, 21.5312, 21.5000, 21.8594, 26.3594, 21.7656,\n",
+            "         20.2500, 18.5469],\n",
+            "        [20.0781, 26.7188, 20.1094, 19.5312, 17.7344, 20.3281, 21.1406, 18.2656,\n",
+            "         20.0938, 23.0938],\n",
+            "        [17.1719, 18.9688, 20.0000, 19.1875, 18.8750, 20.7344, 14.0469, 28.3750,\n",
+            "         18.0312, 18.3906],\n",
+            "        [25.9688, 19.8281, 22.6250, 18.6406, 19.1094, 19.0469, 19.8750, 19.9062,\n",
+            "         22.0938, 20.3281],\n",
+            "        [19.7656, 20.3281, 21.7031, 26.9219, 19.7188, 23.1562, 20.8750, 20.6875,\n",
+            "         20.2344, 19.4375],\n",
+            "        [18.0469, 18.4844, 21.5000, 21.1719, 26.6562, 23.6406, 21.7188, 21.5938,\n",
+            "         19.0625, 18.1406],\n",
+            "        [22.2969, 21.1875, 29.5625, 22.6562, 21.1406, 22.7031, 21.3281, 21.3594,\n",
+            "         20.9844, 19.9375],\n",
+            "        [19.4219, 23.6250, 17.5938, 16.5156, 16.3594, 18.7812, 16.6875, 17.5469,\n",
+            "         18.2969, 23.9531],\n",
+            "        [20.5312, 21.4062, 21.0938, 21.2656, 26.0938, 22.6094, 18.9219, 25.8594,\n",
+            "         19.5000, 21.2500],\n",
+            "        [16.7969, 17.7969, 19.6094, 20.6094, 22.6562, 25.3750, 18.2031, 24.4062,\n",
+            "         17.4531, 18.1094],\n",
+            "        [21.2812, 22.3594, 22.2031, 20.8750, 21.0625, 20.5781, 18.9531, 20.8906,\n",
+            "         26.3906, 21.5156],\n",
+            "        [19.7500, 19.9688, 22.1875, 20.4062, 24.3125, 21.1719, 18.8281, 25.2031,\n",
+            "         18.7656, 20.0469],\n",
+            "        [20.7656, 20.4531, 20.8125, 20.7031, 22.3281, 22.0781, 19.6406, 27.8125,\n",
+            "         19.0625, 19.6875],\n",
+            "        [26.7656, 20.6094, 23.3906, 18.3594, 18.5938, 19.2656, 16.9375, 19.3750,\n",
+            "         21.4688, 19.9375],\n",
+            "        [18.2656, 23.2188, 18.9531, 17.9844, 18.2188, 18.9375, 18.1406, 19.6875,\n",
+            "         18.7812, 26.0781],\n",
+            "        [18.7188, 19.9062, 21.5938, 19.9219, 22.1875, 21.2969, 23.5000, 20.0469,\n",
+            "         18.7500, 18.2969]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[8],\n",
+            "        [4],\n",
+            "        [4],\n",
+            "        [5],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [7],\n",
+            "        [3],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [8],\n",
+            "        [2],\n",
+            "        [3],\n",
+            "        [3],\n",
+            "        [1],\n",
+            "        [6],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [3],\n",
+            "        [4],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [4],\n",
+            "        [5],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [6]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[8, 4, 4, 5, 7, 9, 5, 7, 3, 9, 5, 8, 2, 3, 3, 1, 6, 1, 7, 0, 3, 4, 2, 9,\n",
+            "         4, 5, 8, 7, 7, 0, 9, 6],\n",
+            "        [0, 7, 5, 3, 5, 1, 7, 4, 5, 1, 4, 1, 6, 5, 5, 9, 2, 9, 5, 2, 5, 5, 5, 1,\n",
+            "         7, 7, 1, 4, 4, 2, 1, 4],\n",
+            "        [2, 2, 7, 6, 2, 6, 3, 5, 7, 8, 6, 2, 4, 2, 7, 5, 5, 6, 2, 8, 2, 6, 3, 0,\n",
+            "         5, 4, 2, 2, 5, 8, 7, 2],\n",
+            "        [1, 5, 3, 7, 3, 2, 6, 3, 2, 0, 3, 0, 0, 6, 2, 6, 7, 5, 3, 9, 6, 7, 0, 5,\n",
+            "         1, 3, 9, 5, 2, 1, 2, 5],\n",
+            "        [7, 3, 2, 2, 0, 3, 2, 2, 8, 2, 7, 5, 8, 7, 1, 3, 3, 2, 1, 7, 7, 2, 7, 8,\n",
+            "         3, 2, 0, 3, 0, 9, 5, 7]], device='cuda:0')\n",
+            "tensor([8, 0, 8, 2, 8, 5, 7, 7, 2, 2, 0, 0, 0, 7, 4, 1, 6, 6, 8, 8, 9, 0, 9, 0,\n",
+            "        1, 3, 3, 0, 9, 6, 6, 2], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[20.9219, 22.4062, 21.1250, 20.4844, 18.6562, 20.0781, 17.3906, 20.0312,\n",
+            "         27.5000, 21.9062],\n",
+            "        [28.9844, 20.6250, 25.0781, 19.5781, 21.3750, 20.3594, 21.2969, 20.8281,\n",
+            "         22.7656, 20.1719],\n",
+            "        [22.2656, 21.1094, 21.7500, 20.5000, 19.4688, 20.4844, 18.5000, 20.3438,\n",
+            "         27.5000, 19.7188],\n",
+            "        [19.8438, 19.5469, 28.7031, 21.2500, 22.4688, 21.4531, 20.7188, 21.4062,\n",
+            "         19.1094, 18.7031],\n",
+            "        [20.5469, 22.3125, 19.5156, 18.8281, 17.7656, 18.6406, 18.8438, 17.9844,\n",
+            "         23.1406, 18.6875],\n",
+            "        [20.6562, 20.2031, 21.7031, 22.2344, 20.4688, 27.8750, 21.0938, 21.8438,\n",
+            "         19.6094, 19.3906],\n",
+            "        [18.9219, 19.3438, 21.2031, 20.3594, 20.5625, 21.0312, 16.8594, 28.3594,\n",
+            "         19.4688, 18.7344],\n",
+            "        [20.9219, 21.2031, 22.6406, 21.7656, 23.2500, 22.3438, 17.3125, 30.4688,\n",
+            "         20.0156, 20.2344],\n",
+            "        [21.2812, 22.3594, 25.5469, 24.2344, 23.6406, 23.0938, 25.4531, 21.8281,\n",
+            "         21.0312, 21.7344],\n",
+            "        [19.9844, 19.0469, 27.8281, 21.4375, 21.0938, 20.8594, 20.5312, 20.6406,\n",
+            "         18.7031, 18.7969],\n",
+            "        [22.3438, 20.4531, 22.7188, 18.9375, 19.5312, 19.7500, 20.0156, 20.2969,\n",
+            "         20.9062, 19.9219],\n",
+            "        [27.3594, 20.8906, 23.1094, 18.7656, 17.8125, 19.3594, 18.1719, 19.4531,\n",
+            "         23.4375, 19.2969],\n",
+            "        [25.9531, 22.1250, 23.0469, 19.9219, 20.2344, 20.3438, 19.9531, 21.3281,\n",
+            "         20.9844, 20.5000],\n",
+            "        [18.7969, 20.2969, 20.5938, 20.5625, 22.3594, 22.0469, 15.6641, 28.5000,\n",
+            "         19.3281, 19.3438],\n",
+            "        [19.7500, 18.1406, 21.9688, 20.4375, 27.0312, 20.8281, 17.4844, 24.4688,\n",
+            "         18.9062, 17.4531],\n",
+            "        [18.2344, 25.5000, 20.2812, 19.5156, 18.3750, 19.2969, 17.7812, 18.9844,\n",
+            "         18.8750, 20.6094],\n",
+            "        [20.8594, 21.5000, 22.4062, 22.3750, 21.3750, 22.3281, 25.1562, 20.8906,\n",
+            "         21.3906, 19.5469],\n",
+            "        [19.9844, 20.7812, 20.3750, 19.7969, 18.7500, 19.4375, 21.3125, 19.3750,\n",
+            "         20.3125, 19.7031],\n",
+            "        [23.7656, 24.5625, 21.5938, 20.4375, 19.8594, 21.2969, 19.7188, 21.1250,\n",
+            "         26.6875, 23.4219],\n",
+            "        [19.2656, 19.1875, 19.8906, 17.5625, 17.9688, 18.7344, 16.4844, 18.7031,\n",
+            "         26.0312, 18.9062],\n",
+            "        [18.1094, 24.3125, 19.8906, 18.2656, 17.9375, 19.0000, 18.2188, 19.3906,\n",
+            "         19.8750, 27.7969],\n",
+            "        [26.1562, 24.3281, 21.3125, 20.1406, 19.9688, 20.6094, 19.7344, 20.9375,\n",
+            "         24.5625, 25.6250],\n",
+            "        [20.3438, 23.8281, 21.1562, 20.4219, 19.5000, 20.5312, 18.8438, 21.0938,\n",
+            "         21.9531, 28.1250],\n",
+            "        [26.2656, 21.2656, 27.2969, 22.2812, 22.1094, 22.1094, 22.5312, 22.6875,\n",
+            "         24.0625, 19.7031],\n",
+            "        [17.0469, 23.9844, 18.4531, 17.4844, 15.3906, 18.4062, 17.5156, 14.9844,\n",
+            "         18.0625, 19.2812],\n",
+            "        [20.2500, 20.5156, 23.6875, 25.8750, 23.7969, 23.0781, 23.9219, 22.3906,\n",
+            "         21.2500, 19.2500],\n",
+            "        [18.7344, 19.0625, 20.8594, 26.3281, 18.5781, 21.7344, 20.4844, 20.7500,\n",
+            "         20.5000, 17.8906],\n",
+            "        [27.7500, 24.0469, 25.0000, 21.4688, 21.0781, 21.7969, 22.3125, 24.0312,\n",
+            "         23.6562, 22.8750],\n",
+            "        [17.3125, 22.2344, 19.0938, 18.1406, 18.3750, 19.1094, 17.0156, 19.2500,\n",
+            "         19.6250, 26.3281],\n",
+            "        [22.9688, 21.3281, 25.9375, 22.3750, 22.2812, 22.7031, 27.4844, 22.8438,\n",
+            "         23.2500, 20.6406],\n",
+            "        [22.2500, 21.4688, 24.8594, 22.6250, 22.5469, 23.2344, 24.5156, 23.7031,\n",
+            "         21.5000, 20.1250],\n",
+            "        [19.6875, 19.2500, 27.4531, 23.4844, 25.9219, 22.7344, 23.0312, 21.7812,\n",
+            "         18.7344, 18.8594]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[8],\n",
+            "        [0],\n",
+            "        [8],\n",
+            "        [2],\n",
+            "        [8],\n",
+            "        [5],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [0],\n",
+            "        [0],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [1],\n",
+            "        [6],\n",
+            "        [6],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [3],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [6],\n",
+            "        [2],\n",
+            "        [2]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[8, 0, 8, 2, 8, 5, 7, 7, 2, 2, 2, 0, 0, 7, 4, 1, 6, 6, 8, 8, 9, 0, 9, 2,\n",
+            "         1, 3, 3, 0, 9, 6, 2, 2],\n",
+            "        [1, 2, 0, 4, 1, 3, 2, 4, 6, 3, 0, 8, 2, 4, 7, 9, 2, 1, 1, 2, 1, 9, 1, 0,\n",
+            "         9, 6, 5, 2, 1, 2, 6, 4],\n",
+            "        [9, 8, 2, 5, 0, 7, 5, 2, 3, 4, 8, 2, 1, 5, 2, 2, 3, 2, 0, 0, 2, 8, 8, 8,\n",
+            "         2, 4, 2, 1, 8, 8, 7, 3],\n",
+            "        [2, 4, 1, 7, 2, 2, 4, 5, 4, 5, 1, 1, 7, 2, 5, 3, 5, 8, 9, 1, 8, 1, 2, 7,\n",
+            "         5, 2, 7, 7, 7, 0, 5, 6],\n",
+            "        [0, 6, 3, 3, 6, 6, 3, 3, 5, 7, 7, 7, 8, 3, 3, 5, 1, 0, 2, 9, 7, 2, 7, 6,\n",
+            "         8, 5, 8, 8, 5, 7, 3, 5]], device='cuda:0')\n",
+            "tensor([6, 3, 4, 0, 8, 4, 1, 4, 0, 6, 5, 0, 9, 9, 9, 9, 1, 2, 3, 5, 4, 2, 9, 6,\n",
+            "        0, 9, 6, 6, 8, 0, 6, 1], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[24.0469, 19.6719, 22.2656, 19.6875, 21.1562, 19.6094, 21.7812, 19.8906,\n",
+            "         21.6875, 18.8438],\n",
+            "        [19.7031, 20.1719, 21.9531, 27.4688, 24.0469, 24.3125, 21.5938, 21.7969,\n",
+            "         20.2188, 19.4844],\n",
+            "        [23.3594, 23.2969, 23.8906, 22.3281, 26.9844, 23.6875, 21.4062, 29.0938,\n",
+            "         22.2188, 21.1562],\n",
+            "        [27.8438, 22.3125, 22.9531, 19.8750, 19.0312, 20.1250, 19.9531, 20.0156,\n",
+            "         26.9531, 21.4531],\n",
+            "        [21.6406, 22.7969, 21.5156, 21.0000, 20.2031, 20.9531, 17.8750, 21.7344,\n",
+            "         28.9375, 23.2031],\n",
+            "        [18.3438, 18.9844, 21.1406, 21.8281, 26.3594, 23.1875, 19.2656, 24.2188,\n",
+            "         18.3281, 18.2031],\n",
+            "        [20.4375, 25.5000, 19.1562, 18.9375, 18.5625, 19.7500, 19.1562, 18.3906,\n",
+            "         19.5156, 24.1875],\n",
+            "        [19.0781, 19.8281, 21.6562, 20.5938, 26.3594, 22.1875, 16.5312, 29.2812,\n",
+            "         18.9844, 19.3750],\n",
+            "        [29.4062, 21.2500, 25.5000, 20.4062, 21.5625, 21.5312, 19.8438, 21.4375,\n",
+            "         22.6875, 21.6719],\n",
+            "        [19.8594, 21.3594, 22.9531, 25.8125, 22.8750, 25.7812, 23.5781, 23.0312,\n",
+            "         21.2656, 20.4531],\n",
+            "        [20.8750, 21.2656, 24.0625, 23.6719, 23.0312, 26.2031, 23.6406, 23.0469,\n",
+            "         20.0312, 21.0000],\n",
+            "        [23.1094, 20.1094, 21.3750, 19.1250, 17.9844, 19.8750, 18.5938, 19.7656,\n",
+            "         20.3906, 18.8125],\n",
+            "        [17.3906, 22.7656, 19.3281, 18.2656, 16.8750, 18.7344, 20.2188, 18.3281,\n",
+            "         19.3438, 26.5625],\n",
+            "        [22.3125, 23.7188, 22.1719, 20.9531, 19.8594, 21.0469, 19.9375, 21.6562,\n",
+            "         23.4375, 25.7812],\n",
+            "        [19.2656, 24.3906, 20.2656, 22.1094, 21.2969, 22.1719, 19.7656, 21.9062,\n",
+            "         20.8281, 25.9844],\n",
+            "        [18.4844, 22.3281, 17.8125, 17.5469, 17.2031, 17.9844, 16.7344, 19.0469,\n",
+            "         19.3906, 22.9531],\n",
+            "        [16.8125, 26.4688, 19.0000, 17.8750, 17.5469, 18.0469, 19.1406, 17.0938,\n",
+            "         18.3125, 21.4219],\n",
+            "        [19.5156, 19.0000, 28.1562, 20.0938, 20.3125, 20.6719, 19.5469, 19.2031,\n",
+            "         17.8906, 18.8594],\n",
+            "        [22.6250, 23.6719, 25.4688, 25.3594, 23.8594, 25.2344, 25.3906, 24.2812,\n",
+            "         22.2656, 22.3438],\n",
+            "        [20.7500, 21.1562, 24.1875, 25.0625, 23.7500, 26.8750, 22.5156, 24.8906,\n",
+            "         20.6719, 20.4844],\n",
+            "        [17.6250, 18.3125, 20.5312, 17.6250, 28.3750, 19.9062, 16.5625, 21.9062,\n",
+            "         16.6719, 17.9531],\n",
+            "        [21.4062, 20.3125, 29.1250, 22.5000, 21.6250, 22.6250, 23.0938, 21.6875,\n",
+            "         19.5781, 19.9844],\n",
+            "        [18.7812, 21.6250, 18.3125, 17.0938, 16.8281, 18.1094, 15.6641, 18.4375,\n",
+            "         20.6719, 25.6719],\n",
+            "        [19.0781, 19.7500, 24.0000, 20.2344, 21.3438, 20.3750, 27.9375, 19.9062,\n",
+            "         19.5469, 18.8906],\n",
+            "        [25.8125, 22.5312, 23.8281, 20.1562, 21.7969, 21.8594, 22.3125, 21.0781,\n",
+            "         24.5625, 22.1562],\n",
+            "        [21.8906, 21.6250, 19.9688, 19.1562, 18.8594, 20.0469, 17.5625, 20.5469,\n",
+            "         22.6875, 22.8281],\n",
+            "        [17.9531, 19.9062, 22.5938, 21.6406, 20.0469, 22.2031, 27.5156, 20.8438,\n",
+            "         19.9375, 18.9062],\n",
+            "        [18.6719, 20.7812, 23.2188, 23.6562, 21.4375, 23.9375, 27.1406, 21.2031,\n",
+            "         20.0312, 18.7969],\n",
+            "        [21.5000, 22.4844, 20.5312, 20.8906, 18.7812, 19.2656, 17.9062, 19.1562,\n",
+            "         26.1875, 19.4219],\n",
+            "        [25.6094, 20.5000, 24.1094, 20.4844, 21.0156, 20.4062, 20.5625, 20.7812,\n",
+            "         22.8281, 18.7188],\n",
+            "        [19.7344, 21.0000, 23.9219, 23.5469, 23.4688, 23.9375, 24.6562, 23.8594,\n",
+            "         19.9688, 19.7969],\n",
+            "        [20.0938, 24.1719, 20.5469, 20.6719, 20.4062, 21.4375, 20.5156, 20.1562,\n",
+            "         21.4375, 24.2969]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[0],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [8],\n",
+            "        [4],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [3],\n",
+            "        [5],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [9],\n",
+            "        [9],\n",
+            "        [9],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [5],\n",
+            "        [4],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [6],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [6],\n",
+            "        [6],\n",
+            "        [8],\n",
+            "        [0],\n",
+            "        [6],\n",
+            "        [9]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[0, 3, 7, 0, 8, 4, 1, 7, 0, 3, 5, 0, 9, 9, 9, 9, 1, 2, 2, 5, 4, 2, 9, 6,\n",
+            "         0, 9, 6, 6, 8, 0, 6, 9],\n",
+            "        [2, 5, 4, 8, 9, 7, 9, 4, 2, 5, 2, 2, 1, 1, 1, 1, 9, 5, 6, 3, 7, 6, 1, 2,\n",
+            "         8, 8, 2, 5, 1, 2, 5, 1],\n",
+            "        [6, 4, 2, 2, 1, 5, 0, 5, 8, 6, 3, 8, 6, 8, 5, 8, 6, 4, 3, 7, 2, 5, 8, 4,\n",
+            "         2, 0, 5, 3, 0, 8, 2, 8],\n",
+            "        [8, 2, 5, 1, 7, 3, 5, 2, 9, 7, 6, 1, 8, 0, 3, 7, 2, 3, 5, 2, 5, 3, 0, 5,\n",
+            "         1, 1, 3, 2, 3, 4, 7, 5],\n",
+            "        [4, 7, 0, 9, 0, 2, 8, 3, 4, 2, 7, 5, 2, 2, 7, 0, 8, 6, 7, 4, 1, 7, 7, 3,\n",
+            "         6, 7, 7, 4, 2, 7, 3, 3]], device='cuda:0')\n",
+            "tensor([4, 6, 8, 0, 5, 4, 1, 2, 0, 9, 6, 4, 2, 4, 6, 5, 9, 7, 7, 4, 6, 5, 0, 1,\n",
+            "        9, 0, 3, 1, 9, 0, 9, 7], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[20.0938, 19.6875, 23.2656, 20.8125, 27.7344, 22.0000, 20.6250, 21.7188,\n",
+            "         19.2656, 19.3750],\n",
+            "        [17.9375, 19.0938, 22.0781, 20.1562, 19.5000, 20.6250, 24.3281, 20.4531,\n",
+            "         19.9844, 18.1406],\n",
+            "        [18.6562, 19.4375, 19.8750, 18.2969, 18.3438, 19.1875, 17.3906, 18.7812,\n",
+            "         24.3594, 18.5625],\n",
+            "        [26.9688, 21.8750, 22.0938, 19.6719, 19.1250, 19.1250, 18.8750, 20.6562,\n",
+            "         23.7812, 20.4219],\n",
+            "        [19.8594, 20.1094, 20.9844, 22.3125, 20.2031, 27.5000, 21.2188, 21.5938,\n",
+            "         19.2344, 19.2500],\n",
+            "        [21.0312, 20.0312, 23.8125, 21.8906, 29.6094, 23.7969, 21.1875, 25.5000,\n",
+            "         19.2031, 20.0469],\n",
+            "        [19.7188, 27.1562, 20.3438, 20.5156, 19.1719, 21.4531, 20.4688, 20.6719,\n",
+            "         20.1406, 24.0312],\n",
+            "        [20.3438, 19.7969, 27.5312, 19.7500, 20.1875, 20.3281, 22.3125, 19.2188,\n",
+            "         18.5938, 18.5000],\n",
+            "        [27.5000, 21.2031, 26.8750, 20.4219, 21.2500, 21.0156, 21.8125, 21.2031,\n",
+            "         23.5312, 20.1250],\n",
+            "        [15.5547, 21.6094, 17.4219, 15.8906, 15.5312, 16.7188, 15.3828, 17.6875,\n",
+            "         17.3906, 26.0625],\n",
+            "        [18.3750, 19.3438, 21.7812, 22.9844, 22.3281, 23.5000, 22.5469, 23.0781,\n",
+            "         19.3438, 18.8750],\n",
+            "        [19.7188, 19.4219, 22.3125, 21.2031, 29.5625, 22.7656, 19.8438, 23.2188,\n",
+            "         18.3438, 18.4688],\n",
+            "        [18.2656, 18.6719, 27.0312, 19.8594, 19.7031, 19.3438, 22.3281, 19.3125,\n",
+            "         18.9531, 16.9062],\n",
+            "        [20.0625, 20.7188, 22.6875, 24.5938, 29.9062, 24.7188, 19.6875, 25.7812,\n",
+            "         20.0156, 20.5781],\n",
+            "        [19.7031, 21.0625, 22.6094, 21.7812, 21.2969, 21.2500, 26.0625, 20.0156,\n",
+            "         20.8125, 19.2656],\n",
+            "        [18.4219, 19.6250, 21.7656, 25.8750, 21.5156, 23.9844, 22.6562, 21.5469,\n",
+            "         19.5312, 18.7969],\n",
+            "        [17.6094, 21.9375, 19.6094, 18.0000, 17.4062, 18.4219, 17.1875, 19.0156,\n",
+            "         19.3906, 26.4375],\n",
+            "        [18.9688, 20.2812, 20.4688, 21.2500, 24.9844, 23.1719, 16.9688, 27.1875,\n",
+            "         19.1406, 19.4844],\n",
+            "        [19.4219, 20.1094, 20.7812, 20.3125, 20.7500, 21.1250, 16.5781, 27.6875,\n",
+            "         19.1094, 19.2500],\n",
+            "        [21.6406, 22.3750, 25.5000, 25.8594, 25.2031, 25.0000, 27.3125, 23.1094,\n",
+            "         21.8594, 20.7812],\n",
+            "        [19.2500, 20.0781, 22.6250, 22.7500, 21.0156, 23.3750, 30.6406, 19.8125,\n",
+            "         19.5469, 19.4375],\n",
+            "        [19.4531, 19.7031, 21.8906, 23.7656, 22.7344, 26.4844, 22.3750, 22.6094,\n",
+            "         19.0625, 19.4688],\n",
+            "        [24.5469, 17.6562, 21.5625, 16.7812, 18.4844, 17.0156, 16.9688, 17.4062,\n",
+            "         19.5156, 16.7344],\n",
+            "        [20.5000, 25.3906, 20.6250, 18.9375, 18.9844, 19.7031, 20.2344, 18.9219,\n",
+            "         19.9531, 24.4375],\n",
+            "        [18.6875, 24.7188, 19.2812, 19.2188, 18.4062, 20.1250, 18.3125, 19.8125,\n",
+            "         19.6875, 24.9688],\n",
+            "        [29.9062, 24.5312, 24.2344, 21.5000, 21.4375, 21.6875, 19.5625, 21.6094,\n",
+            "         25.1562, 25.3125],\n",
+            "        [22.3125, 21.6250, 23.9531, 27.1875, 22.7344, 26.7500, 25.4844, 23.6562,\n",
+            "         22.1562, 20.4688],\n",
+            "        [19.5469, 25.8438, 20.4375, 20.4844, 21.3125, 20.7500, 20.3750, 20.1875,\n",
+            "         19.2812, 22.4688],\n",
+            "        [18.0000, 22.3750, 19.0938, 17.9375, 18.9531, 18.6562, 17.4062, 19.2656,\n",
+            "         19.3750, 26.7656],\n",
+            "        [27.2344, 21.7500, 23.5156, 20.2656, 19.4219, 20.1875, 19.0781, 19.8438,\n",
+            "         22.4844, 19.7812],\n",
+            "        [18.0938, 21.5312, 18.7656, 17.5938, 16.5625, 18.1250, 16.7812, 18.1719,\n",
+            "         19.4062, 23.8125],\n",
+            "        [20.3438, 20.2812, 20.8594, 20.8281, 21.0312, 21.8594, 16.8438, 29.1406,\n",
+            "         18.9844, 20.0312]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[4],\n",
+            "        [6],\n",
+            "        [8],\n",
+            "        [0],\n",
+            "        [5],\n",
+            "        [4],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [4],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [6],\n",
+            "        [3],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [6],\n",
+            "        [5],\n",
+            "        [0],\n",
+            "        [1],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [3],\n",
+            "        [1],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [7]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[4, 6, 8, 0, 5, 4, 1, 2, 0, 9, 5, 4, 2, 4, 6, 3, 9, 7, 7, 6, 6, 5, 0, 1,\n",
+            "         9, 0, 3, 1, 9, 0, 9, 7],\n",
+            "        [2, 2, 2, 8, 3, 7, 9, 6, 2, 1, 7, 7, 6, 7, 2, 5, 1, 4, 5, 3, 5, 3, 2, 9,\n",
+            "         1, 9, 5, 9, 1, 2, 1, 5],\n",
+            "        [5, 5, 1, 2, 7, 2, 5, 0, 8, 7, 3, 5, 3, 5, 3, 6, 2, 5, 2, 2, 3, 4, 8, 2,\n",
+            "         5, 8, 6, 4, 8, 8, 8, 4],\n",
+            "        [7, 7, 5, 1, 6, 5, 7, 5, 6, 2, 6, 2, 4, 3, 4, 2, 8, 3, 4, 4, 2, 7, 4, 0,\n",
+            "         7, 1, 2, 5, 7, 1, 2, 2],\n",
+            "        [3, 3, 7, 7, 2, 3, 3, 4, 4, 8, 4, 3, 5, 2, 5, 7, 7, 2, 3, 5, 4, 6, 1, 6,\n",
+            "         8, 2, 7, 3, 2, 3, 7, 3]], device='cuda:0')\n",
+            "tensor([8, 6, 7, 6, 8, 2, 4, 5, 3, 0, 3, 2, 1, 7, 5, 9, 3, 4, 5, 7, 1, 5, 0, 1,\n",
+            "        1, 1, 9, 7, 5, 4, 9, 7], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[23.0000, 20.7344, 22.3594, 20.0156, 19.8438, 20.2656, 18.3438, 21.1719,\n",
+            "         27.6719, 19.7969],\n",
+            "        [20.5625, 21.1562, 23.7812, 21.1406, 22.3281, 21.4375, 24.0938, 21.5312,\n",
+            "         21.2500, 19.4375],\n",
+            "        [17.4375, 19.7188, 19.3594, 18.8281, 19.8125, 20.1719, 14.9609, 26.9062,\n",
+            "         17.6875, 18.8906],\n",
+            "        [19.4531, 21.3281, 22.5156, 22.8281, 22.4531, 23.7656, 23.4688, 22.2812,\n",
+            "         20.1250, 20.1094],\n",
+            "        [18.3594, 18.1719, 17.9844, 16.1406, 15.6719, 16.9219, 15.0938, 16.9688,\n",
+            "         25.1562, 18.0156],\n",
+            "        [19.4062, 19.8281, 26.2656, 22.9531, 23.7188, 24.0156, 20.5938, 24.0156,\n",
+            "         18.7188, 19.7344],\n",
+            "        [17.6406, 17.7031, 19.3281, 17.2812, 20.5625, 19.0000, 17.9688, 19.0469,\n",
+            "         17.4531, 18.1875],\n",
+            "        [17.8906, 18.1250, 19.3438, 20.3125, 18.9219, 25.4375, 18.0156, 21.0625,\n",
+            "         17.7188, 17.5312],\n",
+            "        [21.7188, 21.7031, 23.5312, 26.9062, 23.0938, 25.2969, 22.9219, 23.5625,\n",
+            "         22.5312, 20.4219],\n",
+            "        [25.8750, 17.9219, 21.9688, 17.2188, 16.7500, 17.2188, 16.8594, 17.4062,\n",
+            "         20.8281, 16.8750],\n",
+            "        [20.1094, 21.4844, 22.8906, 28.1562, 22.0625, 24.4688, 22.1719, 23.0312,\n",
+            "         20.5625, 19.6719],\n",
+            "        [18.4844, 17.4062, 26.3125, 19.9688, 19.4219, 19.3438, 20.6562, 18.6094,\n",
+            "         17.8594, 17.5938],\n",
+            "        [18.4219, 25.8906, 20.5000, 18.6719, 19.4375, 19.8438, 19.8281, 18.6562,\n",
+            "         19.1719, 20.3281],\n",
+            "        [19.1250, 19.9375, 21.1719, 20.6094, 20.8750, 21.5469, 17.4062, 28.0781,\n",
+            "         18.7969, 19.3750],\n",
+            "        [21.1875, 21.2812, 23.9375, 25.2500, 24.6406, 26.9531, 22.1094, 26.3750,\n",
+            "         21.4688, 19.8438],\n",
+            "        [20.7031, 25.3906, 19.9688, 19.3906, 18.9375, 20.2188, 20.0312, 19.9844,\n",
+            "         20.2812, 27.5625],\n",
+            "        [18.9219, 18.6250, 21.2812, 25.2812, 19.9219, 23.8750, 21.9531, 20.6875,\n",
+            "         19.4375, 18.1719],\n",
+            "        [17.9531, 18.9062, 21.6719, 20.0625, 29.4219, 21.6406, 18.3125, 21.9531,\n",
+            "         17.7344, 17.6719],\n",
+            "        [20.1094, 19.4844, 22.5312, 23.1562, 22.2188, 26.5469, 20.7656, 25.5469,\n",
+            "         19.7969, 19.0781],\n",
+            "        [19.6719, 20.5938, 19.7656, 21.7500, 20.7188, 24.6719, 18.6250, 23.1406,\n",
+            "         19.4062, 19.9688],\n",
+            "        [18.9531, 26.0938, 20.5938, 19.0938, 19.1562, 19.4219, 19.3438, 19.3594,\n",
+            "         19.4688, 22.4062],\n",
+            "        [20.0938, 20.0625, 22.7188, 23.0312, 19.6875, 24.7656, 23.7344, 20.6562,\n",
+            "         20.8750, 19.5312],\n",
+            "        [28.4688, 21.4219, 24.4688, 19.0781, 19.9844, 19.9531, 18.8594, 19.5625,\n",
+            "         22.0938, 20.2812],\n",
+            "        [19.7500, 26.2188, 20.2969, 19.2656, 19.9688, 20.2812, 18.8438, 19.3750,\n",
+            "         19.5781, 20.8750],\n",
+            "        [19.0156, 26.2812, 21.4531, 20.0000, 19.6094, 20.2656, 19.5156, 18.6719,\n",
+            "         19.3438, 23.6719],\n",
+            "        [18.2188, 24.7812, 19.0938, 17.3750, 18.4375, 18.6875, 19.3750, 18.2344,\n",
+            "         17.7344, 23.7031],\n",
+            "        [19.3750, 23.6406, 20.9062, 19.5625, 18.5156, 19.1094, 18.3281, 19.6250,\n",
+            "         20.2500, 25.8438],\n",
+            "        [18.2344, 18.2969, 18.8125, 18.6406, 20.7344, 20.1875, 14.7578, 24.1094,\n",
+            "         17.7969, 17.6562],\n",
+            "        [18.4688, 18.9062, 19.5938, 23.1719, 20.0469, 26.6250, 20.3281, 20.6875,\n",
+            "         19.3281, 17.5312],\n",
+            "        [19.1094, 19.5938, 21.9844, 19.9844, 28.9062, 22.1406, 18.1094, 23.9844,\n",
+            "         18.3438, 19.0000],\n",
+            "        [18.1719, 22.5781, 18.9062, 17.2500, 17.1250, 18.3906, 19.3906, 19.2812,\n",
+            "         19.3750, 22.7969],\n",
+            "        [17.8906, 19.9219, 20.5156, 19.7500, 19.6094, 20.8906, 15.4375, 28.9844,\n",
+            "         18.6094, 18.5156]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[8],\n",
+            "        [6],\n",
+            "        [7],\n",
+            "        [5],\n",
+            "        [8],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [5],\n",
+            "        [3],\n",
+            "        [0],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [4],\n",
+            "        [5],\n",
+            "        [5],\n",
+            "        [1],\n",
+            "        [5],\n",
+            "        [0],\n",
+            "        [1],\n",
+            "        [1],\n",
+            "        [1],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [5],\n",
+            "        [4],\n",
+            "        [9],\n",
+            "        [7]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[8, 6, 7, 5, 8, 2, 4, 5, 3, 0, 3, 2, 1, 7, 5, 9, 3, 4, 5, 5, 1, 5, 0, 1,\n",
+            "         1, 1, 9, 7, 5, 4, 9, 7],\n",
+            "        [0, 2, 5, 6, 0, 7, 2, 7, 5, 2, 5, 6, 2, 5, 7, 1, 5, 7, 7, 7, 9, 6, 2, 9,\n",
+            "         9, 9, 1, 4, 3, 7, 1, 5],\n",
+            "        [2, 4, 4, 3, 1, 5, 7, 3, 7, 8, 7, 3, 9, 2, 3, 0, 6, 2, 3, 3, 2, 3, 8, 2,\n",
+            "         2, 6, 2, 5, 7, 5, 6, 2],\n",
+            "        [7, 7, 1, 2, 9, 4, 5, 2, 2, 1, 2, 4, 5, 4, 4, 8, 2, 5, 2, 4, 8, 2, 1, 5,\n",
+            "         5, 2, 8, 2, 6, 2, 8, 1],\n",
+            "        [1, 5, 2, 4, 2, 3, 9, 4, 4, 7, 6, 5, 6, 3, 2, 5, 7, 3, 4, 1, 5, 8, 9, 4,\n",
+            "         3, 5, 7, 3, 4, 3, 7, 3]], device='cuda:0')\n",
+            "tensor([8, 1, 0, 2, 8, 5, 6, 7, 0, 1, 4, 8, 4, 4, 6, 6, 5, 8, 1, 8, 4, 6, 5, 9,\n",
+            "        2, 2, 1, 4, 9, 1, 6, 7], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[22.5000, 21.0938, 21.0625, 18.2969, 19.5000, 19.5625, 18.1250, 19.1875,\n",
+            "         26.5781, 20.1094],\n",
+            "        [21.1719, 23.2656, 21.0625, 21.0938, 19.7344, 21.5469, 21.3750, 21.5312,\n",
+            "         21.3906, 21.0938],\n",
+            "        [25.9375, 18.2344, 22.4219, 16.9219, 18.7031, 18.3750, 16.7344, 17.9375,\n",
+            "         20.8750, 17.5000],\n",
+            "        [20.6875, 21.5469, 28.3281, 22.0156, 24.0312, 23.5000, 21.8906, 24.1562,\n",
+            "         20.3125, 20.9844],\n",
+            "        [21.2500, 22.9688, 20.8281, 20.6562, 19.4844, 20.6094, 19.9062, 20.0938,\n",
+            "         26.2031, 21.1094],\n",
+            "        [20.5156, 20.0625, 21.7969, 22.2812, 23.7344, 26.8906, 22.0156, 22.6875,\n",
+            "         19.2031, 18.8125],\n",
+            "        [18.8750, 18.7500, 22.8594, 19.8281, 19.5000, 20.5938, 24.8438, 19.4844,\n",
+            "         18.9219, 18.7031],\n",
+            "        [19.5938, 18.9219, 20.9219, 22.3594, 22.6094, 23.0000, 17.7656, 27.3125,\n",
+            "         19.1875, 19.2500],\n",
+            "        [30.7969, 22.7656, 28.3125, 22.2344, 22.1562, 22.2656, 23.3594, 22.4375,\n",
+            "         25.6406, 22.1094],\n",
+            "        [20.7969, 25.8906, 20.6250, 19.7188, 19.9219, 19.8906, 21.4844, 19.6562,\n",
+            "         21.8594, 25.0000],\n",
+            "        [20.6094, 19.8750, 23.1094, 22.5312, 29.7812, 22.4688, 18.6875, 22.6562,\n",
+            "         20.6250, 18.5625],\n",
+            "        [22.8281, 20.4531, 22.0625, 19.3906, 18.6406, 19.5781, 19.8594, 20.0469,\n",
+            "         27.6094, 19.5312],\n",
+            "        [21.0781, 19.0469, 21.2031, 19.3438, 25.0625, 19.8750, 18.8906, 21.5625,\n",
+            "         19.5312, 17.7812],\n",
+            "        [19.5938, 19.5156, 22.9062, 22.5781, 28.4531, 23.1562, 20.1562, 24.1562,\n",
+            "         19.0000, 18.6406],\n",
+            "        [17.7656, 20.1719, 23.2969, 23.6406, 22.7656, 23.0312, 27.9688, 21.1406,\n",
+            "         19.5000, 18.6250],\n",
+            "        [21.6875, 21.2031, 23.3281, 21.2500, 21.7500, 21.5156, 25.1250, 21.1719,\n",
+            "         20.8594, 19.3438],\n",
+            "        [19.1094, 19.8594, 20.9531, 21.5469, 22.9688, 26.8906, 19.5625, 24.2188,\n",
+            "         19.1094, 19.7188],\n",
+            "        [20.8750, 21.5000, 22.3438, 20.6406, 21.0469, 21.9844, 20.0938, 20.3750,\n",
+            "         23.9062, 20.4531],\n",
+            "        [17.5312, 24.7031, 19.1562, 18.5781, 19.3125, 18.2969, 18.1094, 18.7812,\n",
+            "         18.5781, 19.7969],\n",
+            "        [19.2656, 18.7656, 20.1094, 17.9531, 16.8281, 18.8750, 16.5156, 19.2656,\n",
+            "         25.7812, 17.4531],\n",
+            "        [18.4375, 20.2031, 23.3438, 21.3438, 26.3750, 23.7812, 18.7500, 24.3750,\n",
+            "         19.0781, 19.5625],\n",
+            "        [19.3281, 19.2812, 22.1875, 25.6406, 22.0938, 25.3594, 23.0938, 22.3906,\n",
+            "         19.0469, 18.4688],\n",
+            "        [21.5469, 20.5781, 22.8438, 24.7500, 22.6094, 28.2656, 21.0469, 24.3438,\n",
+            "         20.7031, 20.4844],\n",
+            "        [19.2969, 23.2812, 19.7812, 19.6406, 18.3125, 20.1094, 18.5625, 20.9531,\n",
+            "         20.8281, 24.9531],\n",
+            "        [23.6719, 21.5938, 29.3750, 24.2031, 21.3438, 23.2969, 23.4844, 22.0000,\n",
+            "         21.1250, 20.9375],\n",
+            "        [27.4531, 20.5469, 26.2188, 20.9219, 22.0156, 21.1875, 24.1406, 21.7344,\n",
+            "         23.6875, 21.2812],\n",
+            "        [19.1719, 25.6094, 21.3281, 20.8906, 19.9688, 21.5938, 19.9219, 20.9531,\n",
+            "         19.7188, 21.8438],\n",
+            "        [20.3438, 20.1250, 23.7188, 21.1875, 32.4375, 23.6719, 20.4375, 24.4219,\n",
+            "         18.9062, 19.2188],\n",
+            "        [17.6250, 22.2188, 19.9375, 17.6094, 18.1719, 17.9688, 18.1719, 19.0156,\n",
+            "         18.9219, 26.3438],\n",
+            "        [17.1250, 23.2656, 17.4531, 16.2812, 15.0625, 17.2500, 16.2188, 16.5312,\n",
+            "         18.1406, 23.0781],\n",
+            "        [18.3750, 19.1250, 21.0469, 20.7344, 20.1406, 20.9531, 21.3125, 21.5000,\n",
+            "         19.7031, 18.2969],\n",
+            "        [16.9844, 18.5938, 19.0938, 18.3594, 19.4062, 19.5781, 14.0625, 27.7344,\n",
+            "         17.3594, 17.6875]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[8],\n",
+            "        [1],\n",
+            "        [0],\n",
+            "        [2],\n",
+            "        [8],\n",
+            "        [5],\n",
+            "        [6],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [1],\n",
+            "        [4],\n",
+            "        [8],\n",
+            "        [4],\n",
+            "        [4],\n",
+            "        [6],\n",
+            "        [6],\n",
+            "        [5],\n",
+            "        [8],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [4],\n",
+            "        [3],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [2],\n",
+            "        [0],\n",
+            "        [1],\n",
+            "        [4],\n",
+            "        [9],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [7]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[8, 1, 0, 2, 8, 5, 6, 7, 0, 1, 4, 8, 4, 4, 6, 6, 5, 8, 1, 8, 4, 3, 5, 9,\n",
+            "         2, 0, 1, 4, 9, 1, 7, 7],\n",
+            "        [0, 5, 2, 7, 1, 4, 2, 5, 2, 9, 2, 0, 7, 7, 3, 2, 7, 2, 9, 2, 7, 5, 3, 1,\n",
+            "         3, 2, 9, 7, 1, 9, 6, 5],\n",
+            "        [1, 7, 8, 4, 0, 7, 5, 4, 8, 8, 7, 2, 2, 5, 2, 4, 4, 5, 4, 7, 5, 6, 7, 7,\n",
+            "         0, 6, 5, 2, 2, 8, 2, 4],\n",
+            "        [2, 8, 4, 5, 9, 3, 3, 3, 6, 6, 3, 1, 0, 2, 5, 0, 3, 1, 2, 0, 2, 7, 2, 8,\n",
+            "         6, 8, 2, 5, 7, 2, 5, 2],\n",
+            "        [9, 6, 5, 3, 2, 6, 4, 2, 1, 0, 5, 7, 5, 3, 4, 5, 2, 4, 7, 5, 3, 2, 4, 5,\n",
+            "         5, 4, 7, 3, 8, 5, 3, 1]], device='cuda:0')\n",
+            "tensor([2, 0, 1, 7, 6, 5, 2, 2, 5, 6, 0, 9, 0, 1, 5, 3, 3, 5, 8, 7, 5, 6, 5, 8,\n",
+            "        0, 5, 9, 4, 6, 5, 1, 1], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[19.4375, 18.5000, 27.7188, 20.6562, 19.1719, 20.3281, 21.0469, 18.2031,\n",
+            "         18.5469, 18.8125],\n",
+            "        [21.2500, 20.5781, 21.7500, 20.9062, 19.6094, 19.5781, 18.7344, 20.5781,\n",
+            "         21.1875, 19.6406],\n",
+            "        [18.4219, 24.9062, 20.6406, 18.8750, 17.5469, 20.7969, 18.5781, 19.8438,\n",
+            "         19.2656, 24.1719],\n",
+            "        [17.2344, 18.8750, 19.6094, 19.2656, 20.5156, 20.8438, 17.0000, 25.7969,\n",
+            "         17.4844, 18.4688],\n",
+            "        [18.3906, 20.5625, 22.4062, 20.3906, 19.5625, 21.8594, 30.2500, 18.6250,\n",
+            "         18.3125, 18.6406],\n",
+            "        [18.8750, 18.4219, 22.1406, 24.7188, 19.9844, 25.4062, 22.2969, 19.5625,\n",
+            "         20.0469, 19.1094],\n",
+            "        [20.5938, 20.2500, 26.4219, 22.9844, 25.8750, 23.6875, 22.7188, 23.6250,\n",
+            "         19.6250, 19.9375],\n",
+            "        [21.7812, 20.0312, 28.2500, 21.9531, 21.5000, 22.5625, 22.2969, 23.1875,\n",
+            "         20.9219, 19.3906],\n",
+            "        [19.7344, 20.1250, 21.1406, 21.8750, 20.9375, 27.0938, 20.2969, 23.5000,\n",
+            "         18.7969, 19.0938],\n",
+            "        [20.4688, 21.3125, 23.3438, 21.7031, 20.9531, 22.6094, 26.1094, 21.6562,\n",
+            "         21.7500, 19.5938],\n",
+            "        [28.4531, 20.9531, 25.5781, 19.3750, 20.4375, 20.2031, 19.9844, 20.3281,\n",
+            "         22.9375, 21.0000],\n",
+            "        [22.1094, 22.9219, 20.4375, 18.5156, 19.0625, 18.9531, 18.7656, 18.9375,\n",
+            "         23.9531, 23.4375],\n",
+            "        [24.3906, 21.4688, 21.1406, 18.2500, 16.4844, 19.1250, 18.8125, 18.9844,\n",
+            "         25.3438, 23.1250],\n",
+            "        [16.9219, 22.7500, 16.7656, 16.3906, 15.7422, 17.2500, 15.9062, 15.8906,\n",
+            "         18.0938, 21.0000],\n",
+            "        [18.3594, 20.0625, 19.5469, 22.3594, 22.5938, 26.6875, 18.6562, 25.0156,\n",
+            "         18.6250, 19.5469],\n",
+            "        [19.5312, 21.8125, 22.7344, 28.6719, 20.6250, 23.7031, 21.7188, 21.9688,\n",
+            "         20.5000, 19.4219],\n",
+            "        [19.7031, 20.3125, 22.6719, 27.5781, 20.5625, 23.9688, 20.9062, 21.8906,\n",
+            "         20.0000, 19.8438],\n",
+            "        [20.8906, 20.8281, 23.0625, 23.6406, 23.1250, 28.0625, 22.2188, 23.1094,\n",
+            "         20.5938, 20.9375],\n",
+            "        [19.3750, 20.5156, 20.5625, 18.8594, 19.3438, 18.8125, 18.0781, 20.0781,\n",
+            "         26.5469, 20.6719],\n",
+            "        [18.1250, 19.2812, 20.3438, 19.5625, 20.7031, 20.3906, 17.2656, 27.0312,\n",
+            "         17.8594, 18.0781],\n",
+            "        [18.2656, 18.0156, 20.5469, 21.8438, 18.6094, 26.1094, 19.3125, 20.8281,\n",
+            "         17.7656, 18.1406],\n",
+            "        [19.6562, 19.2969, 22.4531, 20.4844, 19.6719, 19.6875, 23.6406, 19.6875,\n",
+            "         20.0000, 17.4062],\n",
+            "        [20.6250, 20.9219, 22.4219, 23.8594, 20.7344, 27.3906, 22.3906, 23.0625,\n",
+            "         20.6875, 20.2812],\n",
+            "        [19.3281, 18.5000, 18.4844, 17.2500, 16.5625, 17.7969, 16.2969, 17.5156,\n",
+            "         25.6562, 18.0625],\n",
+            "        [28.9375, 24.9375, 24.2188, 22.2031, 21.1250, 22.0781, 20.7188, 21.5156,\n",
+            "         23.8594, 23.2031],\n",
+            "        [18.6562, 18.9219, 19.9062, 21.5000, 20.9688, 26.4688, 20.2969, 20.3750,\n",
+            "         18.2656, 18.2656],\n",
+            "        [20.8750, 25.1562, 20.9375, 19.8438, 18.8281, 20.2031, 20.2656, 21.5938,\n",
+            "         22.4375, 25.4531],\n",
+            "        [21.8594, 19.7031, 23.0625, 20.8438, 27.8125, 21.7344, 19.1562, 23.4688,\n",
+            "         19.8125, 18.7344],\n",
+            "        [18.1406, 20.0000, 23.5469, 21.8438, 20.1562, 23.1250, 27.7031, 20.3594,\n",
+            "         19.5781, 19.7656],\n",
+            "        [17.9219, 18.9062, 20.9219, 23.8438, 19.6875, 25.6562, 22.3281, 20.2031,\n",
+            "         18.3594, 17.6875],\n",
+            "        [19.5469, 25.4375, 19.5312, 18.8750, 17.6875, 19.1406, 18.5312, 19.8281,\n",
+            "         18.5625, 20.4375],\n",
+            "        [15.8750, 25.9688, 19.1406, 17.5625, 16.6719, 18.3125, 17.6406, 16.9844,\n",
+            "         17.3281, 22.8750]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[2],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [5],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [5],\n",
+            "        [6],\n",
+            "        [0],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [1],\n",
+            "        [5],\n",
+            "        [3],\n",
+            "        [3],\n",
+            "        [5],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [5],\n",
+            "        [6],\n",
+            "        [5],\n",
+            "        [8],\n",
+            "        [0],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [4],\n",
+            "        [6],\n",
+            "        [5],\n",
+            "        [1],\n",
+            "        [1]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[2, 2, 1, 7, 6, 5, 2, 2, 5, 6, 0, 8, 8, 1, 5, 3, 3, 5, 8, 7, 5, 6, 5, 8,\n",
+            "         0, 5, 9, 4, 6, 5, 1, 1],\n",
+            "        [6, 0, 9, 5, 2, 3, 4, 7, 7, 2, 2, 9, 0, 9, 7, 5, 5, 3, 9, 4, 3, 2, 3, 0,\n",
+            "         1, 3, 1, 7, 2, 3, 9, 9],\n",
+            "        [3, 8, 5, 4, 5, 6, 5, 5, 3, 5, 8, 1, 9, 8, 4, 2, 2, 4, 2, 5, 7, 3, 7, 1,\n",
+            "         2, 4, 8, 2, 5, 6, 7, 2],\n",
+            "        [5, 3, 2, 2, 1, 2, 7, 6, 2, 8, 9, 0, 1, 5, 3, 7, 7, 7, 1, 2, 2, 8, 2, 2,\n",
+            "         8, 7, 7, 0, 3, 2, 0, 5],\n",
+            "        [0, 1, 7, 3, 3, 8, 3, 3, 4, 3, 1, 2, 2, 0, 1, 1, 6, 2, 7, 3, 6, 5, 6, 9,\n",
+            "         9, 6, 2, 5, 7, 7, 2, 6]], device='cuda:0')\n",
+            "tensor([0, 3, 3, 9, 4, 8, 1, 7, 7, 9, 9, 4, 3, 6, 3, 2, 8, 2, 7, 6, 7, 0, 2, 1,\n",
+            "        2, 9, 4, 6, 9, 6, 1, 0], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[28.7188, 20.9688, 23.7031, 19.2344, 18.8281, 19.0938, 18.9375, 19.9219,\n",
+            "         22.8750, 19.8750],\n",
+            "        [17.4531, 19.1250, 21.3594, 25.6094, 19.7344, 23.2031, 20.3125, 21.7812,\n",
+            "         18.8281, 18.5625],\n",
+            "        [18.7344, 20.3906, 21.4688, 27.3750, 18.7812, 23.4062, 20.9062, 20.6094,\n",
+            "         19.8750, 18.8750],\n",
+            "        [19.2188, 23.0000, 19.9062, 18.5000, 18.5625, 19.3906, 18.2188, 20.5938,\n",
+            "         20.3594, 27.4844],\n",
+            "        [18.6562, 19.2500, 21.7500, 19.5000, 30.2656, 21.7969, 17.6406, 25.5000,\n",
+            "         17.5625, 17.9531],\n",
+            "        [19.0625, 20.5781, 18.9219, 18.2188, 17.9531, 19.1875, 16.7031, 18.7188,\n",
+            "         22.8750, 20.3438],\n",
+            "        [18.8438, 26.7969, 20.5625, 20.3750, 19.4844, 20.6406, 18.7344, 18.8438,\n",
+            "         20.2031, 21.7344],\n",
+            "        [19.6719, 20.4375, 20.9375, 20.2344, 22.2500, 21.6406, 16.6719, 28.0000,\n",
+            "         19.3438, 20.0469],\n",
+            "        [18.2969, 20.2656, 21.9844, 20.2812, 21.6094, 21.7500, 17.6562, 28.0625,\n",
+            "         18.0000, 18.8125],\n",
+            "        [18.3594, 23.3438, 19.8750, 18.3750, 19.3281, 19.5312, 19.5625, 19.0312,\n",
+            "         18.6875, 27.4062],\n",
+            "        [17.3750, 22.3438, 19.7344, 17.5625, 17.4844, 18.2969, 17.2344, 18.9688,\n",
+            "         19.2812, 27.0156],\n",
+            "        [18.3438, 19.1875, 20.3438, 19.8125, 23.5312, 21.6719, 16.7031, 26.6250,\n",
+            "         18.4844, 19.1875],\n",
+            "        [19.3750, 20.0781, 22.5469, 25.4531, 22.6875, 23.8750, 24.6094, 22.1250,\n",
+            "         21.4062, 19.7969],\n",
+            "        [18.5156, 18.8125, 20.4531, 19.5156, 19.6406, 19.7500, 23.2812, 19.3906,\n",
+            "         20.4688, 17.9531],\n",
+            "        [20.6406, 21.0781, 22.2656, 27.2188, 22.8281, 24.4219, 22.3906, 21.5000,\n",
+            "         21.8438, 19.9844],\n",
+            "        [19.6719, 19.6406, 27.2656, 21.0156, 18.6406, 19.9062, 21.4688, 20.1562,\n",
+            "         18.3594, 18.0156],\n",
+            "        [19.3125, 19.5938, 19.1094, 17.5312, 17.5781, 17.8594, 17.7344, 18.5312,\n",
+            "         25.6719, 18.1875],\n",
+            "        [21.3594, 19.9531, 22.9375, 21.0938, 19.1562, 22.1875, 21.4844, 21.0625,\n",
+            "         20.3438, 19.4062],\n",
+            "        [21.1719, 21.3594, 24.8750, 24.0312, 25.0000, 26.4062, 19.5469, 29.1094,\n",
+            "         19.6719, 21.2969],\n",
+            "        [20.0781, 19.9219, 22.8438, 21.9219, 21.0469, 21.5312, 24.8125, 20.5938,\n",
+            "         19.5312, 18.3125],\n",
+            "        [16.8594, 18.8281, 19.0781, 19.7188, 21.3438, 20.8281, 14.7578, 26.7812,\n",
+            "         17.9062, 18.2812],\n",
+            "        [26.2812, 21.2656, 23.8438, 20.5469, 20.6094, 19.9375, 19.5938, 21.2031,\n",
+            "         23.5156, 19.8750],\n",
+            "        [20.6094, 20.2812, 27.2344, 22.1719, 20.7500, 22.7031, 22.5625, 22.9062,\n",
+            "         19.6250, 19.6406],\n",
+            "        [19.8750, 27.4844, 20.4531, 20.2812, 17.2656, 20.9688, 20.2812, 18.2031,\n",
+            "         20.4062, 23.3281],\n",
+            "        [22.9844, 22.6875, 29.3125, 22.4688, 20.1406, 22.0469, 23.1875, 21.9062,\n",
+            "         21.8594, 20.7344],\n",
+            "        [22.1250, 23.4844, 20.8906, 19.9531, 19.3906, 20.6875, 19.8125, 20.3594,\n",
+            "         22.8281, 22.6406],\n",
+            "        [20.7812, 20.2188, 23.2500, 21.9062, 29.9531, 24.7812, 20.6562, 23.5938,\n",
+            "         19.2812, 20.2344],\n",
+            "        [21.5156, 20.8906, 24.0000, 20.9688, 22.2188, 20.5312, 23.8750, 21.1250,\n",
+            "         20.0781, 18.1094],\n",
+            "        [18.8750, 22.2188, 20.0781, 20.3750, 19.5938, 20.7969, 19.2656, 20.8750,\n",
+            "         19.8281, 20.5000],\n",
+            "        [20.3750, 20.2188, 21.5938, 24.1250, 21.4531, 23.0938, 24.0781, 21.4219,\n",
+            "         20.3125, 19.3906],\n",
+            "        [19.1875, 26.7656, 19.4219, 18.6719, 17.8438, 19.9844, 18.9375, 18.0625,\n",
+            "         19.5000, 24.3438],\n",
+            "        [27.7031, 19.2344, 23.6719, 18.9375, 18.9375, 19.1250, 19.7500, 19.0312,\n",
+            "         21.5469, 18.2969]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[0],\n",
+            "        [3],\n",
+            "        [3],\n",
+            "        [9],\n",
+            "        [4],\n",
+            "        [8],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [3],\n",
+            "        [6],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [8],\n",
+            "        [2],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [4],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [1],\n",
+            "        [0]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[0, 3, 3, 9, 4, 8, 1, 7, 7, 9, 9, 7, 3, 6, 3, 2, 8, 2, 7, 6, 7, 0, 2, 1,\n",
+            "         2, 1, 4, 2, 1, 3, 1, 0],\n",
+            "        [2, 5, 5, 1, 7, 1, 9, 4, 2, 1, 1, 4, 6, 8, 5, 6, 1, 5, 5, 2, 4, 2, 7, 9,\n",
+            "         6, 8, 5, 6, 7, 6, 9, 2],\n",
+            "        [8, 7, 2, 7, 5, 9, 5, 5, 5, 2, 2, 5, 5, 2, 4, 3, 0, 6, 4, 3, 5, 8, 5, 5,\n",
+            "         0, 9, 7, 4, 5, 5, 5, 8],\n",
+            "        [1, 2, 6, 8, 2, 5, 2, 2, 4, 6, 8, 2, 4, 5, 6, 7, 2, 0, 2, 5, 3, 1, 6, 2,\n",
+            "         1, 0, 2, 0, 9, 2, 8, 6],\n",
+            "        [7, 6, 7, 2, 3, 0, 3, 1, 3, 5, 7, 3, 2, 4, 2, 5, 7, 3, 3, 4, 2, 7, 3, 8,\n",
+            "         3, 2, 3, 7, 3, 4, 2, 1]], device='cuda:0')\n",
+            "tensor([1, 8, 7, 0, 0, 4, 7, 4, 2, 6, 9, 5, 9, 0, 7, 4, 5, 8, 1, 4, 7, 9, 9, 8,\n",
+            "        8, 6, 3, 7, 0, 8, 9, 6], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[18.3906, 25.4531, 19.9375, 18.6094, 18.4531, 19.1875, 17.0469, 18.3594,\n",
+            "         19.7656, 21.5781],\n",
+            "        [20.9375, 20.4219, 21.6562, 19.1875, 20.3750, 20.6562, 19.4375, 19.6562,\n",
+            "         24.4844, 20.8438],\n",
+            "        [18.0781, 20.1719, 20.9375, 20.9688, 22.0938, 21.4219, 16.8438, 29.2344,\n",
+            "         18.3438, 19.0469],\n",
+            "        [26.6250, 23.5625, 22.5625, 20.1406, 19.5312, 20.9844, 19.6875, 20.9219,\n",
+            "         24.3906, 24.3906],\n",
+            "        [26.6562, 20.4219, 22.6875, 19.4062, 20.8750, 20.1406, 19.7969, 20.1875,\n",
+            "         22.3438, 19.0938],\n",
+            "        [20.1406, 20.2344, 23.4688, 21.1562, 30.8750, 22.5312, 19.1406, 24.5469,\n",
+            "         18.8750, 19.3594],\n",
+            "        [18.0938, 19.0625, 20.0469, 19.4844, 19.4844, 20.3125, 15.0938, 28.0000,\n",
+            "         19.0156, 18.4219],\n",
+            "        [20.3438, 20.6094, 21.7031, 20.1562, 28.0000, 21.8750, 17.3906, 23.9219,\n",
+            "         19.3906, 19.2188],\n",
+            "        [22.0938, 20.5156, 25.8281, 22.1875, 23.1250, 23.4844, 23.5469, 23.7344,\n",
+            "         21.5781, 20.0625],\n",
+            "        [18.3281, 18.5312, 22.2188, 21.2500, 24.2188, 20.8438, 23.2188, 20.3906,\n",
+            "         18.7812, 19.0000],\n",
+            "        [20.0938, 22.4688, 20.3906, 19.5938, 18.3438, 19.1406, 17.8750, 19.9219,\n",
+            "         21.6250, 25.2344],\n",
+            "        [18.6250, 19.6094, 21.2188, 22.6562, 22.4375, 27.1562, 20.0000, 23.4531,\n",
+            "         18.8750, 19.0469],\n",
+            "        [20.2188, 24.2344, 21.7031, 19.6250, 20.4062, 19.9219, 17.9062, 21.9219,\n",
+            "         21.8438, 25.1719],\n",
+            "        [25.9531, 25.2500, 23.0000, 22.7656, 21.5625, 22.9219, 22.1875, 23.4062,\n",
+            "         26.1719, 23.2969],\n",
+            "        [16.7656, 19.3594, 19.2656, 20.8438, 21.8594, 22.6406, 15.7578, 28.4844,\n",
+            "         17.8750, 18.2188],\n",
+            "        [18.7969, 18.4531, 21.8750, 20.6094, 28.6406, 21.5781, 19.7344, 22.0156,\n",
+            "         17.8281, 17.8438],\n",
+            "        [20.0156, 20.2812, 21.4375, 22.9844, 20.0156, 27.5625, 20.8281, 21.4688,\n",
+            "         19.8906, 19.1250],\n",
+            "        [21.2500, 20.9219, 21.1406, 18.9062, 19.0469, 19.7188, 17.8281, 20.0312,\n",
+            "         27.9688, 19.8438],\n",
+            "        [19.6406, 26.2812, 20.2969, 19.9688, 18.8906, 19.6250, 18.7812, 20.5469,\n",
+            "         19.7344, 21.4062],\n",
+            "        [20.3281, 19.6406, 22.8594, 18.6875, 27.2500, 19.9688, 19.2500, 20.7031,\n",
+            "         18.9062, 19.4844],\n",
+            "        [17.7188, 18.8750, 20.6562, 20.3750, 22.0469, 20.9375, 17.7656, 27.7500,\n",
+            "         18.1719, 18.2188],\n",
+            "        [19.8125, 23.1094, 19.3594, 17.7031, 18.3594, 18.2500, 17.5156, 19.4688,\n",
+            "         20.7188, 24.8125],\n",
+            "        [19.0781, 22.5000, 19.0938, 18.5469, 19.0156, 18.0938, 17.5156, 18.4844,\n",
+            "         20.8750, 26.3750],\n",
+            "        [21.7344, 21.5000, 20.7656, 18.8125, 19.0156, 18.9531, 17.9844, 19.2812,\n",
+            "         28.8125, 20.9531],\n",
+            "        [19.1250, 19.8906, 18.2188, 18.4688, 17.1250, 18.8125, 16.7656, 16.5312,\n",
+            "         25.4688, 18.1250],\n",
+            "        [19.8906, 20.6250, 21.3281, 21.5625, 21.0938, 21.9375, 24.2812, 21.6562,\n",
+            "         19.4844, 19.2188],\n",
+            "        [18.0000, 19.5156, 21.7812, 26.6719, 24.4219, 26.6562, 20.5000, 22.2969,\n",
+            "         19.4844, 18.3594],\n",
+            "        [17.1719, 21.4062, 20.2656, 19.6250, 21.3906, 21.8594, 16.3906, 27.5156,\n",
+            "         19.6406, 19.3281],\n",
+            "        [30.5469, 22.7031, 27.0625, 21.9844, 22.3438, 22.4219, 22.3125, 22.1719,\n",
+            "         24.2812, 22.5000],\n",
+            "        [20.3438, 20.5000, 20.8906, 19.6406, 19.2812, 20.2188, 18.7031, 19.2656,\n",
+            "         24.0312, 19.2812],\n",
+            "        [17.6875, 23.0469, 18.7188, 17.3281, 17.7656, 18.0781, 18.4844, 18.2500,\n",
+            "         18.4219, 26.0312],\n",
+            "        [20.4844, 22.8438, 22.6719, 21.7344, 19.5156, 22.2656, 28.9844, 20.4375,\n",
+            "         21.9062, 21.5625]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[1],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [0],\n",
+            "        [4],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [5],\n",
+            "        [8],\n",
+            "        [1],\n",
+            "        [4],\n",
+            "        [7],\n",
+            "        [9],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [6],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [6]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[1, 8, 7, 0, 0, 4, 7, 4, 2, 4, 9, 5, 9, 8, 7, 4, 5, 8, 1, 4, 7, 9, 9, 8,\n",
+            "         8, 6, 3, 7, 0, 8, 9, 6],\n",
+            "        [9, 2, 4, 9, 2, 7, 5, 7, 7, 6, 1, 7, 1, 0, 5, 7, 3, 0, 9, 2, 4, 1, 1, 0,\n",
+            "         1, 5, 5, 5, 2, 2, 1, 1],\n",
+            "        [2, 0, 5, 8, 8, 2, 2, 5, 6, 2, 8, 3, 7, 1, 4, 2, 7, 2, 7, 7, 5, 8, 8, 1,\n",
+            "         0, 7, 4, 1, 8, 1, 2, 2],\n",
+            "        [8, 9, 3, 1, 4, 5, 4, 2, 5, 3, 2, 4, 8, 7, 3, 5, 2, 1, 2, 0, 2, 0, 2, 9,\n",
+            "         5, 3, 7, 4, 1, 0, 6, 5],\n",
+            "        [5, 5, 2, 2, 1, 3, 3, 1, 4, 5, 0, 2, 2, 9, 1, 3, 6, 7, 3, 5, 3, 7, 0, 2,\n",
+            "         3, 2, 2, 2, 9, 5, 8, 8]], device='cuda:0')\n",
+            "tensor([2, 4, 6, 2, 9, 7, 4, 6, 8, 5, 6, 1, 3, 5, 9, 9, 1, 3, 2, 0, 3, 0, 2, 0,\n",
+            "        7, 3, 9, 3, 5, 7, 6, 5], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[21.1719, 19.6094, 27.3594, 21.1562, 21.3125, 20.7969, 21.3750, 21.2812,\n",
+            "         19.8906, 18.8750],\n",
+            "        [19.3906, 20.1094, 23.1562, 23.9531, 27.5938, 26.0781, 21.9375, 22.7500,\n",
+            "         19.1250, 19.1562],\n",
+            "        [18.2188, 20.0781, 22.1406, 27.1250, 20.8750, 24.3125, 23.1094, 21.7969,\n",
+            "         20.1719, 18.5938],\n",
+            "        [20.2188, 19.2969, 28.3594, 21.5312, 19.2969, 21.2969, 21.3594, 20.3125,\n",
+            "         18.7656, 19.1094],\n",
+            "        [18.7500, 24.2344, 20.5312, 19.5781, 20.0156, 20.2812, 18.9844, 20.0938,\n",
+            "         20.6562, 27.7812],\n",
+            "        [19.5312, 19.5312, 21.6719, 20.7812, 21.5781, 21.6875, 16.8750, 28.8125,\n",
+            "         18.7344, 19.6094],\n",
+            "        [20.0938, 21.4844, 21.9375, 21.5781, 24.7969, 23.4688, 19.3750, 28.7812,\n",
+            "         19.2031, 20.7656],\n",
+            "        [20.0469, 21.7031, 23.8594, 22.3750, 20.9219, 23.5625, 27.8281, 21.0625,\n",
+            "         20.0000, 20.2344],\n",
+            "        [21.4531, 20.2344, 21.4375, 19.9062, 18.0156, 20.1250, 19.0938, 20.2656,\n",
+            "         26.7656, 17.8125],\n",
+            "        [19.3906, 19.4844, 20.7188, 23.3906, 21.7344, 26.5781, 20.5625, 22.3438,\n",
+            "         19.5000, 18.9219],\n",
+            "        [18.7031, 19.0312, 22.8438, 22.5469, 22.4531, 21.8906, 25.7969, 19.3750,\n",
+            "         18.4844, 17.9531],\n",
+            "        [18.5469, 25.0156, 19.1094, 17.4844, 17.3906, 18.6719, 18.0156, 18.2656,\n",
+            "         19.5156, 22.7344],\n",
+            "        [20.0469, 20.6562, 21.8906, 25.5781, 22.0625, 21.9688, 22.5469, 21.0000,\n",
+            "         20.7188, 19.3906],\n",
+            "        [21.3438, 21.2344, 22.5156, 22.8438, 19.7812, 26.5312, 23.5469, 22.8750,\n",
+            "         21.1250, 20.3594],\n",
+            "        [19.1406, 23.2344, 19.7031, 17.3750, 17.9844, 19.1406, 17.2500, 19.0469,\n",
+            "         20.1094, 27.9062],\n",
+            "        [17.5312, 23.8750, 17.9375, 17.1406, 16.1406, 17.7344, 17.5781, 17.3125,\n",
+            "         16.1094, 24.0625],\n",
+            "        [20.0938, 25.7188, 20.7500, 19.4062, 19.3438, 20.7656, 19.8281, 18.5625,\n",
+            "         19.3281, 24.3125],\n",
+            "        [19.4688, 19.6875, 21.5156, 26.8750, 20.1719, 23.2188, 20.7344, 20.8750,\n",
+            "         20.3125, 18.4375],\n",
+            "        [20.2500, 21.4219, 27.3125, 23.3594, 23.0312, 22.9531, 22.0312, 24.5469,\n",
+            "         21.1562, 20.7969],\n",
+            "        [27.1250, 19.1250, 24.4062, 17.6250, 17.9688, 18.9375, 17.2344, 18.5469,\n",
+            "         21.0000, 18.0000],\n",
+            "        [19.3906, 19.6094, 20.7188, 27.3594, 18.7188, 22.4844, 19.4062, 20.8438,\n",
+            "         20.4531, 18.5938],\n",
+            "        [27.9688, 21.5469, 24.4062, 20.5156, 20.9062, 21.3125, 21.7812, 22.4219,\n",
+            "         23.2344, 21.0781],\n",
+            "        [21.4531, 20.7812, 27.1250, 22.4531, 22.4062, 23.0312, 25.3750, 21.3594,\n",
+            "         20.5469, 19.6562],\n",
+            "        [28.1875, 21.7188, 24.4062, 22.0156, 21.7656, 21.9062, 21.5938, 21.6719,\n",
+            "         22.8906, 19.7656],\n",
+            "        [20.6875, 20.5469, 21.9219, 21.6406, 22.3906, 21.8281, 18.3125, 29.1250,\n",
+            "         19.8594, 19.5469],\n",
+            "        [20.2344, 21.3281, 22.2344, 27.0312, 20.3906, 25.1250, 21.4219, 22.8281,\n",
+            "         20.8438, 20.4531],\n",
+            "        [17.8750, 23.1562, 19.2031, 17.3281, 17.7500, 18.6875, 16.5938, 18.5000,\n",
+            "         19.3438, 27.4062],\n",
+            "        [18.2812, 18.3281, 22.6250, 24.7969, 20.9844, 21.3906, 22.9062, 20.1094,\n",
+            "         18.3906, 17.7031],\n",
+            "        [19.0469, 19.3750, 20.3594, 21.0312, 21.1562, 26.5156, 19.3594, 21.0156,\n",
+            "         18.3750, 19.3594],\n",
+            "        [19.5312, 21.6250, 21.9688, 20.7500, 20.4062, 21.7656, 17.0625, 28.9688,\n",
+            "         20.4375, 21.0156],\n",
+            "        [20.0156, 21.5156, 24.9844, 23.2344, 20.8594, 23.3125, 29.6562, 21.9844,\n",
+            "         21.1250, 20.3438],\n",
+            "        [19.8125, 19.9375, 21.2188, 21.2969, 19.1875, 27.0781, 22.0938, 21.3906,\n",
+            "         19.1562, 19.3125]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[2],\n",
+            "        [4],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [8],\n",
+            "        [5],\n",
+            "        [6],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [9],\n",
+            "        [1],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [0],\n",
+            "        [3],\n",
+            "        [0],\n",
+            "        [2],\n",
+            "        [0],\n",
+            "        [7],\n",
+            "        [3],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [5],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [5]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[2, 4, 3, 2, 9, 7, 7, 6, 8, 5, 6, 1, 3, 5, 9, 9, 1, 3, 2, 0, 3, 0, 2, 0,\n",
+            "         7, 3, 9, 3, 5, 7, 6, 5],\n",
+            "        [6, 5, 5, 3, 1, 5, 4, 2, 0, 3, 2, 9, 6, 6, 1, 1, 9, 5, 7, 2, 5, 2, 6, 2,\n",
+            "         4, 5, 1, 6, 4, 2, 2, 6],\n",
+            "        [4, 3, 6, 6, 8, 2, 5, 5, 2, 7, 3, 8, 4, 7, 8, 2, 5, 2, 3, 8, 7, 8, 5, 8,\n",
+            "         2, 7, 8, 2, 3, 5, 5, 7],\n",
+            "        [7, 2, 2, 5, 2, 4, 2, 3, 7, 4, 4, 2, 5, 3, 2, 5, 2, 7, 4, 1, 2, 7, 3, 3,\n",
+            "         5, 2, 2, 5, 7, 1, 3, 3],\n",
+            "        [0, 7, 7, 7, 5, 3, 3, 1, 1, 2, 5, 5, 2, 2, 0, 6, 0, 6, 5, 5, 8, 6, 4, 5,\n",
+            "         3, 6, 5, 4, 2, 9, 7, 2]], device='cuda:0')\n",
+            "tensor([9, 5, 6, 1, 2, 4, 0, 2, 7, 8, 4, 4, 0, 9, 5, 9, 2, 9, 3, 2, 4, 3, 2, 2,\n",
+            "        8, 8, 6, 8, 1, 6, 8, 9], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[18.9844, 22.3125, 20.4219, 21.1562, 18.6406, 19.0000, 18.2500, 20.2188,\n",
+            "         20.2500, 24.2031],\n",
+            "        [19.5625, 18.5156, 20.5781, 20.9375, 18.2969, 25.9688, 19.5938, 20.3906,\n",
+            "         19.0312, 18.8438],\n",
+            "        [19.9531, 20.8906, 23.2188, 21.6250, 21.1562, 22.0469, 27.2656, 21.7031,\n",
+            "         21.2031, 20.3594],\n",
+            "        [17.8594, 25.3594, 18.4531, 17.2500, 17.3750, 18.0469, 19.8438, 17.1562,\n",
+            "         17.6719, 19.8594],\n",
+            "        [21.1250, 20.0156, 28.5469, 21.5469, 20.4688, 21.1094, 22.3281, 20.7344,\n",
+            "         19.7969, 19.7188],\n",
+            "        [21.7031, 22.3594, 22.6250, 23.1094, 32.3438, 24.5469, 22.4062, 26.1250,\n",
+            "         20.1406, 22.4531],\n",
+            "        [26.6875, 19.4219, 26.4062, 20.4062, 20.8906, 20.9062, 20.5000, 20.8594,\n",
+            "         22.5625, 20.3594],\n",
+            "        [20.4844, 20.6250, 27.2656, 22.4688, 24.0938, 22.5156, 22.9844, 22.5312,\n",
+            "         20.7656, 19.5938],\n",
+            "        [18.3281, 19.7969, 19.7969, 19.8125, 19.2969, 21.3125, 15.2188, 27.7500,\n",
+            "         18.2969, 19.5469],\n",
+            "        [21.8750, 20.3438, 21.1094, 19.7031, 19.0469, 19.2656, 18.5000, 19.3906,\n",
+            "         24.4688, 19.3906],\n",
+            "        [19.8281, 20.8906, 23.0000, 22.9844, 29.4844, 23.4844, 21.2188, 24.3125,\n",
+            "         19.5156, 19.7344],\n",
+            "        [18.1562, 18.6875, 22.4375, 19.5625, 28.3750, 21.8438, 18.8438, 25.0781,\n",
+            "         17.5625, 18.2812],\n",
+            "        [28.0781, 20.7812, 24.7031, 19.8594, 20.1406, 20.6406, 20.2344, 20.7500,\n",
+            "         22.4531, 20.1250],\n",
+            "        [20.7188, 23.7969, 21.8125, 21.6719, 20.4219, 21.2344, 20.4688, 22.0469,\n",
+            "         22.9375, 26.3281],\n",
+            "        [21.9531, 20.8906, 22.3750, 23.5312, 23.7188, 27.5469, 21.8125, 25.0469,\n",
+            "         20.7188, 21.5156],\n",
+            "        [16.9688, 21.7188, 18.2812, 17.4688, 17.3281, 18.0312, 16.4375, 19.5000,\n",
+            "         18.7969, 25.7344],\n",
+            "        [25.6250, 21.1406, 24.8438, 21.3281, 23.3438, 21.4844, 22.3125, 22.2969,\n",
+            "         23.5156, 20.8125],\n",
+            "        [18.9844, 21.9375, 20.0156, 19.5938, 16.8281, 18.2969, 18.6875, 18.4219,\n",
+            "         19.9375, 24.5312],\n",
+            "        [19.4375, 20.2500, 21.5938, 27.4062, 20.3750, 23.9688, 20.6250, 21.8125,\n",
+            "         19.9219, 19.1406],\n",
+            "        [18.9688, 17.9531, 26.5469, 20.7344, 19.7969, 20.2500, 21.9062, 19.1719,\n",
+            "         18.0469, 18.2188],\n",
+            "        [19.1094, 19.5000, 22.5000, 22.9688, 28.0469, 23.9375, 22.1250, 23.4219,\n",
+            "         18.7812, 18.7969],\n",
+            "        [20.1562, 20.8281, 23.9531, 25.9062, 22.1719, 23.6719, 23.2031, 21.7656,\n",
+            "         20.6250, 19.2188],\n",
+            "        [22.0312, 19.5000, 27.4062, 20.7500, 20.9531, 21.9844, 23.0469, 20.9688,\n",
+            "         20.7031, 19.4844],\n",
+            "        [19.2188, 17.7188, 27.7188, 19.7969, 19.3750, 19.7500, 20.9531, 18.6406,\n",
+            "         17.6094, 17.3281],\n",
+            "        [20.3438, 20.3594, 19.1094, 17.8281, 16.9375, 18.5938, 15.8047, 18.6250,\n",
+            "         26.5625, 20.3125],\n",
+            "        [20.7969, 20.2344, 18.9219, 19.1562, 18.5156, 18.6562, 17.2812, 18.3125,\n",
+            "         25.0938, 19.1719],\n",
+            "        [20.1250, 21.2344, 23.2500, 22.6719, 21.5938, 22.0938, 25.7656, 22.6094,\n",
+            "         22.2188, 19.7031],\n",
+            "        [21.5312, 19.1406, 20.8906, 20.8438, 20.6250, 21.0625, 20.8281, 20.2031,\n",
+            "         23.3125, 18.3750],\n",
+            "        [17.9062, 24.7188, 19.0000, 18.0000, 16.2656, 19.7969, 18.5156, 15.6953,\n",
+            "         19.0938, 20.5469],\n",
+            "        [18.5000, 20.0000, 23.1094, 24.6719, 21.2812, 23.5156, 27.4375, 21.8750,\n",
+            "         20.0469, 19.0781],\n",
+            "        [22.9062, 22.3594, 22.5000, 20.0000, 20.8750, 20.1719, 20.4531, 20.1094,\n",
+            "         25.2969, 20.8438],\n",
+            "        [22.1094, 22.7344, 21.9062, 20.0781, 19.5000, 19.7031, 20.5625, 20.8125,\n",
+            "         22.8125, 24.7656]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[9],\n",
+            "        [5],\n",
+            "        [6],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [2],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [4],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [3],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [6],\n",
+            "        [8],\n",
+            "        [1],\n",
+            "        [6],\n",
+            "        [8],\n",
+            "        [9]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[9, 5, 6, 1, 2, 4, 0, 2, 7, 8, 4, 4, 0, 9, 5, 9, 0, 9, 3, 2, 4, 3, 2, 2,\n",
+            "         8, 8, 6, 8, 1, 6, 8, 9],\n",
+            "        [1, 3, 2, 9, 6, 7, 2, 4, 5, 0, 7, 7, 2, 1, 7, 1, 2, 1, 5, 6, 5, 2, 6, 6,\n",
+            "         1, 0, 2, 0, 9, 3, 0, 8],\n",
+            "        [3, 2, 5, 6, 3, 5, 8, 6, 3, 2, 5, 2, 8, 8, 4, 7, 8, 2, 7, 3, 7, 5, 0, 3,\n",
+            "         0, 1, 3, 5, 5, 5, 2, 1],\n",
+            "        [2, 7, 7, 2, 0, 3, 5, 7, 2, 1, 2, 5, 1, 7, 3, 8, 4, 8, 2, 5, 3, 6, 5, 5,\n",
+            "         9, 9, 7, 2, 8, 2, 1, 0],\n",
+            "        [8, 6, 3, 5, 5, 2, 4, 5, 1, 3, 3, 3, 7, 2, 2, 2, 6, 3, 6, 4, 2, 4, 7, 4,\n",
+            "         2, 3, 8, 3, 2, 7, 4, 2]], device='cuda:0')\n",
+            "tensor([8, 2, 1, 4, 9, 1, 5, 7, 1, 6, 0, 1, 5, 2, 8, 1, 1, 3, 8, 1, 3, 1, 8, 5,\n",
+            "        1, 9, 0, 3, 0, 4, 0, 5], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[21.1562, 24.0938, 20.4219, 20.7656, 19.6250, 20.2188, 20.0312, 19.4688,\n",
+            "         23.7969, 23.5938],\n",
+            "        [21.0938, 20.2500, 23.9219, 21.4375, 20.5938, 21.9062, 21.4688, 23.5469,\n",
+            "         21.0625, 19.5781],\n",
+            "        [19.8125, 23.2812, 19.6719, 18.3750, 19.2500, 19.0469, 19.4219, 18.7500,\n",
+            "         19.2188, 21.3594],\n",
+            "        [23.0781, 22.3750, 26.0469, 25.5625, 28.1562, 26.8906, 25.1250, 27.4688,\n",
+            "         21.7031, 21.4844],\n",
+            "        [17.9375, 21.7344, 18.1250, 17.0469, 17.7500, 17.5000, 15.0625, 18.1406,\n",
+            "         19.9688, 24.8750],\n",
+            "        [18.9062, 25.5625, 19.5469, 18.6406, 19.3594, 19.6094, 18.3125, 18.1406,\n",
+            "         19.3438, 22.4062],\n",
+            "        [20.0469, 20.2344, 21.4844, 22.2188, 18.7031, 27.5938, 21.0938, 21.1719,\n",
+            "         19.0469, 18.8906],\n",
+            "        [19.2656, 20.2969, 20.5312, 19.8594, 22.4219, 21.8281, 16.2969, 28.1875,\n",
+            "         18.9375, 19.5781],\n",
+            "        [17.8125, 24.9375, 19.2188, 18.3281, 16.8906, 19.2500, 17.2500, 17.5312,\n",
+            "         18.3750, 23.7812],\n",
+            "        [22.5000, 21.7344, 24.1250, 23.8281, 22.1719, 23.6875, 26.0000, 22.0938,\n",
+            "         24.3594, 21.1406],\n",
+            "        [27.2812, 19.8125, 23.9062, 18.7969, 18.5938, 18.3125, 19.5469, 19.4219,\n",
+            "         22.3281, 18.5156],\n",
+            "        [18.3906, 26.8281, 20.7188, 18.6719, 17.4062, 20.1094, 17.7500, 18.7656,\n",
+            "         19.6406, 25.7969],\n",
+            "        [20.4688, 21.4844, 22.7812, 23.5781, 21.3750, 28.6562, 21.8906, 23.0781,\n",
+            "         19.6719, 20.6406],\n",
+            "        [25.9844, 20.5938, 28.7500, 20.6406, 21.0469, 21.0781, 21.7344, 21.2344,\n",
+            "         22.7188, 21.0625],\n",
+            "        [22.0625, 24.7031, 21.3594, 19.0625, 18.2969, 20.4219, 20.0469, 19.0781,\n",
+            "         25.4062, 22.3281],\n",
+            "        [16.9531, 25.6719, 19.3125, 17.6719, 17.3281, 18.4844, 18.4375, 16.9219,\n",
+            "         17.9062, 20.2500],\n",
+            "        [19.7812, 26.9531, 20.4531, 19.9531, 17.7500, 19.8438, 20.1406, 19.9219,\n",
+            "         21.3750, 26.5625],\n",
+            "        [20.6562, 21.6875, 23.2812, 23.7656, 22.4844, 24.0938, 24.0000, 22.1250,\n",
+            "         21.5938, 19.7188],\n",
+            "        [19.8750, 21.4219, 20.2188, 18.7188, 18.1406, 20.1719, 19.2969, 18.6719,\n",
+            "         23.0625, 18.9531],\n",
+            "        [17.9844, 25.0781, 19.5469, 18.9219, 18.2969, 20.2188, 18.7812, 18.0938,\n",
+            "         18.3906, 19.8438],\n",
+            "        [20.6719, 22.7500, 21.2500, 21.4844, 19.1094, 21.0625, 21.9062, 20.6719,\n",
+            "         23.0938, 22.0156],\n",
+            "        [19.9844, 24.7500, 20.1719, 17.8438, 18.7031, 18.7344, 18.6094, 18.1094,\n",
+            "         19.1250, 19.8438],\n",
+            "        [19.6719, 19.3125, 19.8438, 18.2500, 17.1562, 18.0469, 15.9453, 19.0938,\n",
+            "         26.5156, 18.1875],\n",
+            "        [20.8750, 20.9219, 22.5469, 22.7812, 22.7188, 28.0156, 21.0156, 23.1406,\n",
+            "         19.8438, 20.8281],\n",
+            "        [21.4375, 28.3125, 22.4688, 20.1562, 18.8438, 20.1719, 20.6719, 19.7656,\n",
+            "         20.7500, 24.8125],\n",
+            "        [17.4375, 24.9062, 19.3906, 17.9062, 17.7969, 19.0781, 18.4062, 18.5469,\n",
+            "         18.3594, 25.0625],\n",
+            "        [26.5938, 22.3906, 23.3125, 20.5000, 20.4688, 20.3594, 19.9688, 20.7812,\n",
+            "         24.5781, 21.9844],\n",
+            "        [20.8594, 20.6250, 22.5625, 26.8750, 20.8281, 23.5469, 22.5000, 21.3281,\n",
+            "         21.3906, 19.6250],\n",
+            "        [22.0156, 16.9375, 19.6406, 15.7578, 16.7812, 16.9219, 18.5938, 15.7422,\n",
+            "         18.2188, 13.7031],\n",
+            "        [18.4531, 18.1875, 21.0312, 19.8906, 26.1250, 21.1406, 19.3594, 22.3438,\n",
+            "         18.3281, 17.1406],\n",
+            "        [25.5156, 19.2188, 22.4531, 19.0938, 20.2188, 19.5625, 20.1250, 18.8594,\n",
+            "         22.3906, 18.7969],\n",
+            "        [19.9219, 19.4062, 21.7812, 22.7344, 21.3125, 26.9688, 21.5312, 22.4531,\n",
+            "         19.7656, 19.5625]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[1],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [4],\n",
+            "        [9],\n",
+            "        [1],\n",
+            "        [5],\n",
+            "        [7],\n",
+            "        [1],\n",
+            "        [6],\n",
+            "        [0],\n",
+            "        [1],\n",
+            "        [5],\n",
+            "        [2],\n",
+            "        [8],\n",
+            "        [1],\n",
+            "        [1],\n",
+            "        [5],\n",
+            "        [8],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [5],\n",
+            "        [1],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [3],\n",
+            "        [0],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [5]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[1, 2, 1, 4, 9, 1, 5, 7, 1, 6, 0, 1, 5, 2, 8, 1, 1, 5, 8, 1, 8, 1, 8, 5,\n",
+            "         1, 9, 0, 3, 0, 4, 0, 5],\n",
+            "        [8, 7, 9, 7, 1, 9, 3, 4, 9, 8, 2, 9, 3, 0, 1, 9, 9, 6, 1, 5, 1, 2, 2, 7,\n",
+            "         9, 1, 8, 5, 2, 7, 2, 3],\n",
+            "        [9, 5, 0, 5, 8, 5, 2, 5, 5, 2, 8, 2, 7, 8, 9, 2, 8, 3, 2, 9, 9, 0, 0, 3,\n",
+            "         2, 2, 2, 2, 6, 5, 8, 7],\n",
+            "        [0, 6, 2, 2, 7, 2, 7, 2, 2, 3, 1, 5, 2, 6, 0, 5, 2, 2, 5, 2, 6, 9, 1, 4,\n",
+            "         0, 5, 1, 6, 8, 2, 4, 2],\n",
+            "        [3, 3, 6, 3, 2, 4, 6, 1, 8, 5, 6, 8, 6, 7, 2, 6, 6, 4, 0, 3, 3, 8, 7, 2,\n",
+            "         8, 7, 9, 8, 1, 3, 6, 6]], device='cuda:0')\n",
+            "tensor([1, 2, 0, 4, 9, 5, 1, 2, 2, 7, 8, 7, 2, 4, 5, 3, 4, 0, 6, 1, 5, 9, 8, 0,\n",
+            "        2, 0, 7, 6, 0, 5, 9, 7], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[21.3906, 26.3750, 21.4375, 19.4219, 18.8594, 19.8594, 20.1562, 19.2188,\n",
+            "         19.7031, 24.0938],\n",
+            "        [22.4375, 20.1719, 29.3750, 25.5469, 25.5000, 26.3281, 23.5156, 24.2188,\n",
+            "         20.4375, 19.8750],\n",
+            "        [28.2500, 20.2812, 24.0469, 19.1406, 20.3125, 20.0312, 19.5625, 20.4531,\n",
+            "         23.1406, 20.2812],\n",
+            "        [18.5469, 18.8438, 22.2500, 22.2812, 28.9844, 22.2031, 20.6719, 20.3750,\n",
+            "         17.9062, 17.7812],\n",
+            "        [18.3125, 23.3906, 20.0000, 18.6562, 18.8125, 20.2656, 17.6875, 19.7188,\n",
+            "         19.7031, 26.2812],\n",
+            "        [19.8125, 19.7344, 21.3906, 21.8281, 22.5000, 25.7969, 24.6406, 21.2500,\n",
+            "         20.7500, 20.3281],\n",
+            "        [18.6719, 24.2031, 19.0625, 17.9375, 17.5625, 18.9219, 17.9844, 17.6562,\n",
+            "         19.2031, 22.0938],\n",
+            "        [20.5781, 20.1875, 25.8594, 20.4688, 23.0156, 22.0156, 22.5781, 23.0781,\n",
+            "         19.5156, 19.0312],\n",
+            "        [21.0469, 20.6094, 25.4844, 22.9688, 27.5469, 24.4219, 24.9219, 25.0781,\n",
+            "         21.0781, 20.2500],\n",
+            "        [18.8438, 20.0469, 21.9531, 20.7344, 23.0625, 21.9688, 17.6406, 29.2344,\n",
+            "         19.1094, 19.4531],\n",
+            "        [25.9375, 20.8438, 22.9062, 20.2188, 18.6875, 19.8594, 21.1250, 19.0156,\n",
+            "         25.6875, 19.0156],\n",
+            "        [18.6562, 20.2188, 20.5312, 20.6719, 21.9531, 21.3125, 17.9531, 27.5469,\n",
+            "         18.9219, 18.5781],\n",
+            "        [20.5781, 19.6562, 28.7500, 21.3906, 20.4062, 21.2188, 23.5469, 20.2031,\n",
+            "         20.4688, 19.1406],\n",
+            "        [19.4062, 20.7656, 23.5781, 20.7812, 25.5625, 21.7344, 19.1719, 24.0781,\n",
+            "         19.1875, 19.3438],\n",
+            "        [18.3594, 17.7969, 18.6562, 20.1250, 17.9375, 25.0625, 17.5000, 21.0938,\n",
+            "         18.3125, 17.1719],\n",
+            "        [19.9219, 20.3750, 22.5469, 26.2031, 20.4688, 22.8438, 20.2500, 21.5156,\n",
+            "         19.7031, 19.1250],\n",
+            "        [17.6719, 18.6406, 18.7188, 19.1094, 19.8750, 19.8594, 14.9766, 23.9844,\n",
+            "         17.5469, 18.8438],\n",
+            "        [28.8906, 20.5469, 25.5312, 18.9531, 19.3906, 19.9844, 18.5781, 19.5781,\n",
+            "         22.5625, 20.0156],\n",
+            "        [18.5000, 20.0938, 22.2812, 22.2969, 26.9844, 22.6719, 25.2500, 21.4844,\n",
+            "         18.1719, 18.1562],\n",
+            "        [18.8281, 25.2500, 19.9531, 19.4688, 19.9531, 19.4688, 18.6094, 18.5469,\n",
+            "         18.2812, 20.1250],\n",
+            "        [21.0312, 20.1250, 23.0781, 23.4219, 20.7656, 28.5625, 22.4531, 22.2500,\n",
+            "         20.1094, 20.1250],\n",
+            "        [18.8594, 22.3750, 20.4375, 19.4844, 17.8594, 19.1250, 18.9844, 18.8750,\n",
+            "         21.1875, 25.3281],\n",
+            "        [20.3594, 20.2656, 21.3438, 19.1875, 18.2031, 18.7812, 17.0156, 19.9062,\n",
+            "         27.5156, 19.4531],\n",
+            "        [29.4219, 22.0000, 25.6875, 20.6250, 21.0469, 21.6875, 20.7969, 20.8750,\n",
+            "         24.0938, 20.9219],\n",
+            "        [20.6875, 19.3750, 27.9219, 20.5625, 19.4375, 20.3125, 20.6562, 19.7500,\n",
+            "         19.2500, 19.0312],\n",
+            "        [25.1250, 19.7656, 24.0156, 21.2188, 19.6875, 20.0312, 21.0781, 20.0156,\n",
+            "         22.5781, 18.9062],\n",
+            "        [20.5156, 20.5625, 21.5469, 21.0938, 25.4062, 22.5156, 19.6250, 29.2344,\n",
+            "         19.5156, 19.9531],\n",
+            "        [18.6562, 20.2656, 22.8906, 21.8594, 20.0156, 22.2344, 28.2812, 20.0781,\n",
+            "         18.9375, 19.3125],\n",
+            "        [26.1875, 20.9219, 24.7188, 20.9219, 17.5469, 20.6562, 20.4062, 20.2188,\n",
+            "         22.3281, 18.6719],\n",
+            "        [18.3594, 19.1250, 23.2188, 22.4531, 22.2188, 23.0938, 24.6250, 20.8125,\n",
+            "         19.9531, 18.8438],\n",
+            "        [19.3906, 23.7031, 19.2812, 18.1094, 17.4844, 18.9062, 18.1406, 18.7188,\n",
+            "         21.2656, 24.3125],\n",
+            "        [19.4688, 20.7656, 21.2812, 19.1406, 22.1719, 20.1719, 17.2344, 27.3906,\n",
+            "         19.1094, 20.0469]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[1],\n",
+            "        [2],\n",
+            "        [0],\n",
+            "        [4],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [4],\n",
+            "        [5],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [4],\n",
+            "        [1],\n",
+            "        [5],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [0],\n",
+            "        [2],\n",
+            "        [0],\n",
+            "        [7],\n",
+            "        [6],\n",
+            "        [0],\n",
+            "        [6],\n",
+            "        [9],\n",
+            "        [7]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[1, 2, 0, 4, 9, 5, 1, 2, 4, 7, 0, 7, 2, 4, 5, 3, 7, 0, 4, 1, 5, 9, 8, 0,\n",
+            "         2, 0, 7, 6, 0, 6, 9, 7],\n",
+            "        [9, 5, 2, 3, 1, 6, 9, 7, 2, 4, 8, 4, 6, 7, 7, 5, 4, 2, 6, 9, 3, 1, 2, 2,\n",
+            "         0, 2, 4, 2, 2, 2, 1, 4],\n",
+            "        [2, 3, 8, 2, 5, 4, 8, 4, 7, 5, 2, 5, 3, 2, 3, 2, 5, 8, 5, 4, 2, 8, 0, 8,\n",
+            "         6, 8, 5, 5, 8, 5, 8, 2],\n",
+            "        [0, 4, 7, 5, 2, 3, 2, 6, 6, 2, 6, 3, 5, 5, 2, 7, 3, 1, 3, 2, 6, 2, 1, 1,\n",
+            "         3, 3, 2, 3, 3, 3, 0, 1],\n",
+            "        [6, 7, 4, 6, 7, 2, 5, 5, 5, 3, 1, 2, 0, 3, 0, 4, 9, 9, 2, 3, 7, 3, 7, 5,\n",
+            "         5, 6, 3, 1, 1, 4, 2, 5]], device='cuda:0')\n",
+            "tensor([5, 0, 4, 6, 0, 6, 3, 6, 8, 1, 1, 8, 9, 7, 2, 9, 0, 2, 2, 9, 0, 6, 5, 7,\n",
+            "        7, 9, 1, 7, 9, 8, 4, 5], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[20.4219, 20.1562, 21.3438, 21.5156, 19.9375, 26.7656, 20.9219, 21.5312,\n",
+            "         18.9688, 19.7500],\n",
+            "        [24.8281, 21.9844, 20.4844, 18.2812, 17.4688, 18.7656, 18.2500, 18.8438,\n",
+            "         26.1875, 20.6406],\n",
+            "        [22.4219, 20.9219, 24.5938, 23.7969, 27.5000, 24.6562, 23.1250, 26.0625,\n",
+            "         21.0000, 20.4375],\n",
+            "        [20.0000, 20.7188, 21.8594, 21.4219, 22.7500, 22.1406, 27.1406, 21.9531,\n",
+            "         19.3125, 18.6562],\n",
+            "        [24.6875, 20.4375, 22.5469, 20.8594, 20.2500, 20.3750, 20.1719, 20.5625,\n",
+            "         21.7500, 19.3594],\n",
+            "        [19.6875, 20.6406, 23.4219, 22.2969, 20.6406, 23.0312, 25.5312, 22.8438,\n",
+            "         20.9531, 18.5000],\n",
+            "        [20.3906, 19.4688, 24.5312, 23.1562, 19.5469, 21.9375, 21.1875, 20.6719,\n",
+            "         19.1875, 18.9062],\n",
+            "        [17.0625, 17.9375, 18.9688, 23.0938, 18.5938, 21.0312, 21.4375, 19.2031,\n",
+            "         17.9531, 16.8594],\n",
+            "        [18.7188, 17.2188, 16.9844, 16.3438, 16.5469, 16.3750, 14.7734, 18.0938,\n",
+            "         22.6875, 15.7891],\n",
+            "        [16.9375, 23.9219, 18.3906, 15.8594, 16.7812, 18.3594, 14.6172, 17.9688,\n",
+            "         17.3438, 21.7031],\n",
+            "        [21.1406, 25.5781, 21.1250, 19.6875, 19.5156, 21.0938, 19.4375, 20.6094,\n",
+            "         21.1875, 24.5469],\n",
+            "        [21.1562, 20.3750, 20.6094, 19.0625, 19.3594, 19.2969, 17.4062, 18.8281,\n",
+            "         25.8125, 21.1094],\n",
+            "        [18.5938, 21.8906, 19.6406, 18.1406, 17.1875, 19.0000, 17.6875, 19.3750,\n",
+            "         20.4844, 25.0625],\n",
+            "        [18.3281, 20.4844, 20.3750, 19.3594, 20.9688, 20.9844, 17.2500, 27.4844,\n",
+            "         18.8750, 19.5938],\n",
+            "        [21.3906, 22.0781, 27.4219, 26.2500, 26.3281, 24.0000, 25.8281, 23.7812,\n",
+            "         21.0156, 20.8125],\n",
+            "        [18.0156, 24.7344, 19.2656, 18.2188, 17.9531, 18.9531, 17.9219, 18.9688,\n",
+            "         18.3906, 27.7969],\n",
+            "        [26.3906, 22.2188, 22.1406, 20.4375, 20.7500, 20.0469, 19.8438, 21.8125,\n",
+            "         22.5938, 19.9062],\n",
+            "        [19.8281, 18.0000, 26.9688, 19.7656, 18.3750, 20.4531, 21.9375, 20.2344,\n",
+            "         18.6250, 19.1562],\n",
+            "        [19.9375, 19.0469, 26.0781, 19.6562, 17.7031, 19.1875, 20.8750, 19.1250,\n",
+            "         18.4062, 18.6094],\n",
+            "        [19.3281, 23.3125, 19.5156, 18.1562, 18.1562, 18.9062, 17.9531, 19.4375,\n",
+            "         20.5938, 26.3750],\n",
+            "        [28.5938, 23.3906, 25.9219, 22.1719, 23.0000, 22.5312, 23.0469, 23.6250,\n",
+            "         24.0156, 24.2656],\n",
+            "        [20.3281, 20.7188, 25.5625, 22.1562, 21.0312, 21.5938, 25.0000, 21.0781,\n",
+            "         19.5000, 19.8125],\n",
+            "        [19.8750, 20.8438, 23.0625, 24.3125, 21.8125, 27.6406, 21.4375, 24.4375,\n",
+            "         19.6719, 20.8125],\n",
+            "        [16.8281, 18.6250, 18.2031, 18.5156, 19.4219, 19.9531, 14.9688, 25.7969,\n",
+            "         17.4062, 18.1719],\n",
+            "        [18.2031, 18.9531, 18.6719, 20.0781, 19.7188, 21.3594, 17.3594, 23.8906,\n",
+            "         19.7344, 18.5938],\n",
+            "        [20.3750, 26.0469, 20.7031, 20.2969, 20.2188, 20.9688, 18.4219, 19.1250,\n",
+            "         19.7812, 26.0156],\n",
+            "        [15.8984, 24.9219, 18.5781, 17.3438, 17.5000, 18.5156, 17.9219, 16.5156,\n",
+            "         17.9531, 20.7656],\n",
+            "        [18.7812, 20.6562, 18.5000, 17.8281, 18.9531, 19.2344, 17.2969, 19.9219,\n",
+            "         19.7031, 20.2500],\n",
+            "        [18.2969, 22.6406, 18.7969, 16.9219, 16.9219, 18.1406, 18.8750, 17.1719,\n",
+            "         20.1094, 24.3125],\n",
+            "        [23.5781, 19.3906, 20.7969, 16.9219, 16.9375, 18.0156, 16.0312, 18.1875,\n",
+            "         27.2656, 17.8125],\n",
+            "        [18.7188, 19.1094, 21.6250, 20.5469, 28.6250, 22.2344, 20.0000, 23.2812,\n",
+            "         17.9531, 18.0000],\n",
+            "        [19.0312, 19.8438, 21.7344, 24.5312, 22.5156, 26.6562, 21.7188, 23.4531,\n",
+            "         19.2812, 18.9375]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[5],\n",
+            "        [8],\n",
+            "        [4],\n",
+            "        [6],\n",
+            "        [0],\n",
+            "        [6],\n",
+            "        [2],\n",
+            "        [3],\n",
+            "        [8],\n",
+            "        [1],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [0],\n",
+            "        [2],\n",
+            "        [5],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [1],\n",
+            "        [1],\n",
+            "        [1],\n",
+            "        [9],\n",
+            "        [8],\n",
+            "        [4],\n",
+            "        [5]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[5, 8, 4, 6, 0, 6, 2, 3, 8, 1, 1, 8, 9, 7, 2, 9, 0, 2, 2, 9, 0, 2, 5, 7,\n",
+            "         7, 1, 1, 1, 9, 8, 4, 5],\n",
+            "        [7, 0, 7, 4, 2, 2, 3, 6, 0, 9, 9, 0, 1, 5, 4, 1, 8, 6, 6, 1, 2, 6, 7, 5,\n",
+            "         5, 9, 9, 9, 1, 0, 7, 3],\n",
+            "        [3, 1, 5, 5, 8, 5, 5, 5, 7, 2, 8, 9, 8, 4, 3, 2, 1, 5, 0, 8, 9, 3, 3, 4,\n",
+            "         3, 5, 2, 7, 8, 2, 5, 7],\n",
+            "        [2, 9, 2, 7, 3, 7, 6, 7, 1, 5, 0, 2, 2, 1, 6, 7, 2, 7, 3, 2, 8, 5, 2, 1,\n",
+            "         8, 2, 5, 8, 6, 1, 2, 4],\n",
+            "        [6, 2, 3, 2, 7, 3, 7, 2, 2, 7, 2, 1, 7, 2, 5, 5, 7, 0, 5, 7, 7, 7, 4, 3,\n",
+            "         4, 0, 8, 5, 2, 7, 3, 2]], device='cuda:0')\n",
+            "tensor([0, 8, 0, 2, 5, 2, 1, 4, 4, 8, 9, 7, 8, 3, 6, 6, 0, 1, 1, 1, 8, 1, 4, 4,\n",
+            "        0, 7, 8, 2, 1, 2, 5, 4], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[22.7031, 18.9844, 21.9219, 18.0938, 18.3438, 18.7812, 20.5000, 19.1875,\n",
+            "         22.5156, 18.1250],\n",
+            "        [21.4688, 20.9062, 21.3125, 19.8594, 21.1406, 19.5000, 19.5312, 20.6250,\n",
+            "         27.6562, 21.2344],\n",
+            "        [26.7656, 20.5625, 23.2344, 18.7969, 19.0781, 19.1094, 16.3438, 20.0625,\n",
+            "         21.5312, 19.8281],\n",
+            "        [20.5625, 21.4375, 26.8438, 22.0000, 21.3438, 23.0000, 21.8594, 22.8281,\n",
+            "         20.1875, 21.1562],\n",
+            "        [22.4375, 23.3906, 20.5156, 22.1406, 20.7500, 24.5000, 19.9062, 21.8281,\n",
+            "         22.0156, 20.2500],\n",
+            "        [26.2812, 21.7812, 26.2188, 22.1719, 22.2344, 21.8125, 22.7344, 22.0625,\n",
+            "         22.1719, 21.1250],\n",
+            "        [19.4844, 24.6719, 20.5312, 16.9219, 15.5469, 17.5000, 20.4375, 19.6719,\n",
+            "         19.7969, 20.8125],\n",
+            "        [23.6719, 22.5938, 23.5000, 22.0625, 26.2344, 23.9219, 21.6562, 26.8906,\n",
+            "         21.8906, 22.3906],\n",
+            "        [21.7500, 20.9062, 23.8750, 21.6562, 28.6406, 23.3125, 20.0156, 25.2656,\n",
+            "         20.6719, 20.3125],\n",
+            "        [17.9844, 17.6250, 18.4062, 16.9219, 15.7656, 17.2969, 15.3750, 16.9844,\n",
+            "         25.0781, 16.0156],\n",
+            "        [19.2344, 22.3125, 20.4688, 18.4531, 17.7500, 18.7969, 17.5781, 19.2656,\n",
+            "         20.5781, 24.9375],\n",
+            "        [20.3281, 22.4062, 21.0469, 22.5938, 22.5312, 24.0781, 18.5781, 28.7969,\n",
+            "         21.9062, 20.9219],\n",
+            "        [23.0312, 23.0156, 22.4531, 19.7656, 20.1719, 20.3594, 19.8438, 20.3906,\n",
+            "         24.9844, 21.1094],\n",
+            "        [20.3750, 20.2656, 22.2656, 24.4219, 20.6406, 22.2031, 22.6562, 20.4219,\n",
+            "         20.5938, 19.0156],\n",
+            "        [19.8438, 19.2344, 21.1406, 21.8750, 21.9531, 21.5469, 22.2812, 21.1719,\n",
+            "         19.2188, 17.8594],\n",
+            "        [19.3281, 20.5000, 23.0000, 25.8750, 23.1719, 25.2500, 24.9062, 22.5156,\n",
+            "         20.1562, 19.2812],\n",
+            "        [28.8594, 21.8750, 26.3281, 20.2812, 21.5000, 21.1250, 20.4844, 21.8438,\n",
+            "         24.3594, 22.1719],\n",
+            "        [18.2656, 24.1406, 19.0781, 17.5312, 17.7344, 18.9375, 16.7188, 18.4688,\n",
+            "         18.9062, 24.1250],\n",
+            "        [20.1094, 26.8750, 21.8438, 20.3906, 20.0781, 21.0156, 20.3750, 19.2188,\n",
+            "         20.7031, 22.9375],\n",
+            "        [21.4688, 27.9375, 20.3125, 19.2500, 18.9688, 19.7812, 18.8438, 19.0938,\n",
+            "         20.9375, 23.1562],\n",
+            "        [20.2344, 19.2656, 20.5156, 17.8438, 19.8125, 19.4375, 18.8438, 18.6719,\n",
+            "         22.9531, 19.1094],\n",
+            "        [19.4375, 26.3438, 21.6094, 21.4375, 18.2188, 21.2188, 20.2188, 20.0469,\n",
+            "         19.5000, 20.8438],\n",
+            "        [18.8750, 20.1250, 20.9062, 20.0156, 22.7031, 21.9062, 18.1875, 27.9844,\n",
+            "         18.0625, 19.7188],\n",
+            "        [18.7969, 18.6875, 20.9219, 18.5156, 29.5000, 20.4219, 17.7031, 22.1094,\n",
+            "         17.8906, 18.1562],\n",
+            "        [29.9375, 24.0156, 23.9688, 21.8750, 21.4531, 22.3438, 20.0938, 22.8125,\n",
+            "         25.5781, 22.5312],\n",
+            "        [20.5312, 21.2500, 22.7969, 21.3750, 22.7188, 22.6250, 17.8281, 30.3906,\n",
+            "         20.0156, 20.7812],\n",
+            "        [18.8906, 21.0938, 18.7188, 18.1406, 16.4844, 18.2969, 16.1250, 17.5938,\n",
+            "         27.1406, 19.3438],\n",
+            "        [22.4688, 21.6875, 25.0625, 23.1562, 24.1250, 24.0938, 22.9844, 24.5938,\n",
+            "         21.1562, 21.0938],\n",
+            "        [18.1562, 25.5938, 19.6562, 18.2188, 18.5938, 18.2500, 18.8594, 17.8438,\n",
+            "         18.4219, 21.5781],\n",
+            "        [20.6406, 19.3125, 28.0000, 20.8750, 20.7812, 20.7500, 22.3594, 19.9844,\n",
+            "         19.6875, 19.2500],\n",
+            "        [19.8594, 21.5625, 20.8906, 21.6562, 20.7500, 27.0938, 21.0469, 23.4688,\n",
+            "         20.7188, 20.0625],\n",
+            "        [21.1406, 19.8438, 24.6406, 22.4062, 26.3906, 23.3125, 23.4219, 23.1250,\n",
+            "         21.4062, 19.6094]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[0],\n",
+            "        [8],\n",
+            "        [0],\n",
+            "        [2],\n",
+            "        [5],\n",
+            "        [0],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [8],\n",
+            "        [9],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [3],\n",
+            "        [6],\n",
+            "        [3],\n",
+            "        [0],\n",
+            "        [1],\n",
+            "        [1],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [7],\n",
+            "        [8],\n",
+            "        [2],\n",
+            "        [1],\n",
+            "        [2],\n",
+            "        [5],\n",
+            "        [4]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[0, 8, 0, 2, 5, 0, 1, 7, 4, 8, 9, 7, 8, 3, 6, 3, 0, 1, 1, 1, 8, 1, 7, 4,\n",
+            "         0, 7, 8, 2, 1, 2, 5, 4],\n",
+            "        [8, 0, 2, 5, 1, 2, 9, 4, 7, 2, 1, 5, 0, 6, 4, 5, 2, 9, 9, 9, 2, 2, 4, 7,\n",
+            "         8, 2, 1, 7, 9, 6, 7, 2],\n",
+            "        [2, 2, 8, 7, 0, 6, 2, 5, 2, 0, 8, 3, 1, 2, 3, 6, 8, 2, 2, 0, 0, 3, 5, 2,\n",
+            "         1, 4, 9, 4, 2, 3, 3, 6],\n",
+            "        [6, 9, 1, 3, 3, 4, 6, 0, 5, 1, 2, 4, 2, 5, 5, 4, 9, 5, 5, 8, 4, 5, 2, 5,\n",
+            "         2, 5, 0, 5, 6, 4, 1, 5],\n",
+            "        [7, 4, 7, 6, 8, 3, 8, 2, 0, 5, 7, 1, 9, 4, 7, 2, 1, 8, 8, 2, 5, 9, 1, 0,\n",
+            "         7, 3, 2, 3, 4, 5, 6, 7]], device='cuda:0')\n",
+            "tensor([6, 0, 5, 7, 4, 4, 3, 9, 5, 8, 8, 0, 8, 7, 4, 1, 8, 4, 9, 5, 4, 1, 7, 7,\n",
+            "        7, 7, 0, 3, 8, 3, 3, 0], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[18.2188, 19.0938, 22.5469, 21.1875, 21.3594, 22.2344, 27.2500, 19.8906,\n",
+            "         17.9844, 17.9688],\n",
+            "        [23.4375, 19.5312, 20.5000, 16.3438, 16.3125, 16.8281, 16.4844, 17.1719,\n",
+            "         19.2812, 17.7344],\n",
+            "        [18.8438, 18.2969, 19.9844, 20.3594, 19.2656, 25.7969, 19.3125, 20.5938,\n",
+            "         18.3750, 18.3594],\n",
+            "        [19.8438, 21.5000, 21.7500, 20.2969, 21.3594, 22.9688, 20.2031, 27.9062,\n",
+            "         19.6719, 19.6875],\n",
+            "        [23.6719, 22.9062, 23.3750, 22.0000, 26.1250, 23.6719, 20.2812, 27.4688,\n",
+            "         22.2969, 23.2969],\n",
+            "        [20.2812, 20.4219, 22.9688, 20.5625, 28.0938, 21.5938, 19.9062, 22.7188,\n",
+            "         19.2812, 19.7656],\n",
+            "        [22.5938, 19.8594, 22.7344, 25.5000, 24.0156, 24.2031, 21.8438, 23.2344,\n",
+            "         22.0000, 19.5000],\n",
+            "        [20.7031, 23.7969, 21.0000, 19.6406, 20.5469, 20.9531, 18.9688, 21.9062,\n",
+            "         22.1094, 27.7656],\n",
+            "        [20.2344, 19.5312, 22.0938, 23.5156, 21.4531, 26.8906, 22.7188, 21.1250,\n",
+            "         19.4219, 18.6875],\n",
+            "        [22.5156, 21.0469, 21.6406, 19.7500, 19.9375, 19.8281, 17.9844, 19.8594,\n",
+            "         27.9531, 20.8438],\n",
+            "        [19.8750, 19.5781, 21.1719, 19.2031, 17.6250, 19.6562, 17.6406, 19.6094,\n",
+            "         23.9375, 16.5000],\n",
+            "        [28.4688, 21.9062, 25.7969, 19.7344, 20.3594, 20.5000, 17.8281, 21.6406,\n",
+            "         22.7656, 22.3594],\n",
+            "        [22.2969, 20.8594, 21.4531, 19.4531, 19.6406, 20.0156, 20.1406, 20.3438,\n",
+            "         24.7969, 20.9219],\n",
+            "        [20.1875, 19.9375, 21.9375, 20.5625, 22.6250, 21.7812, 19.7188, 27.9062,\n",
+            "         19.7344, 19.3281],\n",
+            "        [19.5469, 19.5312, 22.3438, 21.9844, 26.5625, 23.3750, 20.1406, 27.5000,\n",
+            "         18.7344, 18.4844],\n",
+            "        [18.8750, 25.4531, 20.1719, 18.8750, 19.2812, 19.6562, 19.4531, 18.6875,\n",
+            "         18.7500, 22.1875],\n",
+            "        [20.3594, 20.2344, 19.6875, 18.6094, 18.4062, 18.1094, 17.2656, 18.4531,\n",
+            "         26.3125, 17.9688],\n",
+            "        [18.2031, 19.0938, 19.9219, 19.4844, 26.2969, 19.8125, 17.0938, 21.7500,\n",
+            "         18.2812, 17.5312],\n",
+            "        [18.1406, 22.5312, 19.6094, 19.6719, 19.0312, 19.8906, 18.5156, 21.0938,\n",
+            "         18.7969, 25.6875],\n",
+            "        [19.5781, 19.9688, 20.3750, 21.4844, 19.0938, 26.9844, 20.2969, 21.5000,\n",
+            "         19.4219, 18.5000],\n",
+            "        [20.1250, 20.5000, 23.2344, 23.5000, 29.7344, 23.9375, 21.0000, 22.7500,\n",
+            "         19.4844, 18.7344],\n",
+            "        [19.0938, 24.8281, 20.4844, 18.3750, 17.8281, 20.1406, 15.8438, 19.2812,\n",
+            "         19.1875, 23.9062],\n",
+            "        [19.3594, 19.7188, 20.7969, 19.8594, 21.2031, 21.2656, 17.0156, 28.0625,\n",
+            "         18.3750, 19.6406],\n",
+            "        [25.9844, 23.0625, 23.7500, 23.1719, 22.7500, 24.2031, 21.6875, 27.2969,\n",
+            "         22.1875, 21.9531],\n",
+            "        [18.9531, 20.1875, 20.4688, 20.7031, 21.7812, 21.5469, 16.9531, 28.5312,\n",
+            "         18.2031, 18.6406],\n",
+            "        [18.7344, 19.9688, 20.7344, 20.9219, 21.1094, 21.3281, 17.7031, 27.8438,\n",
+            "         18.3281, 19.0312],\n",
+            "        [28.8438, 21.0000, 23.9375, 19.7031, 20.6875, 20.2656, 21.0469, 20.1719,\n",
+            "         23.0938, 20.5625],\n",
+            "        [20.0312, 20.0000, 21.8906, 27.0625, 20.6875, 22.5156, 22.1250, 21.3906,\n",
+            "         19.8438, 19.0312],\n",
+            "        [20.5625, 23.7969, 20.6250, 18.5625, 18.4219, 20.2031, 17.9219, 19.5781,\n",
+            "         23.7344, 24.3750],\n",
+            "        [20.5312, 20.2500, 23.1562, 25.0469, 23.6562, 25.7500, 22.2656, 22.3438,\n",
+            "         20.6562, 19.1875],\n",
+            "        [20.5000, 20.9062, 23.0000, 27.3281, 22.5469, 24.3906, 23.2188, 23.4062,\n",
+            "         20.9375, 20.9844],\n",
+            "        [25.6250, 21.5312, 22.6562, 19.5156, 17.6719, 20.2969, 20.2812, 20.6719,\n",
+            "         23.4375, 19.3281]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[6],\n",
+            "        [0],\n",
+            "        [5],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [4],\n",
+            "        [3],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [8],\n",
+            "        [8],\n",
+            "        [0],\n",
+            "        [8],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [1],\n",
+            "        [8],\n",
+            "        [4],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [4],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [3],\n",
+            "        [9],\n",
+            "        [5],\n",
+            "        [3],\n",
+            "        [0]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[6, 0, 5, 7, 7, 4, 3, 9, 5, 8, 8, 0, 8, 7, 7, 1, 8, 4, 9, 5, 4, 1, 7, 7,\n",
+            "         7, 7, 0, 3, 9, 5, 3, 0],\n",
+            "        [2, 2, 7, 5, 4, 2, 5, 1, 3, 0, 2, 2, 0, 4, 4, 9, 0, 7, 1, 7, 5, 9, 5, 0,\n",
+            "         4, 5, 2, 5, 1, 3, 5, 8],\n",
+            "        [5, 1, 3, 2, 5, 7, 4, 8, 6, 2, 0, 8, 2, 2, 5, 2, 1, 2, 7, 3, 3, 2, 4, 5,\n",
+            "         5, 4, 8, 6, 8, 4, 7, 2],\n",
+            "        [4, 8, 2, 1, 0, 5, 7, 7, 2, 1, 5, 9, 9, 5, 2, 5, 2, 5, 5, 2, 2, 5, 2, 2,\n",
+            "         3, 3, 6, 2, 2, 2, 6, 1],\n",
+            "        [3, 9, 6, 4, 2, 3, 2, 2, 4, 9, 7, 1, 1, 3, 3, 6, 3, 3, 3, 6, 7, 7, 3, 3,\n",
+            "         2, 2, 1, 7, 0, 7, 2, 7]], device='cuda:0')\n",
+            "tensor([5, 7, 0, 8, 0, 0, 9, 2, 2, 3, 4, 8, 2, 2, 6, 3, 3, 6, 2, 9, 4, 0, 1, 7,\n",
+            "        5, 5, 7, 3, 0, 4, 2, 0], device='cuda:0')\n",
+            "image_features torch.Size([32, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([32, 10])\n",
+            "tensor([[18.3750, 18.0469, 19.6250, 20.2031, 18.4844, 25.7031, 19.3125, 20.1406,\n",
+            "         17.4062, 18.3438],\n",
+            "        [19.0469, 20.3125, 21.0000, 20.6562, 22.1250, 22.0625, 17.1875, 28.0312,\n",
+            "         18.2500, 19.4062],\n",
+            "        [28.3125, 19.8906, 23.3750, 18.3438, 19.4375, 19.1875, 19.3125, 18.8125,\n",
+            "         21.6250, 18.7969],\n",
+            "        [20.9531, 20.6719, 21.3281, 19.2656, 19.6250, 19.7969, 17.7188, 20.0469,\n",
+            "         27.6250, 21.2812],\n",
+            "        [25.2656, 19.3906, 21.2188, 18.3750, 17.9531, 19.1094, 19.9375, 18.9531,\n",
+            "         20.3125, 17.7188],\n",
+            "        [25.2969, 19.8906, 22.8281, 19.0156, 18.7969, 19.6094, 19.6406, 18.4219,\n",
+            "         21.2344, 18.8438],\n",
+            "        [19.0938, 23.3438, 19.1875, 18.6250, 18.7812, 19.4375, 18.7188, 20.2656,\n",
+            "         20.2188, 26.0469],\n",
+            "        [20.6875, 20.3906, 28.2969, 24.6250, 22.0156, 23.9062, 24.2656, 22.5000,\n",
+            "         21.1406, 19.0000],\n",
+            "        [24.8281, 19.7812, 28.5625, 20.6250, 20.7188, 20.9219, 21.4375, 21.0781,\n",
+            "         21.3594, 20.1719],\n",
+            "        [20.9062, 20.6562, 22.2500, 27.4375, 20.7969, 23.4062, 20.2656, 22.7812,\n",
+            "         21.4375, 19.0469],\n",
+            "        [19.2969, 19.2031, 23.3281, 21.2656, 28.0781, 22.2656, 17.8594, 24.6250,\n",
+            "         18.3438, 18.3281],\n",
+            "        [21.2500, 19.4375, 21.1250, 19.3281, 19.0312, 20.5156, 20.2031, 19.5781,\n",
+            "         23.5938, 17.9375],\n",
+            "        [18.9219, 17.9375, 21.9219, 19.7188, 18.8594, 20.6094, 21.4062, 19.7344,\n",
+            "         18.0156, 16.9844],\n",
+            "        [20.4531, 20.5156, 28.6094, 20.2500, 20.4844, 20.1406, 21.6406, 20.3906,\n",
+            "         18.6094, 18.6875],\n",
+            "        [18.5000, 19.7500, 23.2344, 25.6562, 23.7344, 25.0312, 25.1875, 21.6250,\n",
+            "         18.9219, 19.1562],\n",
+            "        [20.5625, 21.5312, 22.6875, 25.0781, 22.9531, 24.2500, 21.5000, 21.9844,\n",
+            "         20.5625, 20.9219],\n",
+            "        [19.2188, 20.0469, 22.0781, 26.7344, 20.1406, 24.3125, 21.3438, 21.7031,\n",
+            "         20.3125, 18.0312],\n",
+            "        [19.3750, 21.4531, 20.9062, 19.4062, 18.6250, 20.4531, 23.7969, 19.7812,\n",
+            "         21.2344, 18.8125],\n",
+            "        [21.6875, 21.1875, 29.1719, 21.5312, 22.2188, 21.4844, 23.2812, 21.7188,\n",
+            "         19.7188, 20.8750],\n",
+            "        [17.8125, 22.1406, 17.1562, 16.9062, 16.6406, 18.0156, 15.1484, 17.8125,\n",
+            "         19.0781, 25.5000],\n",
+            "        [18.0938, 18.1875, 20.7031, 19.5625, 25.3594, 20.5781, 17.8438, 23.5781,\n",
+            "         18.4219, 17.2656],\n",
+            "        [27.9688, 21.3906, 23.9062, 19.8750, 21.0625, 20.4688, 20.9375, 21.2031,\n",
+            "         24.6719, 20.5156],\n",
+            "        [19.5625, 26.4375, 20.0625, 19.1250, 18.4844, 19.3125, 18.6875, 19.4219,\n",
+            "         19.8906, 22.3438],\n",
+            "        [18.9062, 20.0625, 20.3906, 18.6406, 21.7031, 19.7656, 14.5078, 27.8438,\n",
+            "         18.1250, 17.9844],\n",
+            "        [17.5469, 18.5312, 20.3594, 20.7500, 17.8281, 26.0781, 20.0938, 20.3438,\n",
+            "         17.1875, 18.0312],\n",
+            "        [19.3438, 19.2344, 22.9375, 24.7812, 20.6250, 24.2031, 22.4844, 21.8594,\n",
+            "         19.9531, 18.5312],\n",
+            "        [20.4531, 20.7344, 21.0781, 20.7344, 21.3125, 22.6562, 15.7969, 30.2656,\n",
+            "         19.3125, 21.2656],\n",
+            "        [17.7344, 19.7500, 21.2500, 27.0625, 19.5000, 23.0938, 19.1094, 21.3125,\n",
+            "         19.5156, 18.7812],\n",
+            "        [27.4688, 22.0469, 26.3750, 20.5938, 20.3750, 20.3281, 22.4375, 21.7656,\n",
+            "         23.2656, 20.4219],\n",
+            "        [18.4375, 19.8125, 22.3281, 21.2812, 28.1875, 22.7188, 20.3125, 26.5312,\n",
+            "         18.1875, 19.2969],\n",
+            "        [20.8906, 20.8906, 26.7656, 23.1250, 24.0938, 22.8125, 24.0938, 21.8438,\n",
+            "         20.4688, 20.1094],\n",
+            "        [28.0000, 22.6875, 24.6875, 21.2656, 20.6562, 21.1250, 19.6875, 20.8281,\n",
+            "         22.4844, 20.6875]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[5],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [8],\n",
+            "        [0],\n",
+            "        [0],\n",
+            "        [9],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [3],\n",
+            "        [4],\n",
+            "        [8],\n",
+            "        [2],\n",
+            "        [2],\n",
+            "        [3],\n",
+            "        [3],\n",
+            "        [3],\n",
+            "        [6],\n",
+            "        [2],\n",
+            "        [9],\n",
+            "        [4],\n",
+            "        [0],\n",
+            "        [1],\n",
+            "        [7],\n",
+            "        [5],\n",
+            "        [3],\n",
+            "        [7],\n",
+            "        [3],\n",
+            "        [0],\n",
+            "        [4],\n",
+            "        [2],\n",
+            "        [0]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([32, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 32])\n",
+            "top_k_preds_indices tensor([[5, 7, 0, 8, 0, 0, 9, 2, 2, 3, 4, 8, 2, 2, 3, 3, 3, 6, 2, 9, 4, 0, 1, 7,\n",
+            "         5, 3, 7, 3, 0, 4, 2, 0],\n",
+            "        [3, 4, 2, 2, 2, 2, 1, 3, 0, 5, 7, 0, 6, 6, 6, 5, 5, 1, 6, 1, 7, 8, 9, 4,\n",
+            "         3, 5, 5, 5, 2, 7, 6, 2],\n",
+            "        [7, 5, 8, 9, 8, 8, 7, 6, 6, 7, 2, 2, 5, 1, 5, 4, 2, 8, 4, 8, 2, 2, 2, 2,\n",
+            "         2, 2, 4, 7, 8, 5, 4, 1],\n",
+            "        [2, 2, 1, 0, 6, 1, 8, 5, 8, 2, 5, 5, 7, 4, 4, 2, 7, 2, 7, 5, 5, 1, 8, 1,\n",
+            "         7, 6, 9, 2, 6, 2, 3, 8],\n",
+            "        [6, 3, 4, 1, 1, 6, 5, 7, 7, 8, 3, 6, 3, 0, 2, 7, 6, 5, 0, 0, 3, 7, 0, 5,\n",
+            "         6, 7, 2, 1, 1, 3, 5, 3]], device='cuda:0')\n",
+            "tensor([7, 5, 8, 0, 8, 2, 7, 0, 3, 5, 3, 8, 3, 5, 1, 7], device='cuda:0')\n",
+            "image_features torch.Size([16, 512])\n",
+            "zeroshot_weights torch.Size([512, 10])\n",
+            "torch.Size([16, 10])\n",
+            "tensor([[18.1094, 20.5625, 19.5781, 19.8281, 21.2969, 23.8438, 17.4531, 26.9219,\n",
+            "         18.3750, 20.0000],\n",
+            "        [19.5469, 20.1094, 21.5312, 22.7969, 19.2812, 21.8594, 20.6094, 21.1562,\n",
+            "         20.2344, 18.6406],\n",
+            "        [27.0469, 22.0312, 22.3125, 19.8906, 18.7188, 20.2031, 19.3438, 18.6875,\n",
+            "         25.5000, 19.7500],\n",
+            "        [28.5781, 20.5000, 25.3438, 20.2500, 20.2031, 20.6094, 19.6719, 20.9219,\n",
+            "         23.2188, 19.8750],\n",
+            "        [18.9219, 19.2812, 19.6875, 17.5625, 16.6719, 18.2656, 17.1406, 18.1406,\n",
+            "         25.9844, 19.1875],\n",
+            "        [20.5312, 21.7500, 23.8906, 20.6719, 29.3906, 22.5469, 19.3438, 25.6719,\n",
+            "         19.5312, 21.2188],\n",
+            "        [20.1719, 20.2969, 20.8281, 21.5000, 20.9219, 21.8594, 17.7031, 28.6406,\n",
+            "         19.3125, 19.3750],\n",
+            "        [27.9688, 22.4375, 24.0469, 20.6250, 19.4375, 20.5781, 20.3438, 20.9375,\n",
+            "         21.4062, 20.1094],\n",
+            "        [19.2656, 19.3438, 21.9688, 25.9688, 20.6094, 23.1406, 21.0781, 21.2031,\n",
+            "         19.5000, 18.4219],\n",
+            "        [19.4062, 19.5000, 20.4375, 20.1719, 17.9375, 20.6562, 18.5312, 19.8750,\n",
+            "         20.1094, 17.4062],\n",
+            "        [20.4688, 20.8906, 22.8594, 27.6562, 20.9062, 24.3125, 22.8281, 22.2656,\n",
+            "         20.4219, 19.3594],\n",
+            "        [20.8594, 18.9531, 21.2656, 21.3594, 21.7656, 21.5312, 20.8750, 21.7344,\n",
+            "         21.3906, 18.0469],\n",
+            "        [20.6250, 20.3750, 22.8906, 26.6406, 22.8594, 27.0469, 21.4844, 23.7344,\n",
+            "         20.7656, 19.8438],\n",
+            "        [20.5156, 19.7344, 22.2969, 23.0625, 20.6250, 27.4844, 23.7969, 22.6406,\n",
+            "         19.7656, 19.4844],\n",
+            "        [27.5938, 23.0625, 23.8906, 20.8438, 20.2500, 21.5469, 20.7656, 20.5000,\n",
+            "         22.5781, 20.4062],\n",
+            "        [20.5625, 21.0312, 22.9062, 20.8438, 23.0312, 22.6250, 17.7812, 30.3281,\n",
+            "         19.4844, 20.7031]], device='cuda:0', dtype=torch.float16)\n",
+            "top_prediction tensor([[7],\n",
+            "        [3],\n",
+            "        [0],\n",
+            "        [0],\n",
+            "        [8],\n",
+            "        [4],\n",
+            "        [7],\n",
+            "        [0],\n",
+            "        [3],\n",
+            "        [5],\n",
+            "        [3],\n",
+            "        [4],\n",
+            "        [5],\n",
+            "        [5],\n",
+            "        [0],\n",
+            "        [7]], device='cuda:0')\n",
+            "top_prediction.shape torch.Size([16, 1])\n",
+            "top_k_preds_indices.shape torch.Size([5, 16])\n",
+            "top_k_preds_indices tensor([[7, 3, 0, 0, 8, 4, 7, 0, 3, 5, 3, 4, 5, 5, 0, 7],\n",
+            "        [5, 5, 8, 2, 2, 7, 5, 2, 5, 2, 5, 7, 3, 6, 2, 4],\n",
+            "        [4, 2, 2, 8, 1, 2, 3, 1, 2, 3, 2, 5, 7, 3, 1, 2],\n",
+            "        [1, 7, 1, 7, 9, 5, 4, 8, 7, 8, 6, 8, 2, 7, 8, 5],\n",
+            "        [9, 6, 5, 5, 0, 1, 2, 7, 6, 7, 7, 3, 4, 2, 5, 1]], device='cuda:0')\n",
+            "Top-1 accuracy: 89.86\n",
+            "Top-5 accuracy: 99.63\n"
+          ]
+        }
+      ],
+      "source": [
+        "def evaluate_and_visualize_model(model, loader, zeroshot_weights, visualize_predictions, accuracy, cifar10_classes, max_visualizations=10):\n",
+        "    \"\"\"\n",
+        "    РћС†РµРЅРєР° Рё РІРёР·СѓР°Р»РёР·Р°С†РёСЏ РїСЂРµРґСЃРєР°Р·Р°РЅРёР№ РјРѕРґРµР»Рё.\n",
+        "\n",
+        "    РџР°СЂР°РјРµС‚СЂС‹:\n",
+        "    - model: РјРѕРґРµР»СЊ РґР»СЏ РѕС†РµРЅРєРё\n",
+        "    - loader: DataLoader РґР»СЏ РѕС†РµРЅРѕС‡РЅРѕРіРѕ РЅР°Р±РѕСЂР° РґР°РЅРЅС‹С…\n",
+        "    - zeroshot_weights: РІРµСЃР°, РёСЃРїРѕР»СЊР·СѓРµРјС‹Рµ РґР»СЏ zero-shot РєР»Р°СЃСЃРёС„РёРєР°С†РёРё\n",
+        "    - visualize_predictions: С„СѓРЅРєС†РёСЏ РґР»СЏ РІРёР·СѓР°Р»РёР·Р°С†РёРё РїСЂРµРґСЃРєР°Р·Р°РЅРёР№ РјРѕРґРµР»Рё\n",
+        "    - accuracy: С„СѓРЅРєС†РёСЏ РґР»СЏ РІС‹С‡РёСЃР»РµРЅРёСЏ С‚РѕС‡РЅРѕСЃС‚Рё РїСЂРµРґСЃРєР°Р·Р°РЅРёР№ РјРѕРґРµР»Рё\n",
+        "    - cifar10_classes: СЃРїРёСЃРѕРє РЅР°Р·РІР°РЅРёР№ РєР»Р°СЃСЃРѕРІ CIFAR-10\n",
+        "    - max_visualizations: РјР°РєСЃРёРјР°Р»СЊРЅРѕРµ РєРѕР»РёС‡РµСЃС‚РІРѕ РїСЂРµРґСЃРєР°Р·Р°РЅРёР№ РґР»СЏ РІРёР·СѓР°Р»РёР·Р°С†РёРё\n",
+        "    \"\"\"\n",
+        "    predictions = []\n",
+        "    model.eval()\n",
+        "    with torch.no_grad():\n",
+        "        top1_accuracy, top5_accuracy, total_samples = 0., 0., 0.\n",
+        "        visualization_count = 0\n",
+        "\n",
+        "        for batch_index, (image_batch, true_labels) in enumerate(loader):\n",
+        "            # TODO: Send image_batch and true_labels to cuda device\n",
+        "            # TODO: РћС‚РїСЂР°РІРёС‚СЊ image_batch Рё true_labels РЅР° СѓСЃС‚СЂРѕР№СЃС‚РІРѕ CUDA\n",
+        "            # image_batch, true_labels = ...\n",
+        "            image_batch, true_labels = image_batch.cuda(), true_labels.cuda()\n",
+        "\n",
+        "            print(true_labels)\n",
+        "\n",
+        "            # TODO: Generate image features and normalize them\n",
+        "            # HINT: The encode_image() function might be helpful\n",
+        "            # TODO: РЎРіРµРЅРµСЂРёСЂРѕРІР°С‚СЊ РїСЂРёР·РЅР°РєРё РёР·РѕР±СЂР°Р¶РµРЅРёР№ Рё РЅРѕСЂРјР°Р»РёР·РѕРІР°С‚СЊ РёС…\n",
+        "            # РџРћР”РЎРљРђР—РљРђ: РјРѕР¶РµС‚ Р±С‹С‚СЊ РїРѕР»РµР·РЅР° С„СѓРЅРєС†РёСЏ encode_image()\n",
+        "            #image_features = ...\n",
+        "            image_features = model.encode_image(image_batch)\n",
+        "            image_features /= image_features.norm(dim=-1, keepdim=True)\n",
+        "\n",
+        "            print(f\"image_features {image_features.shape}\")\n",
+        "            print(f\"zeroshot_weights {zeroshot_weights.shape}\")\n",
+        "            logits = 100. * image_features @ zeroshot_weights\n",
+        "            print(logits.shape)\n",
+        "            print(logits)\n",
+        "\n",
+        "            _, top_prediction = logits.topk(1, dim=1)\n",
+        "\n",
+        "            print(f\"top_prediction {top_prediction}\")\n",
+        "            print(f\"top_prediction.shape {top_prediction.shape}\")\n",
+        "            predictions.append(top_prediction.cpu())\n",
+        "\n",
+        "            if visualization_count < max_visualizations:\n",
+        "                visualize_predictions(image_batch, true_labels.cpu(), top_prediction.cpu().squeeze(), cifar10_classes)\n",
+        "                visualization_count += 1\n",
+        "\n",
+        "            # Measure and accumulate accuracy\n",
+        "            # РР·РјРµСЂРµРЅРёРµ Рё РЅР°РєРѕРїР»РµРЅРёРµ С‚РѕС‡РЅРѕСЃС‚Рё\n",
+        "            batch_top1_acc, batch_top5_acc = accuracy(logits, true_labels, topk=(1,5))\n",
+        "            top1_accuracy += batch_top1_acc\n",
+        "            top5_accuracy += batch_top5_acc\n",
+        "            total_samples += image_batch.size(0)\n",
+        "\n",
+        "        # Calculate the overall top-1 and top-5 accuracy\n",
+        "        # Р’С‹С‡РёСЃР»РµРЅРёРµ РѕР±С‰РµР№ top-1 Рё top-5 С‚РѕС‡РЅРѕСЃС‚Рё\n",
+        "        overall_top1_accuracy = (top1_accuracy / total_samples) * 100\n",
+        "        overall_top5_accuracy = (top5_accuracy / total_samples) * 100\n",
+        "\n",
+        "        print(f\"Top-1 accuracy: {overall_top1_accuracy:.2f}\")\n",
+        "        print(f\"Top-5 accuracy: {overall_top5_accuracy:.2f}\")\n",
+        "\n",
+        "        return predictions\n",
+        "\n",
+        "predictions = evaluate_and_visualize_model(model, loader, zeroshot_weights, visualize_predictions, accuracy, cifar10_classes, 20)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CO-uVGrd3Ve4"
+      },
+      "outputs": [],
+      "source": [
+        "predictions = torch.cat(predictions).tolist()\n",
+        "import pickle\n",
+        "with open(\"zero_shot_predictions.pickle\", \"wb\") as file:\n",
+        "    pickle.dump(predictions, file)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Congrats on finishing HW 3!"
+      ],
+      "metadata": {
+        "id": "normvaRESrMv"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "9K6k_FLZStRC"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4",
+      "collapsed_sections": [
+        "WvC6JWH_TGKm",
+        "normvaRESrMv"
+      ]
+    },
+    "interpreter": {
+      "hash": "658294e3aa175f6d59f7a2879e95930bec429644047dd9952d1080087cadf91e"
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/lab-4/task_text.ipynb b/lab-4/task_text.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..50c0f7096b767245bf1c3006052a8fe49bf3fdeb
--- /dev/null
+++ b/lab-4/task_text.ipynb
@@ -0,0 +1,229 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# РЎСѓС‚СЊ Р·Р°РґР°РЅРёСЏ\n",
+        "\n",
+        "\n",
+        "1.   Р РµР°Р»РёР·РѕРІР°С‚СЊ СЂРµС€РµРЅРёРµ Р·Р°РґР°С‡Рё РІРµСЂРёС„РёРєР°С†РёРё Р»РёС†Р° (СЃСЂР°РІРЅРµРЅРёРµ 1 Рє 1)\n",
+        "2.   2 РЅР° РєРѕРіРѕ РёР· РњРРРњ РІС‹ РїРѕС…РѕР¶Рё + 2 GUI\n",
+        "2.   РџСЂРѕС‚РµСЃС‚РёСЂРѕРІР°С‚СЊ СЂР°Р±РѕС‚Сѓ СЂР°Р·Р»РёС‡РЅС‹С… РјРµС‚РѕРґРѕРІ С‚СЂРµРєРёРЅРіР° РѕР±СЉРµРєС‚РѕРІ РІ СЂР°Р·Р»РёС‡РЅС‹С… Р·Р°РґР°С‡Р°С….\n",
+        "\n",
+        "РџСЂРёРјРµСЂРЅРѕРµ РІСЂРµРјСЏ РІС‹РїРѕР»РЅРµРЅРёСЏ - 8-10 С‡Р°СЃРѕРІ.\n",
+        "\n",
+        "Р”РµРґР»Р°Р№РЅ: 1 С„РµРІСЂР°Р»СЏ РІ 23:59\n",
+        "\n",
+        "РЁС‚СЂР°С„РЅС‹Рµ РєРѕСЌС„С„РёС†РёРµРЅС‚С‹ РѕС†РµРЅРёРІР°РЅРёСЏ РїСЂРё РїСЂРѕСЃСЂРѕС‡РєРµ:\n",
+        "\n",
+        "-|-\n",
+        "----|---\n",
+        "РџСЂРѕСЃСЂРѕС‡РєР° РјРµРЅСЊС€Рµ 12 С‡Р°СЃРѕРІ | Р±РµР· С€С‚СЂР°С„Р°\n",
+        "РћС‚ 12 С‡Р°СЃРѕРІ РґРѕ 7 РґРЅРµР№ РїРѕСЃР»Рµ РґРµРґР»Р°Р№РЅР° | 0.8\n",
+        "РћС‚ 7 РґРѕ 14 РґРЅРµР№ | 0.6\n",
+        "Р‘РѕР»РµРµ 14 РґРЅРµР№ Рё РґРѕ РЅР°С‡Р°Р»Р° СЃРµСЃСЃРёРё | 0.4\n",
+        "\n",
+        "## РћС†РµРЅРёРІР°РЅРёРµ\n",
+        "\n",
+        "Р—Р°РґР°РЅРёРµ:\n",
+        "\n",
+        "Р§Р°СЃС‚СЊ СЂР°Р±РѕС‚С‹ | РЎС‚РѕРёРјРѕСЃС‚СЊ РІ Р±Р°Р»Р»Р°С…\n",
+        "-------------|--------------------\n",
+        "Р”РµС‚РµРєС†РёСЏ + Р’РµСЂРёС„РёРєР°С†РёСЏ **РњРѕР¶РЅРѕ СЃРґР°С‚СЊ Р±РµР· Р·Р°С‰РёС‚С‹**.| 3\n",
+        "РџРѕРёСЃРєРѕРІРёРє \"MIEM Lookalikes\" **РњРѕР¶РЅРѕ СЃРґР°С‚СЊ Р±РµР· Р·Р°С‰РёС‚С‹**.| 2\n",
+        "MIEM Lookalike Web-GUI **РњРѕР¶РЅРѕ СЃРґР°С‚СЊ Р±РµР· Р·Р°С‰РёС‚С‹**.| 1\n",
+        "РСЃСЃР»РµРґРѕРІР°РЅРёРµ СѓСЃС‚РѕР№С‡РёРІРѕСЃС‚Рё РјРµС‚РѕРґРѕРІ СЂР°СЃРїРѕР·РЅР°РІР°РЅРёСЏ | 4\n",
+        "РС‚РѕРіРѕ | 10 Р±Р°Р»Р»РѕРІ\n",
+        "\n",
+        "Р¤РѕСЂРјСѓР»Р° РѕС†РµРЅРёРІР°РЅРёСЏ РІСЃРµР№ СЂР°Р±РѕС‚С‹:\n",
+        "\n",
+        "Рћ = Р—Р°РґР°РЅРёРµ * 0.9 + РўРµСЃС‚ РЅР° Р»РµРєС†РёРё * 0.1\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "0wRYyp1mM2NV"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 1 Р”РµС‚РµРєС†РёСЏ + РІРµСЂРёС„РёРєР°С†РёСЏ (3 Р±Р°Р»Р»Р°)"
+      ],
+      "metadata": {
+        "id": "V1EHcMnf0YCM"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Р§Р°СЃС‚СЊ 1: РћР±РЅР°СЂСѓР¶РµРЅРёРµ Р»РёС† РІ СЂРµР°Р»СЊРЅРѕРј РІСЂРµРјРµРЅРё РёР»Рё РЅР° РІРёРґРµРѕ (3 Р±Р°Р»Р»Р°)\n",
+        "Р—Р°РґР°С‡Р°:\n",
+        "\n",
+        "РСЃРїРѕР»СЊР·СѓСЏ Р±РёР±Р»РёРѕС‚РµРєСѓ OpenCV Рё РјРµС‚РѕРґ [YuNET](https://docs.opencv.org/4.x/d0/dd4/tutorial_dnn_face.html) (РёР»Рё РґСЂСѓРіРѕР№ РЅРµР№СЂРѕСЃРµС‚РµРІРѕР№ РјРµС‚РѕРґ РѕР±РЅР°СЂСѓР¶РµРЅРёСЏ Р»РёС† РЅР° РІР°С€Рµ СѓСЃРјРѕС‚СЂРµРЅРёРµ), СЂРµР°Р»РёР·СѓР№С‚Рµ РѕР±РЅР°СЂСѓР¶РµРЅРёРµ Р»РёС† РЅР° РІРёРґРµРѕ (РёР»Рё РІРµР±-РєР°РјРµСЂРµ).\n",
+        "РџСЂРѕРіСЂР°РјРјР° РґРѕР»Р¶РЅР° РѕР±СЂР°Р±Р°С‚С‹РІР°С‚СЊ РєР°РґСЂС‹ СЃ РІРµР±РєР°РјРµСЂС‹ Рё РєРѕСЂСЂРµРєС‚РЅРѕ СЂР°СЃРїРѕР·РЅР°РІР°С‚СЊ РІР°С€Рµ Р»РёС†Рѕ. Р’Р°С€Рµ Р»РёС†Рѕ РґРѕР»Р¶РЅРѕ Р±С‹С‚СЊ РІС‹РґРµР»РµРЅРѕ Р·РµР»С‘РЅС‹Рј РїСЂСЏРјРѕСѓРіРѕР»СЊРЅРёРєРѕРј, Р° С‡СѓР¶РёРµ - РєСЂР°СЃРЅС‹РјРё."
+      ],
+      "metadata": {
+        "id": "86yB1xI5-8ng"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 2 MIEM Lookalike (2 Р±Р°Р»Р»Р°)"
+      ],
+      "metadata": {
+        "id": "MjhODhxJ0kGJ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Р РµР°Р»РёР·РѕРІР°С‚СЊ Р·Р°РґР°С‡Сѓ РїРѕРёСЃРєР° Р»РёС† СЃ РёСЃРїРѕР»СЊР·РѕРІР°РЅРёРµРј СЌРјР±РµРґРґРёРЅРіРѕРІ Рё FAISS\n",
+        "\n",
+        "Р—Р°РґР°С‡Р°:\n",
+        "РСЃРїРѕР»СЊР·СѓР№С‚Рµ РїСЂРµРґРѕР±СѓС‡РµРЅРЅСѓСЋ РјРѕРґРµР»СЊ СЂР°СЃРїРѕР·РЅР°РІР°РЅРёСЏ Р»РёС† (РЅР°РїСЂРёРјРµСЂ, FaceNet РёР· Р±РёР±Р»РёРѕС‚РµРєРё DeepFace) РґР»СЏ СЃРѕР·РґР°РЅРёСЏ СЌРјР±РµРґРґРёРЅРіРѕРІ (РІРµРєС‚РѕСЂРЅС‹С… РїСЂРµРґСЃС‚Р°РІР»РµРЅРёР№) РґР»СЏ РІСЃРµС… РёР·РѕР±СЂР°Р¶РµРЅРёР№ Р»РёС† РёР· [РІС‹РіСЂСѓР·РєРё С„РѕС‚РѕРіСЂР°С„РёР№](https://drive.google.com/drive/folders/1I7EzyoJYYiLuOiPyjAHeSdTgzmDUAp2X?usp=sharing) СЃРѕС‚СЂСѓРґРЅРёРєРѕРІ РњРРРњ.\n",
+        "РЎРѕР·РґР°Р№С‚Рµ [FAISS](https://habr.com/ru/companies/okkamgroup/articles/509204/)-Р±Р°Р·Сѓ РґР°РЅРЅС‹С… РґР»СЏ С…СЂР°РЅРµРЅРёСЏ СЌРјР±РµРґРґРёРЅРіРѕРІ Р»РёС†.\n",
+        "Р РµР°Р»РёР·СѓР№С‚Рµ С„СѓРЅРєС†РёСЋ РїРѕРёСЃРєР°:\n",
+        "* Р—Р°РіСЂСѓР·РёС‚Рµ РёР·РѕР±СЂР°Р¶РµРЅРёРµ РЅРѕРІРѕРіРѕ Р»РёС†Р°.\n",
+        "* РР·РІР»РµРєРёС‚Рµ СЌРјР±РµРґРґРёРЅРі Рё РЅР°Р№РґРёС‚Рµ Р±Р»РёР¶Р°Р№С€РёР№ СЌРјР±РµРґРґРёРЅРі РІ FAISS.\n",
+        "* Р РµР°Р»РёР·СѓР№С‚Рµ РІС‹РґР°С‡Сѓ РёРјРµРЅРё С‡РµР»РѕРІРµРєР° СЃ РЅР°РёР±РѕР»РµРµ РїРѕС…РѕР¶РёРј Р»РёС†РѕРј Рё РµРіРѕ СЂР°СЃСЃС‚РѕСЏРЅРёРµ РґРѕ С‚РµРєСѓС‰РµРіРѕ Р»РёС†Р°.\n",
+        "\n",
+        "РџРѕ-СѓРјРѕР»С‡Р°РЅРёСЋ РїСЂРµРґРїРѕР»Р°РіР°РµС‚СЃСЏ РІС‹РїРѕР»РЅРµРЅРёРµ СЂРµС€РµРЅРёСЏ РІ РІРёРґРµ РєРѕРЅСЃРѕР»СЊРЅРѕРіРѕ СЃРєСЂРёРїС‚Р° РёР»Рё РІРЅСѓС‚СЂРё Р±Р»РѕРєРЅРѕС‚Р° Jupyter. Р”РѕРїРѕР»РЅРёС‚РµР»СЊРЅРѕ РјРѕР¶РЅРѕ СЂРµР°Р»РёР·РѕРІР°С‚СЊ РІРµР±-РёРЅС‚РµСЂС„РµР№СЃ СЃ РїРѕРјРѕС‰СЊСЋ Р±РёР±Р»РёРѕС‚РµРєРё streamlit РёР»Рё РґСЂСѓРіРѕРіРѕ РІРµР±-С„СЂРµР№РјРІРѕСЂРєР° РЅР° РІР°С€Рµ СѓСЃРјРѕС‚СЂРµРЅРёРµ (+1 Р±Р°Р»Р»).\n"
+      ],
+      "metadata": {
+        "id": "knOMSWM60mKg"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 3 РђРЅР°Р»РёР· СѓСЃС‚РѕР№С‡РёРІРѕСЃС‚Рё СЂР°СЃРїРѕР·РЅР°РІР°РЅРёСЏ Р»РёС† (4 Р±Р°Р»Р»Р°)\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "BdzH5yGw--rt"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Р§Р°СЃС‚СЊ 1. РћС†РµРЅРёС‚СЊ С‚РѕС‡РЅРѕСЃС‚СЊ СЂР°Р±РѕС‚С‹ РјРµС‚РѕРґРѕРІ РёР· DeepFace РЅР° С‚РµСЃС‚РѕРІРѕРј РІРёРґРµРѕ (2 Р±Р°Р»Р»Р°)."
+      ],
+      "metadata": {
+        "id": "G_RL2wOgfAUe"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Р’ РґР°РЅРЅРѕРј Р·Р°РґР°РЅРёРё РїСЂРµРґР»Р°РіР°РµС‚СЃСЏ СЂР°СЃСЃРјРѕС‚СЂРµС‚СЊ Р·Р°РґР°С‡Сѓ СЂР°СЃРїРѕР·РЅР°РІР°РЅРёСЏ Р»РёС† РєР°Рє Р·Р°РґР°С‡Сѓ РєР»Р°СЃСЃРёС„РёРєР°С†РёРё РґР»СЏ РѕС†РµРЅРєРё С‚РѕС‡РЅРѕСЃС‚Рё.\n",
+        "Р’Р°Рј РґР°РЅС‹ [РІРёРґРµРѕ](https://drive.google.com/drive/folders/1z_YCdJF2Rf9WzlNSY3BpNFiakNisq5VB?usp=sharing), РґР»СЏ РєРѕС‚РѕСЂС‹С… РїСЂРµРґСЃС‚Р°РІР»РµРЅР° СЂР°Р·РјРµС‚РєР° РІ РІРёРґРµ С‚Р°Р№Рј-РєРѕРґРѕРІ Рё Р±Р°Р·Р° С„РѕС‚РѕРіСЂР°С„РёР№ Р»СЋРґРµР№ СЃ СЌС‚РёС… РІРёРґРµРѕ.\n",
+        "РќРµРѕР±С…РѕРґРёРјРѕ РІР·СЏС‚СЊ РєР°Р¶РґС‹Р№ 50-Р№ РєР°РґСЂ РІРёРґРµРѕ (СЃРїРѕСЃРѕР± СЂР°Р·Р±РёРµРЅРёСЏ РЅР° РєР°РґСЂС‹ СЃ СѓС‡С‘С‚РѕРј СЂР°Р·РјРµС‚РєРё - РЅР° РІР°С€Рµ СѓСЃРјРѕС‚СЂРµРЅРёРµ) Рё РґР»СЏ РїРѕР»СѓС‡РµРЅРЅРѕРіРѕ РЅР°Р±РѕСЂР° РёР·РѕР±СЂР°Р¶РµРЅРёР№ РѕС†РµРЅРёС‚СЊ РјРµС‚СЂРёРєСѓ Precision РЅР° РґР°РЅРЅРѕРј РЅР°Р±РѕСЂРµ РёР·РѕР±СЂР°Р¶РµРЅРёР№ РґР»СЏ РІСЃРµС… Р»РёС†, РїСЂРёСЃСѓС‚СЃС‚РІСѓСЋС‰РёС… РЅР° РІРёРґРµРѕ Рё РІ СЂР°Р·РјРµС‚РєРµ.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "5UsJb8VkPzd0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Р§Р°СЃС‚СЊ 2. РћС†РµРЅРёС‚СЊ С‚РѕС‡РЅРѕСЃС‚СЊ СЂР°Р±РѕС‚С‹ РјРµС‚РѕРґРѕРІ РёР· DeepFace РЅР° Р°СѓРіРјРµРЅС‚РёСЂРѕРІР°РЅРЅС‹С… РґР°РЅРЅС‹С… (2 Р±Р°Р»Р»Р°)."
+      ],
+      "metadata": {
+        "id": "2iRrc6SRg4-d"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "РќРµРѕР±С…РѕРґРёРјРѕ СЃРѕР±СЂР°С‚СЊ СЃРѕР±СЃС‚РІРµРЅРЅС‹Р№ РЅР°Р±РѕСЂ РґР°РЅРЅС‹С… РёР· **СЂР°Р·Р»РёС‡РЅС‹С…** РёР·РѕР±СЂР°Р¶РµРЅРёР№ Р’Р°С€РµРіРѕ Р»РёС†Р° СЃ СЂР°Р·РЅС‹С… СЂР°РєСѓСЂСЃРѕРІ, Р¶РµР»Р°С‚РµР»СЊРЅРѕ РЅР°СЃС‚РѕСЏС‰РёРµ С„РѕС‚РѕРіСЂР°С„РёРё РёР· Р»РёС‡РЅРѕРіРѕ Р°СЂС…РёРІР° (20 С€С‚СѓРє)\\\n",
+        "Р’РѕР·СЊРјРёС‚Рµ СЌС‚Р°Р»РѕРЅРЅРѕРµ РёР·РѕР±СЂР°Р¶РµРЅРёРµ (РєР°Рє РІ РїР°СЃРїРѕСЂС‚Рµ) Рё РїСЂРё РїРѕРјРѕС‰Рё Р±РёР±Р»РёРѕС‚РµРєРё [DeepFace](https://github.com/serengil/deepface) РїСЂРѕРІРµСЂСЊС‚Рµ РµРіРѕ РЅР° СЃРѕРѕС‚РІРµС‚СЃС‚РІРёРµ РІСЃРµРјСѓ РґР°С‚Р°СЃРµС‚Сѓ. РџРѕСЃС‡РёС‚Р°Р№С‚Рµ РјРµС‚СЂРёРєСѓ Precision. \\\n",
+        "\\\n",
+        "РџСЂРёРјРµРЅРёС‚Рµ РєР°Р¶РґСѓСЋ РёР· РїРµСЂРµС‡РёСЃР»РµРЅРЅС‹С… РЅРёР¶Рµ Р°СѓРіРјРµРЅС‚Р°С†РёР№ (**РїРѕ-РѕС‚РґРµР»СЊРЅРѕСЃС‚Рё**) РєРѕ РІСЃРµРјСѓ РґР°С‚Р°СЃРµС‚Сѓ Рё РёР·РјРµСЂСЊС‚Рµ РјРµС‚СЂРёРєСѓ Precision РґР»СЏ РёР·РјРµРЅРµРЅРЅРЅРѕРіРѕ РґР°С‚Р°СЃРµС‚Р°:\n",
+        "*   РџРѕРІРѕСЂРѕС‚ РёР·РѕР±СЂР°Р¶РµРЅРёСЏ РЅР° 45В° Рё 90В°.\n",
+        "*   Р”РѕР±Р°РІР»РµРЅРёРµ С€СѓРјР° (Gaussian Noise).\n",
+        "*   РР·РјРµРЅРµРЅРёРµ СЏСЂРєРѕСЃС‚Рё (СѓРІРµР»РёС‡РµРЅРёРµ Рё СѓРјРµРЅСЊС€РµРЅРёРµ РЅР° 50%).\n",
+        "*   Р Р°Р·РјС‹С‚РёРµ СЃ СЂР°Р·Р»РёС‡РЅС‹РјРё РїР°СЂР°РјРµС‚СЂР°РјРё.\n",
+        "\\\n",
+        "Р РµСѓР·СѓР»СЊС‚Р°С‚С‹ СЃРѕР±РµСЂРёС‚Рµ РІ С‚Р°Р±Р»РёС†Сѓ РІРёРґР°:\n",
+        "\n",
+        "РњРµС‚РѕРґ | РСЃС…РѕРґРЅС‹Р№ РґР°С‚Р°СЃРµС‚ | РџРѕРІРѕСЂРѕС‚ РЅР° 45В° | РџРѕРІРѕСЂРѕС‚ РЅР° 90В° | РР·РѕР±СЂР°Р¶РµРЅРёРµ СЃ С€СѓРјРѕРј |\n",
+        "--- | ----|--- | --- | --- |\n",
+        "VGG-Face | 0 | 0 | 0 | 0 |\n",
+        "Facenet | 0 | 0 | 0 | 0 |\n",
+        "Facenet512 | 0 | 0 | 0 | 0 |\n",
+        "OpenFace | 0 | 0 | 0 | 0 |\n",
+        "DeepFace | 0 | 0 | 0 | 0 |\n",
+        "DeepID | 0 | 0 | 0 | 0 |\n",
+        "ArcFace | 0 | 0 | 0 | 0 |\n",
+        "Dlib | 0 | 0 | 0 | 0 |\n",
+        "SFace | 0 | 0 | 0 | 0 |\n",
+        "GhostFaceNet | 0 | 0 | 0 | 0 |"
+      ],
+      "metadata": {
+        "id": "aFA9PwlwjeDV"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "SibiLplgP2bC"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# РС‚РѕРіРѕРІР°СЏ РїСЂРѕРІРµСЂРєР° Рё РѕС‚С‡С‘С‚РЅРѕСЃС‚СЊ\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "bMzGSyoKQUgV"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Р—Р°РґР°РЅРёРµ 1\n",
+        "* РЎСЃС‹Р»РєР° РЅР° РёСЃС…РѕРґРЅС‹Р№ РєРѕРґ РїСЂРёР»РѕР¶РµРЅРёСЏ РІ GitHub\n",
+        "* РЎСЃС‹Р»РєР° РЅР° РІРёРґРµРѕР·Р°РїРёСЃСЊ СЌРєСЂР°РЅР° РџРљ РІРѕ РІСЂРµРјСЏ СЂР°Р±РѕС‚С‹ РїСЂРёР»РѕР¶РµРЅРёСЏ\n",
+        "\n",
+        "## Р—Р°РґР°РЅРёРµ 2\n",
+        "* РЎСЃС‹Р»РєР° РЅР° РёСЃС…РѕРґРЅС‹Р№ РєРѕРґ РїСЂРёР»РѕР¶РµРЅРёСЏ РІ GitHub\n",
+        "* РЎСЃС‹Р»РєР° РЅР° РІРёРґРµРѕР·Р°РїРёСЃСЊ СЌРєСЂР°РЅР° РџРљ РІРѕ РІСЂРµРјСЏ СЂР°Р±РѕС‚С‹ РїСЂРёР»РѕР¶РµРЅРёСЏ\n",
+        "\n",
+        "## Р—Р°РґР°РЅРёРµ 3\n",
+        "* РЎСЃС‹Р»РєР° РЅР° Jupyter Notebook СЃ РєРѕРґРѕРј РїРѕРґСЃС‡С‘С‚Р° РјРµС‚СЂРёРєРё РїРѕ РєР°РґСЂР°Рј СЃ РІРёРґРµРѕ РІ GitHub\n",
+        "* РЎСЃС‹Р»РєР° РЅР° Р°СЂС…РёРІ РІ С„РѕСЂРјР°С‚Рµ ZIP РёР»Рё 7z СЃ РІР°С€РёРјРё С„РѕС‚Рѕ\n",
+        "* РЎСЃС‹Р»РєР° РЅР° Jupyter Notebook СЃ РєРѕРґРѕРј РЅР°Р»РѕР¶РµРЅРёСЏ Р°СѓРіРјРµРЅС‚Р°С†РёР№ Рё РїРѕРґСЃС‡С‘С‚Р° РјРµС‚СЂРёРєРё РІ GitHub"
+      ],
+      "metadata": {
+        "id": "oJY1a_RAjivD"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# РљРѕРЅС‚СЂРѕР»СЊРЅС‹Рµ РІРѕРїСЂРѕСЃС‹\n"
+      ],
+      "metadata": {
+        "id": "ny14-XnN8QVM"
+      }
+    }
+  ]
+}
\ No newline at end of file