{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-06 15:29:19.781439: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-01-06 15:29:19.803460: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-01-06 15:29:19.803478: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-01-06 15:29:19.804068: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-01-06 15:29:19.807739: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-01-06 15:29:20.208316: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from matplotlib import pyplot as plt\n",
    "import re\n",
    "import keras\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing import image\n",
    "\n",
    "def atoi(text):\n",
    "    return int(text) if text.isdigit() else text\n",
    "\n",
    "def natural_keys(text):\n",
    "    '''\n",
    "    alist.sort(key=natural_keys) sorts in human order\n",
    "    http://nedbatchelder.com/blog/200712/human_sorting.html\n",
    "    (See Toothy's implementation in the comments)\n",
    "    '''\n",
    "    return [ atoi(c) for c in re.split(r'(\\d+)', text) ]\n",
    "\n",
    "label_list = [1, 2, 3, 4, 5, 6, 0, 0, 0, 0]\n",
    "def load_images_from_path(path):\n",
    "    images = []\n",
    "    labels = []\n",
    "\n",
    "    # there is probably a cleaner way to do this but I am not willing to rename all the files\n",
    "    i = 0\n",
    "    path_ar = os.listdir(path)\n",
    "    path_ar.sort(key=natural_keys)\n",
    "    print(path_ar)\n",
    "    for file in path_ar:\n",
    "        if i % 10 == 0:\n",
    "            i = 0\n",
    "        images.append(image.img_to_array(image.load_img(os.path.join(path, file), target_size=(224, 224, 3))))\n",
    "        labels.append((label_list[i]))\n",
    "        i += 1\n",
    "        \n",
    "    return images, labels\n",
    "\n",
    "def show_images(images):\n",
    "    fig, axes = plt.subplots(1, 8, figsize=(20, 20), subplot_kw={'xticks': [], 'yticks': []})\n",
    "\n",
    "    for i, ax in enumerate(axes.flat):\n",
    "        ax.imshow(images[i] / 255)\n",
    "\n",
    "x_a = []\n",
    "x_b = []\n",
    "x_c = []\n",
    "y = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['rec_0_a_1.png', 'rec_1_a_1.5.png', 'rec_2_a_2.png', 'rec_3_a_2.5.png', 'rec_4_a_3.png', 'rec_5_a_3.5.png', 'rec_6_a_4.png', 'rec_7_a_4.png', 'rec_8_a_4.png', 'rec_9_a_4.png', 'rec_10_a_1.png', 'rec_11_a_1.5.png', 'rec_12_a_2.png', 'rec_13_a_2.5.png', 'rec_14_a_3.png', 'rec_15_a_3.5.png', 'rec_16_a_4.png', 'rec_17_a_4.png', 'rec_18_a_4.png', 'rec_19_a_4.png', 'rec_20_a_1.png', 'rec_21_a_1.5.png', 'rec_22_a_2.png', 'rec_23_a_2.5.png', 'rec_24_a_3.png', 'rec_25_a_3.5.png', 'rec_26_a_4.png', 'rec_27_a_4.png', 'rec_28_a_4.png', 'rec_29_a_4.png', 'rec_30_a_1.png', 'rec_31_a_1.5.png', 'rec_32_a_2.png', 'rec_33_a_2.5.png', 'rec_34_a_3.png', 'rec_35_a_3.5.png', 'rec_36_a_4.png', 'rec_37_a_4.png', 'rec_38_a_4.png', 'rec_39_a_4.png', 'rec_40_a_1.png', 'rec_41_a_1.5.png', 'rec_42_a_2.png', 'rec_43_a_2.5.png', 'rec_44_a_3.png', 'rec_45_a_3.5.png', 'rec_46_a_4.png', 'rec_47_a_4.png', 'rec_48_a_4.png', 'rec_49_a_4.png', 'rec_50_a_1.png', 'rec_51_a_1.5.png', 'rec_52_a_2.png', 'rec_53_a_2.5.png', 'rec_54_a_3.png', 'rec_55_a_3.5.png', 'rec_56_a_4.png', 'rec_57_a_4.png', 'rec_58_a_4.png', 'rec_59_a_4.png', 'rec_60_a_1.png', 'rec_61_a_1.5.png', 'rec_62_a_2.png', 'rec_63_a_2.5.png', 'rec_64_a_3.png', 'rec_65_a_3.5.png', 'rec_66_a_4.png', 'rec_67_a_4.png', 'rec_68_a_4.png', 'rec_69_a_4.png', 'rec_70_a_1.png', 'rec_71_a_1.5.png', 'rec_72_a_2.png', 'rec_73_a_2.5.png', 'rec_74_a_3.png', 'rec_75_a_3.5.png', 'rec_76_a_4.png', 'rec_77_a_4.png', 'rec_78_a_4.png', 'rec_79_a_4.png', 'rec_80_a_1.png', 'rec_81_a_1.5.png', 'rec_82_a_2.png', 'rec_83_a_2.5.png', 'rec_84_a_3.png', 'rec_85_a_3.5.png', 'rec_86_a_4.png', 'rec_87_a_4.png', 'rec_88_a_4.png', 'rec_89_a_4.png', 'rec_90_a_1.png', 'rec_91_a_1.5.png', 'rec_92_a_2.png', 'rec_93_a_2.5.png', 'rec_94_a_3.png', 'rec_95_a_3.5.png', 'rec_96_a_4.png', 'rec_97_a_4.png', 'rec_98_a_4.png', 'rec_99_a_4.png', 'rec_100_a_1.png', 'rec_101_a_1.5.png', 'rec_102_a_2.png', 'rec_103_a_2.5.png', 'rec_104_a_3.png', 'rec_105_a_3.5.png', 'rec_106_a_4.png', 'rec_107_a_4.png', 'rec_108_a_4.png', 'rec_109_a_4.png', 'rec_110_a_1.png', 'rec_111_a_1.5.png', 'rec_112_a_2.png', 'rec_113_a_2.5.png', 'rec_114_a_3.png', 'rec_115_a_3.5.png', 'rec_116_a_4.png', 'rec_117_a_4.png', 'rec_118_a_4.png', 'rec_119_a_4.png']\n",
      "['rec_0_b_1.png', 'rec_1_b_1.5.png', 'rec_2_b_2.png', 'rec_3_b_2.5.png', 'rec_4_b_3.png', 'rec_5_b_3.5.png', 'rec_6_b_4.png', 'rec_7_b_4.png', 'rec_8_b_4.png', 'rec_9_b_4.png', 'rec_10_b_1.png', 'rec_11_b_1.5.png', 'rec_12_b_2.png', 'rec_13_b_2.5.png', 'rec_14_b_3.png', 'rec_15_b_3.5.png', 'rec_16_b_4.png', 'rec_17_b_4.png', 'rec_18_b_4.png', 'rec_19_b_4.png', 'rec_20_b_1.png', 'rec_21_b_1.5.png', 'rec_22_b_2.png', 'rec_23_b_2.5.png', 'rec_24_b_3.png', 'rec_25_b_3.5.png', 'rec_26_b_4.png', 'rec_27_b_4.png', 'rec_28_b_4.png', 'rec_29_b_4.png', 'rec_30_b_1.png', 'rec_31_b_1.5.png', 'rec_32_b_2.png', 'rec_33_b_2.5.png', 'rec_34_b_3.png', 'rec_35_b_3.5.png', 'rec_36_b_4.png', 'rec_37_b_4.png', 'rec_38_b_4.png', 'rec_39_b_4.png', 'rec_40_b_1.png', 'rec_41_b_1.5.png', 'rec_42_b_2.png', 'rec_43_b_2.5.png', 'rec_44_b_3.png', 'rec_45_b_3.5.png', 'rec_46_b_4.png', 'rec_47_b_4.png', 'rec_48_b_4.png', 'rec_49_b_4.png', 'rec_50_b_1.png', 'rec_51_b_1.5.png', 'rec_52_b_2.png', 'rec_53_b_2.5.png', 'rec_54_b_3.png', 'rec_55_b_3.5.png', 'rec_56_b_4.png', 'rec_57_b_4.png', 'rec_58_b_4.png', 'rec_59_b_4.png', 'rec_60_b_1.png', 'rec_61_b_1.5.png', 'rec_62_b_2.png', 'rec_63_b_2.5.png', 'rec_64_b_3.png', 'rec_65_b_3.5.png', 'rec_66_b_4.png', 'rec_67_b_4.png', 'rec_68_b_4.png', 'rec_69_b_4.png', 'rec_70_b_1.png', 'rec_71_b_1.5.png', 'rec_72_b_2.png', 'rec_73_b_2.5.png', 'rec_74_b_3.png', 'rec_75_b_3.5.png', 'rec_76_b_4.png', 'rec_77_b_4.png', 'rec_78_b_4.png', 'rec_79_b_4.png', 'rec_80_b_1.png', 'rec_81_b_1.5.png', 'rec_82_b_2.png', 'rec_83_b_2.5.png', 'rec_84_b_3.png', 'rec_85_b_3.5.png', 'rec_86_b_4.png', 'rec_87_b_4.png', 'rec_88_b_4.png', 'rec_89_b_4.png', 'rec_90_b_1.png', 'rec_91_b_1.5.png', 'rec_92_b_2.png', 'rec_93_b_2.5.png', 'rec_94_b_3.png', 'rec_95_b_3.5.png', 'rec_96_b_4.png', 'rec_97_b_4.png', 'rec_98_b_4.png', 'rec_99_b_4.png', 'rec_100_b_1.png', 'rec_101_b_1.5.png', 'rec_102_b_2.png', 'rec_103_b_2.5.png', 'rec_104_b_3.png', 'rec_105_b_3.5.png', 'rec_106_b_4.png', 'rec_107_b_4.png', 'rec_108_b_4.png', 'rec_109_b_4.png', 'rec_110_b_1.png', 'rec_111_b_1.5.png', 'rec_112_b_2.png', 'rec_113_b_2.5.png', 'rec_114_b_3.png', 'rec_115_b_3.5.png', 'rec_116_b_4.png', 'rec_117_b_4.png', 'rec_118_b_4.png', 'rec_119_b_4.png']\n",
      "['rec_0_c_1.png', 'rec_1_c_1.5.png', 'rec_2_c_2.png', 'rec_3_c_2.5.png', 'rec_4_c_3.png', 'rec_5_c_3.5.png', 'rec_6_c_4.png', 'rec_7_c_4.png', 'rec_8_c_4.png', 'rec_9_c_4.png', 'rec_10_c_1.png', 'rec_11_c_1.5.png', 'rec_12_c_2.png', 'rec_13_c_2.5.png', 'rec_14_c_3.png', 'rec_15_c_3.5.png', 'rec_16_c_4.png', 'rec_17_c_4.png', 'rec_18_c_4.png', 'rec_19_c_4.png', 'rec_20_c_1.png', 'rec_21_c_1.5.png', 'rec_22_c_2.png', 'rec_23_c_2.5.png', 'rec_24_c_3.png', 'rec_25_c_3.5.png', 'rec_26_c_4.png', 'rec_27_c_4.png', 'rec_28_c_4.png', 'rec_29_c_4.png', 'rec_30_c_1.png', 'rec_31_c_1.5.png', 'rec_32_c_2.png', 'rec_33_c_2.5.png', 'rec_34_c_3.png', 'rec_35_c_3.5.png', 'rec_36_c_4.png', 'rec_37_c_4.png', 'rec_38_c_4.png', 'rec_39_c_4.png', 'rec_40_c_1.png', 'rec_41_c_1.5.png', 'rec_42_c_2.png', 'rec_43_c_2.5.png', 'rec_44_c_3.png', 'rec_45_c_3.5.png', 'rec_46_c_4.png', 'rec_47_c_4.png', 'rec_48_c_4.png', 'rec_49_c_4.png', 'rec_50_c_1.png', 'rec_51_c_1.5.png', 'rec_52_c_2.png', 'rec_53_c_2.5.png', 'rec_54_c_3.png', 'rec_55_c_3.5.png', 'rec_56_c_4.png', 'rec_57_c_4.png', 'rec_58_c_4.png', 'rec_59_c_4.png', 'rec_60_c_1.png', 'rec_61_c_1.5.png', 'rec_62_c_2.png', 'rec_63_c_2.5.png', 'rec_64_c_3.png', 'rec_65_c_3.5.png', 'rec_66_c_4.png', 'rec_67_c_4.png', 'rec_68_c_4.png', 'rec_69_c_4.png', 'rec_70_c_1.png', 'rec_71_c_1.5.png', 'rec_72_c_2.png', 'rec_73_c_2.5.png', 'rec_74_c_3.png', 'rec_75_c_3.5.png', 'rec_76_c_4.png', 'rec_77_c_4.png', 'rec_78_c_4.png', 'rec_79_c_4.png', 'rec_80_c_1.png', 'rec_81_c_1.5.png', 'rec_82_c_2.png', 'rec_83_c_2.5.png', 'rec_84_c_3.png', 'rec_85_c_3.5.png', 'rec_86_c_4.png', 'rec_87_c_4.png', 'rec_88_c_4.png', 'rec_89_c_4.png', 'rec_90_c_1.png', 'rec_91_c_1.5.png', 'rec_92_c_2.png', 'rec_93_c_2.5.png', 'rec_94_c_3.png', 'rec_95_c_3.5.png', 'rec_96_c_4.png', 'rec_97_c_4.png', 'rec_98_c_4.png', 'rec_99_c_4.png', 'rec_100_c_1.png', 'rec_101_c_1.5.png', 'rec_102_c_2.png', 'rec_103_c_2.5.png', 'rec_104_c_3.png', 'rec_105_c_3.5.png', 'rec_106_c_4.png', 'rec_107_c_4.png', 'rec_108_c_4.png', 'rec_109_c_4.png', 'rec_110_c_1.png', 'rec_111_c_1.5.png', 'rec_112_c_2.png', 'rec_113_c_2.5.png', 'rec_114_c_3.png', 'rec_115_c_3.5.png', 'rec_116_c_4.png', 'rec_117_c_4.png', 'rec_118_c_4.png', 'rec_119_c_4.png']\n"
     ]
    }
   ],
   "source": [
    "images, labels = load_images_from_path('./data/a_png')\n",
    "x_a += images\n",
    "y += labels\n",
    "images, _ = load_images_from_path('./data/b_png')\n",
    "x_b += images\n",
    "images, _ = load_images_from_path('./data/c_png')\n",
    "x_c += images\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.utils import to_categorical\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_a_train, x_a_test, x_b_train, x_b_test, x_c_train, x_c_test, y_train, y_test = train_test_split(x_a, x_b, x_c, y, stratify=y, test_size=0.2, random_state=42)\n",
    "\n",
    "# normalize\n",
    "x_a_train_norm = np.array(x_a_train) / 255\n",
    "x_a_test_norm = np.array(x_a_test) / 255\n",
    "x_b_train_norm = np.array(x_b_train) / 255\n",
    "x_b_test_norm = np.array(x_b_test) / 255\n",
    "x_c_train_norm = np.array(x_c_train) / 255\n",
    "x_c_test_norm = np.array(x_c_test) / 255\n",
    "\n",
    "# categorize\n",
    "y_train = to_categorical(y_train)\n",
    "y_test = to_categorical(y_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96, 224, 224, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_a_train_norm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)        [(None, 224, 224, 3)]        0         []                            \n",
      "                                                                                                  \n",
      " input_2 (InputLayer)        [(None, 224, 224, 3)]        0         []                            \n",
      "                                                                                                  \n",
      " input_3 (InputLayer)        [(None, 224, 224, 3)]        0         []                            \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)             (None, 222, 222, 64)         1792      ['input_1[0][0]']             \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)           (None, 222, 222, 64)         1792      ['input_2[0][0]']             \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)           (None, 222, 222, 64)         1792      ['input_3[0][0]']             \n",
      "                                                                                                  \n",
      " max_pooling2d (MaxPooling2  (None, 111, 111, 64)         0         ['conv2d[0][0]']              \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " max_pooling2d_1 (MaxPoolin  (None, 111, 111, 64)         0         ['conv2d_1[0][0]']            \n",
      " g2D)                                                                                             \n",
      "                                                                                                  \n",
      " max_pooling2d_2 (MaxPoolin  (None, 111, 111, 64)         0         ['conv2d_2[0][0]']            \n",
      " g2D)                                                                                             \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)           (None, 109, 109, 128)        73856     ['max_pooling2d[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)           (None, 109, 109, 128)        73856     ['max_pooling2d_1[0][0]']     \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)           (None, 109, 109, 128)        73856     ['max_pooling2d_2[0][0]']     \n",
      "                                                                                                  \n",
      " max_pooling2d_3 (MaxPoolin  (None, 54, 54, 128)          0         ['conv2d_3[0][0]']            \n",
      " g2D)                                                                                             \n",
      "                                                                                                  \n",
      " max_pooling2d_4 (MaxPoolin  (None, 54, 54, 128)          0         ['conv2d_4[0][0]']            \n",
      " g2D)                                                                                             \n",
      "                                                                                                  \n",
      " max_pooling2d_5 (MaxPoolin  (None, 54, 54, 128)          0         ['conv2d_5[0][0]']            \n",
      " g2D)                                                                                             \n",
      "                                                                                                  \n",
      " flatten (Flatten)           (None, 373248)               0         ['max_pooling2d_3[0][0]']     \n",
      "                                                                                                  \n",
      " flatten_1 (Flatten)         (None, 373248)               0         ['max_pooling2d_4[0][0]']     \n",
      "                                                                                                  \n",
      " flatten_2 (Flatten)         (None, 373248)               0         ['max_pooling2d_5[0][0]']     \n",
      "                                                                                                  \n",
      " concatenate (Concatenate)   (None, 1119744)              0         ['flatten[0][0]',             \n",
      "                                                                     'flatten_1[0][0]',           \n",
      "                                                                     'flatten_2[0][0]']           \n",
      "                                                                                                  \n",
      " dense (Dense)               (None, 128)                  1433273   ['concatenate[0][0]']         \n",
      "                                                          60                                      \n",
      "                                                                                                  \n",
      " dense_1 (Dense)             (None, 64)                   8256      ['dense[0][0]']               \n",
      "                                                                                                  \n",
      " dense_2 (Dense)             (None, 7)                    455       ['dense_1[0][0]']             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 143563015 (547.65 MB)\n",
      "Trainable params: 143563015 (547.65 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-06 15:29:24.127761: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-01-06 15:29:24.144425: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-01-06 15:29:24.144522: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-01-06 15:29:24.145690: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-01-06 15:29:24.145768: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-01-06 15:29:24.145814: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-01-06 15:29:24.377736: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-01-06 15:29:24.377827: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-01-06 15:29:24.377880: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-01-06 15:29:24.377923: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6348 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3050, pci bus id: 0000:01:00.0, compute capability: 8.6\n"
     ]
    }
   ],
   "source": [
    "# multi input cnn\n",
    "from keras.models import Model\n",
    "\n",
    "input_a = keras.Input(shape=(224, 224, 3))\n",
    "input_b = keras.Input(shape=(224, 224, 3))\n",
    "input_c = keras.Input(shape=(224, 224, 3))\n",
    "\n",
    "conv1_a = keras.layers.Conv2D(64, (3, 3), activation='relu')(input_a)\n",
    "pool1_a = keras.layers.MaxPooling2D((2, 2))(conv1_a)\n",
    "\n",
    "conv1_b = keras.layers.Conv2D(64, (3, 3), activation='relu')(input_b)\n",
    "pool1_b = keras.layers.MaxPooling2D((2, 2))(conv1_b)\n",
    "\n",
    "conv1_c = keras.layers.Conv2D(64, (3, 3), activation='relu')(input_c)\n",
    "pool1_c = keras.layers.MaxPooling2D((2, 2))(conv1_c)\n",
    "\n",
    "conv2_a = keras.layers.Conv2D(128, (3, 3), activation='relu')(pool1_a)\n",
    "pool2_a = keras.layers.MaxPooling2D((2, 2))(conv2_a)\n",
    "\n",
    "conv2_b = keras.layers.Conv2D(128, (3, 3), activation='relu')(pool1_b)\n",
    "pool2_b = keras.layers.MaxPooling2D((2, 2))(conv2_b)\n",
    "\n",
    "conv2_c = keras.layers.Conv2D(128, (3, 3), activation='relu')(pool1_c)\n",
    "pool2_c = keras.layers.MaxPooling2D((2, 2))(conv2_c)\n",
    "\n",
    "# conv3_a = keras.layers.Conv2D(128, (3, 3), activation='relu')(pool2_a)\n",
    "# pool3_a = keras.layers.MaxPooling2D((2, 2))(conv3_a)\n",
    "\n",
    "# conv3_b = keras.layers.Conv2D(128, (3, 3), activation='relu')(pool2_b)\n",
    "# pool3_b = keras.layers.MaxPooling2D((2, 2))(conv3_b)\n",
    "\n",
    "# conv3_c = keras.layers.Conv2D(128, (3, 3), activation='relu')(pool2_c)\n",
    "# pool3_c = keras.layers.MaxPooling2D((2, 2))(conv3_c)\n",
    "\n",
    "flat_a = keras.layers.Flatten()(pool2_a)\n",
    "flat_b = keras.layers.Flatten()(pool2_b)\n",
    "flat_c = keras.layers.Flatten()(pool2_c)\n",
    "\n",
    "concat = keras.layers.concatenate([flat_a, flat_b, flat_c])\n",
    "\n",
    "dense1 = keras.layers.Dense(128, activation='relu')(concat)\n",
    "dense2 = keras.layers.Dense(64, activation='relu')(dense1)\n",
    "output = keras.layers.Dense(7, activation='softmax')(dense2)\n",
    "\n",
    "model = keras.Model(inputs=[input_a, input_b, input_c], outputs=output)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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ow4cPNjY2uDQ2MjLicnCvQKLjcLU/fvwYIRQZGUlugbHEvNenTx8zMzMNDY3g4GBlZeW0tDQySFNTU1pamk6nz5o1y9nZeffu3XV1dQJMKj+pqKjY2tpqaGg4Ojo6OTlRT4uysrKKioq4uPiwYcNCQkKePHnS2uEla9euraiocHJy4nWqm5CamooQ4jBU0sfHx9XVNTY2lu3dxaNHjx48eLB48WI81q5bt27Lli179OjRo0eP8A5NnqLHjx+/ePEiODgYdxfx8/MrKirCxR/39uzZ07t37/nz57cYdO3ataqqqiYfbQpbdN++fRsyZMjw4cOfP3++bt06thEmmpqaP378aG7SQsBPHZr3ucThmhQgS0vLvn37amtr437F1DPTu3dvcXFxFRWV3bt3Kykpbd26tVVH7tGjR25uLq5CdbTq6uqPHz9yKBL19fXXr1+fnZ29YsUK6vaampqIiIjJkyfj8T4SEhJbtmz58eMHtc/I8OHDdXR0+vfv7+/vX1FRkZubW1tbu3fv3j/++ENfXx8h5O7uTqfTqVP9c+PmzZu5ublNnlW2oJycnJ07d/7zzz+tOr6gopswYYKjo+PmzZtDQ0OHDh1KDcL/IHz/AgLXcXm/RXgUpZ+f36RJk6ZNm8bbg7dTh9af79+/z7elEFJTU+Xl5RUVFZsMlZKSOnr0KEEQ/v7+bK9zN23a1KdPH9xJCiE0f/58bW1t6kD0Jq+cHTt29OrVy8PDAyHUq1cve3v7w4cPtyrBRUVF58+f37hxo6ysLOcgJpMZEBCwf/9+6kiN1uJbdByuduEvEjtDk5iKTqc3N1rAxsbm169fX79+5XOShAHn04IQIvsMCyE8FIE6hUxj+/btU1RUnDt3Lu7+gT179gwhRB3sjecexNvZkKcoJSUFIYQrfwihnj17IoS+fPnCfYKfPHly9OjRS5cuycnJcQ4qKipas2bN3r17uT+4AKPT1tZOSUkpKCiIjY0NDw8fMWIE9daC/0GddZTU70sgeZ/DNSkk8EoBTZ4ZaWlpMzMzYS4SS0pKCILgXCQuWrTI2to6KioKP5jH0tLSKisrqUWirq6uvLx8c0UiQojBYHz48KG6uposEiUkJNTU1FpVJBYXF8+ZM2ffvn0DBgzgHEQQREBAwK5duxrX24Qzuvj4+C9fvqSnpz99+tTQ0BB3jsCgSBRO/M/769evT09PLy0t7datm5GR0fXr13l7fF75revPP3784FwkWllZLVq0KDU19e+//6Zuf/bsGduUQCYmJpyLRIRQSkoKWSQihHr27Il76HCZ2oaGBn9/f3d398azwzYO2rBhw9ixY/ESIW3Dz+g4XO3CXyRKCDoB/IOHCRUXF7dqot1Ojzwtgk5Is6qqqhBC4uLiHPbR0ND4559/Zs6cuWDBAnLOWzz5Jy7FMFxB5zwX669fvxBCixYtIicVcHBwUFFR4TK1r169mjp16o0bNxoXKI2D5syZY2Njk5CQgL8mJSUhhB49elRZWdnkKEGBR4f1798/LCzM19f34sWL5Atn/A+qrKzk/jhAsDoo73O4Jn8XSkpKwnzb5qZIFBMTO3LkiIWFhb+/P1kFbFwkIoRkZWW5KRJPnDhBvgPX1dXV09PjMrU/f/4cM2bMihUr/P39WwyKioqqqakpLCyMjY0lo87IyIiNjR0xYgQ3Y335HB2moqKyd+9eTU3NzZs379u3D2+EIvF31HF5X1JSMiws7MyZM0uXLh03blxHRNFxhL/+XFVV1aVLF877rFu37sqVK+vXrx8zZgzewmQyq6qq2IpEOTm5qqoqzpNK/vr16/Xr18OGDSO32NjY1NfXc/NmlSCImTNnKisrN+5r0zjo1atXBw4c2LlzJy6jEEIVFRUsFis2NtbU1LRv377CFh3W5NUu/EWiCDWJ8VSQmpqaeAQFwMjTIuiENAs/v6+treW824wZM6Kjo0+fPm1ubo6HfHTt2hUhVF1dTe6DsyLe3hz8HGv//v0WFhatTWpSUtKMGTOuXLliYmLCTdDLly+/fv164MAB6p7r1q2TlZXlptTgc3RUeM6MT58+kVvwP0ho3wqCxjoi73O4Jn8jJSUl5JM1IcRlkWhkZLR69eo1a9asX78eZ9jGRSJCqLKykpsiMSgoaPHixa1NalFR0bhx40JCQsh+iZyDXr58+fjxY+qbbYTQxYsXL168+OLFi0GDBglVdFQ9e/YUFxeHIvF319F5X0tL6+HDhx13/A4i/PVnbqoxeMTHsGHDZsyYgbdISEjIyso2LhLl5OQ4P3NUUFCwsbG5du1aa9PJZDKnT5+uqqq6Y8cO6jT4zQW9evWqsLCwcYHm4eGxbdu2FlcW4HN0bNiuduEvEjtbx2kOUlJSunfvrqmpiZ/iNDkogu2KEQUpKSk0Gm3gwIEIISkpqebGigjwzKipqSHuHiwdOnRIVlaWHKiA58rDM0ZiePoHa2trDgfBr7Zw9+lW+ffff+fOnXvz5s3GjYHmgrKzs6kj+8n5rrj5sfyM7uLFi0ePHqVu+fz5M0KoV69e5Bb85grPZgl+CzzP+xyuyd8Ig8F49+4dnidTOG8WKioqNBqNm1JixYoVpqamZJFobGwsJydHLRKzs7MrKys5F4l9+/al0+ltKBJzcnLGjh27adOmxhWs5oKOHz9OLaOo81212EDlZ3QlJSX4wSvp69evLBYLisTfGs/z/tixY9m2fPnyhXqR/C6Ev/6spqbGTZE4dOjQoKAg6nBWW1tbapGIEHr79i3nIhEhZGZm9vr169bOJ19XV+fh4WFkZLRz506209Vc0PTp09mmgCLnu2qxgcrn6Fq82oW/SBSVt8SPHj26devWrl27xMXFe/ToIS8vf+bMmTFjxtDp9C9fvpBDIvE6Wi9evPDx8amurhbmhxk8UVBQEBkZ6e7ubmxsjBAyMDBITEx8/Pixnp5efn5+YWEhuaeysvKbN2/q6uoYDIa8vHx4eHhERMTFixf5MMOWubk5QigrK6vFPXV1dTdu3Lhw4UL81c7OzsHBISIiwsXFRV5evri4eNu2bQ4ODvb29hwOMmTIEFNT0zVr1ujr6w8ZMgQhlJubq6amRhDEgAEDZGRknjx50vjCePz48YQJExYvXsz21NDT0/PDhw/NBeHWfpOqqqqEJzpZWdkzZ874+flJSkoihL5+/bplyxYTExO8ygX25csXNTU1Ye5uAKjanPebOyCHa5LDhSeEIiIiKioqVq9ejRAyMDBACEVFRa1Zs4bBYOCxBhi+WTx//rx79+719fVlZWVWVlb9+vXjwwxbMjIyBgYG3BSJkpKSR48eJat30tLSS5Ys2b1798KFC/v168dkMkNDQ9XU1GbNmsXhINLS0gEBAfv27Rs2bNiUKVOkpaV//PhBEIS6urqXl1dCQsKtW7ca96kpKSmxt7cfNmxYRkYGtcZpb2+vpaXVXFDj0b9UwhOdrKzs/fv3f/78iTuUVlVVLV26VEFBYcmSJeQ+eLh1G3obAUFpW95n63ZLVVpa+vLlS/x4hSCIf/75Jzs7+/Tp0x3+S3iqzfXnESNGfPz4kT8zbJmbmz979qyoqEhdXZ3znn///feNGzfI0SIrVqxwdnY+f/48HgUWFRWVk5PT4vSBwcHB7u7u8+fPDw8PV1VV/fXrF150MCoqCi96gmcvZzNp0qTS0lJ5efnIyEhyo66urouLC4cgDskQquhavNp/gyKxdRNUtwkP11lZvXp1nz59unTpsmDBgmfPnuF+XGZmZufOnWMwGMuXL6fT6bq6uqdOncKTyJuamo4dO9bGxsbQ0HDnzp3kcU6fPt2jRw85OTknJ6fo6Gh1dfX+/fvj6fVnz55Np9N1dHSo+zdp+/bt06ZNw/9+CwuLqVOn4vnr2w+1cg2GvXv34iENkydPvnTpUlRUlLq6uqqq6rp16wiCOHDggJ6enrS09F9//UUQhIGBQc+ePceOHTts2DB9ff3g4GBy4vXU1FRjY2NpaWkLC4uIiIixY8cqKiriZVROnDjRtWtXdXV1X19f4n+LMLV2uaC2LcJEEISZmZmNjQ359fDhw2PGjJGWlvby8rp58yZ1TxaLNXjwYHIRprKystmzZ/fv39/Ozs7Q0DA4OBgvyMT5FOXn5/v6+qqpqdHpdH19/RkzZpSVlVVXV+NF2xISEhqnGS9g0NiLFy84BLEdhLoqklBFl5GR4ejo2LdvX2tra2trawsLi7CwMHwmsfr6+q5du+Jrg/pfg0WYmtPaPN4c/uf95nB/4bVBa9dgyMjIWLx4sZycnI6OTmhoaFJSEh4tP27cuGfPnn3+/Bm/4hs6dOjTp0/xeh7Dhg0bM2aMqamptbU1dXm5BQsWKCoqqqmp+fr6Hjt2DCE0atSo5OTkX79+2dnZ0el0Y2PjxMREvAiTs7Nzq35XmxdhWrBggaSkZEVFBf764MEDb29vOp3u7Ozc+M61fPlychGmhoaGbdu2mZiYDB48uH///hMnTszIyCAI4s6dO/jh47x5875///7vv/86ODgghKZOnVpQUMBgMFatWtWrVy8pKSktLS1XV1e86BeecLXJdWWaW85927ZtHILYDsK2KpLwREcQxPjx4/v27Tto0CAbGxtzc/M///zz8+fP1B1cXFy0tbVZLBb1vwaLMDWHV+us8D/vc0jMmjVrjIyMcAXGwsJizJgxDx48aP9vJNp06+R//dnR0VFLS6u1P42b67/xIkx4OZ9bt27hr3l5eQEBAZqammZmZnPnzq2vr6f+eXx8fNeuXcmv169ft7GxsbS0NDc3t7Ozi4+PJwgiMzOTw5VDEMSpU6cGDBjQpUsXNTU1Ozu7S5cuEQQRFRWF92ky2U3OSuPi4sI5iA11VSShiq7Fq33btm00Gq3xf63Jo1HxbRGm36xJzD2cpffs2cPneHmiQ++IBgYGLTZUOkibm8THjx+n0Whfv37tsKRxpbS0VEZGhm09XoiOIIgbN26IiYm9fPmSuhGaxBwIpNYrwLzfTh16R8S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rjo6Obm5uHh4eM2bM0NLSmj59OpPJ5P4gdXV1Hz586Nevn6WlZTt/EQcHDhwYPnw4LqfYhIaGmpqabt26NTU1tXHo27dvjY2NZ82adfHiRV9fXyMjI5wbGxoaZs2a5e3tPWvWLFtbWycnJy0tLfJValJSUu/evUNDQw8ePKilpXXy5Mk2pDk/Px83BloM+vz589GjR9kqbcIcXVFR0adPn8aOHUtuMTc319bW3rdvX5uPCVokISGRmJjo6Oi4dOlSBwcHFxcXMzOzoUOHMplMCQkJnuTlFtNQWlqqpqa2bdu2O3fujBw50s3NraGhAZdvjo6O9+7dw0WipKRkfHz8qlWraDQai8WaPXv2gAEDzp0798cffwwfPvzHjx8SEhIJCQmOjo6XLl1yc3PT1NS8cuVK+09Rc/m9rq5u4sSJ+LEOjUZjMpkLFy78888/GQzGjh070tLScnNzb926lZaWxmQyvb29x48f7+TkNGfOHCcnJ21tbXy/4P4gCKGAgAALC4vjx4+3/0c1af/+/YqKig4ODo2DHBwcAgMDHz9+TN4TqcrKysaPHz98+PCYmJjw8PCePXtGR0cjjjdchFCT97XWpjkvL49Op+PHKJyDGAxGYGDgoUOHqLVPIY/uyZMnZmZm+DkUQkhSUnLs2LHHjh0Thh5MnRiHWzmv8nKLlixZMnr06GvXru3Zs0dTU/Pff/+l0WglJSUjR46cNWtWXV0djUYTExNjMBjTpk378OGDuLh4k3UMFos1a9asxYsX79mzR0tLi9onrs2ay++I66oyr+rbBw8eHDhwYFBQUPt/VJPOnj1bXl7u5ubWOEhVVXXPnj2lpaVz5sxpHNpcXZrDDRch1OR9rbVpxg+Om6y5NQ4KCQlZsGABWcK0AZ+je/LkCZ1OHzZsGLllwoQJb968EeoXxXzonN3mscSrVq0iv7JYrIkTJ4qLi//69YsgiMDAQHIkAIvFmjp1qpiYGH7GM2XKlG7dupHH3LZtG0KouLiYIIhJkybp6elRY+Q8lvjChQvR0dH487x586SkpOrr6wmC2L59O0IoLi6O3NPIyCg/P58gCDc3N21t7aqqKoIgnj9/jhCKjY0lf0tkZOSbN2/mzJlDjkFqEuJiKMXevXvFxcXT0tLw18ePH9NotKioKIIgDh06hBAio3jx4gVC6MKFCwRB7NmzByFEjqrC4zHIcYOHDx9GCB08eLBVB6msrJwyZcratWs5J5ho61hi3AMwNDSUuk9OTo6Ojg6ZNnFxcTMzMwaDQVDGEtfW1mpra3t7e+PdmEzmmDFjtLS08DgTOzs7CwsLJpNJEERZWZm2traVlRVBENXV1d27dyfTuXXr1i5duuTm5raYbKqXL18ihMihGhyCmEymg4NDSkoKQRAXLlxAXA/u5X90VVVV169f37t376BBg3x9fXFOJLm6usrIyODzicFYYg64yeNEo7HEeAIM8it+o3Xz5k2CR3mZaGkscXFxMTmG/OLFiwihhw8fEgSRnJyMEFqyZAm5Z2BgYExMDJnIt2/fEgRRU1OjoqISHBxM/pbAwMCkpKQTJ05QxyA1xk3RwTm/a2hoeHp6kju7uLgYGRnhzyoqKgEBAWSQgYEBOW6wtLTUxMSkV69euGzh/iCxsbGurq6cy3miHWOJe/bsaWtry7abnp5eVlYWQRAVFRVaWlqysrL4K3Us8eTJk/X19SsrK/HXjRs3SkhIvH79muB4w23uvsa92tpatlPEIWjZsmV79uwhCKK4uBhxPbhXINElJCQcP37czc1t8ODBHz9+pAbhegJ1qgsYS8xBm8cSN3crJ3iUl1scSxwcHFxQUIA/m5iYDB8+HH92dXWVkpIqKSnBX/Py8oyNjQmOdQw7OzsNDY2YmJgPHz6MGTOGw0ngsujgkN+5ryrzpL6dlpY2btw4bubF4Ob6bzyW2MfHh0ajkaOIsYCAgGPHjuHPuLWM70rUscQc6tIcbrjN3ddaZdy4cQYGBtRaU3NBN2/eJC8YIyMjLgf3CiS6N2/enD9/fsGCBfr6+jdu3KAG4XrC5s2byS0wlrjVcC8LMTExZ2dnFouFx+pQiYmJhYeHNzQ0HDlyhLdRu7m5kVPmamlpMRgMPGPn1KlTpaSkTpw4gYNevnzZq1evHj16lJWVxcXFTZgwQUZGBiFkaWmpqKh49epV8oABAQEmJiYRERH6+vrtTNvBgwctLCzIXv52dnYmJiYHDx5szzH9/f11dHRaexBZWdnTp0+vWbOmPVFzgJ/U9ujRo7kdBg0atGTJktevX7ON/7l169a3b998fX3xV3Fx8ZkzZ+bk5JCT90pKSuLXAoqKira2tvjOd/fu3cLCQh8fH7yPk5NTXV0d25SGLdqwYYOTkxN5EA5Bf//998iRI9vZwZI/0VVXV//7778PHz6UlZXt2rUr7iJF6t69e3V1Ne6jCzoU2fFs9OjRCKEmp11tW15ukYqKys6dO/FnLS0thBAukC0sLMzNzc+cOYPnOmYwGAkJCePHj0cInTx50tTUFA8qk5aWtre3pxaJEyZMsLa2njp1qp+fXzvT1mJ+bwMlJaVFixZlZ2ffuXOnVX84adIk3MmozVFzUFZWlp+fz6FIlJeXP3DgQFVVFdvw5tLS0tjY2IkTJ8rKyuIts2fPZjKZR48eJfdpfMNt8b7Gjf3790tJSW3atKnFoMePH7969Wru3LmtOr6gonv8+PGDBw8qKip0dHTKysqoQXggAJdvGkF7NHkrZ9PmvNyibdu2kYM+tLS0yDrqjBkzGAwGbrwhhM6ePTtt2jTUUh1DU1PTw8Ojb9+++Jl1e3CT31urzfXt/v3742KkzVFzlpaWpqSkxGHsz759+7p27RocHMw28X6Ldekmb7ic72vcSE1NvX379v79+xt3TmELKikpWblyZTt74fEtunfv3t2/fz89Pb1fv37kIFNM+ItE4R1L3Bjuktfkyjq6urpdunT5/Pkzb2OUlJSsra39+PEjHp6H/tctXlVVdcKECVevXv3586eSktKZM2f8/f0RQpmZmQ0NDTU1NXikCkKITqfj/opU3AzYa9G7d+/Yxm3q6+vHxcW155g0Gs3AwAC/RBIeP3/+RAjh2lhz1q5de+XKlQ0bNlCnT8PrJfTq1Yvcgp9EpKWlNS6XJSQk8KWFJ+UvKCjA/0Q8CrfxP5GDyMjIT58+PXjwQEyM/ZETW1Bqamp8fDx5tbQN36JTVVWNiIhACNXV1bm5uQ0cOPDdu3cqKio4FN908T8L8AeHIrGD8jKNRpOUlPz8+XNJSQnuk0z8rwPtzJkz58yZc/v27bFjx8bHx48ePRoXdB8+fDAyMiKvurq6uvz8fOoqQRhZaWuzVuV37uFmLc9vLu3BTZE4ZswYPz+/U6dOHTp0CD+8QAi9f/++oaGBeoqUlJSUlZWbrKOQVxeX9zUOnj9/vmXLlmvXrlHna2gy6NevX/Pnz7969Wp7VubgZ3R//fUX/rB27VobG5vExERyghkoEgWCvJU31kF5WVpa+ufPnzk5Ob9+/cKdLPB2FxeXnj17njhxAj9wiYmJwTU0LusY7S8SW5XfuddB9e12+vnzJ+cisUePHtu3bw8ICJg/fz61Pc99XZp6w23uvsbl4IuSkhIfH589e/ZQ+xU3FxQUFLRu3br2rN/Gz+i8vb3xDK8JCQlOTk5fvnzB/Y/Q71Ak/k5NYs6kpaVramp4cqjq6uqSkhItLa3ly5efPn168uTJOjo6hYWF1H1mzJgRExMTHR09a9ashISErVu3IoQYDAZCKD8/n8wngYGBXA67bxUWi4UHEFI34n7djeuarUKn03l1GnkFn1XOVRZpaekjR44MHTrU399/6NCheCMexEU9S1JSUuR2ztG9f/+enIY3LCyMy2XZEELHjh07fvx4QkKCsrJyi0GzZ89WUVEhpw3D95jIyMj4+Hjc6U7YosO6dOkSHBzs6up66dKlmTNnUoPw2QPCgLd5OSMjo1+/fufPn8eTlBgbG7MNnfLx8QkJCTlx4sTYsWPPnDmzcuVKvJ3BYFRXV5NForW1tbW1NdEBk5O3Lb+3CM98I1SlIjdFIkJo586dd+7cCQkJIXN341OEEJKSkuKmSGzzfS01NdXX1/fq1auN55toHLRx48aGhgY8bQf633Qvt2/fLisrW7Zsmba2trBFR1qyZEl4ePj+/fupc64iKBKFCW/zcn5+Pp7fxNPT89u3bxMnTlRXV//16xe5g7i4+PTp0zdt2oT7GGtoaOC5ndpZx+Be2/I7N3hY3+YVBoOB53PiwN/fPzo6+uzZs+RD0jbXpdtzXystLR07duzixYvZqk9NBt26dSsxMbFbt25kP4KCgoLS0tLg4OCJEyeOGDFC2KIjDR8+3NzcfO/evWSTGBPmIrGTNInr6urKy8upD8Pa48GDBx8/frS3t9+6deu5c+fwWjVMJvPSpUvkPiNHjtTV1T1x4oSent6IESNwVsRTLtvb269YsYInKWmOuLi4mpoaWzetkpISNTW19kwQghAqKiri1WnkFQUFBYRQdXU1593s7e3nzp0bGRmZnZ0dGhqK/tfXmnqWcIcZzhPb4n+ir6+vjY1Na5O6Z8+ea9euJSQk4DS3GNSrV6/CwkI8qyFCCI9ky8rK4vKOxbfoiouLxcXFqa1u/LSPungP/gc1TgkQFB7m5aqqqlWrVl24cCEwMNDLy2v//v0IoZcvX+JHgZiioqKHh0d0dPTXr1/z8vLI9WzU1NQ0NTXDw8N5khIO2pbfW4QvcqEqFbksEpWVlaOiotzd3VetWmVra4uaOkUEQfz8+ZObIrFt97UnT57Mnj376tWrhoaG3AR17dq1a9euZBlVX1+PECoqKnr37h03VXC+RVdfX5+Tk9O7d29yi7S0tLi4OBSJwoy3eXnXrl0+Pj737t2Lj48vKCjAmej27dt4IQ8sICBg8+bNJ06cEBMT+/PPP/HG9tQxWqVt+b1FvK1v84qCggJbj+gmHTp0yNjYODg4GH9tc126zfe1wsJCV1fX0NDQxivCNhdkZGREllEIodraWiaT+e7dO7anbwKPDj83p26RlZXF9UxM+IvE32AsMTfwEqm4o7+srOyvX7+afMAjJibGzUvUV69eycrK4tfCPXv2bHIfGo3m7+//7NmztWvXkiWdvr6+uro6z0eqNMnJySklJYX8OXV1dS9fvhw5ciTi2DkBd6Bt7iT8/PkzJSWFPI3cHyQzM7PjljbFU96xjUlo0ubNm3V0dMiU4LNB7Tv6+PFjhBDnB114OYEmBw9nZWVxWI1548aNDx8+vHHjRuMM31xQdHR0IgVe1WP79u149KPwRPfkyRO2uXPxrF1WVlbkFjy0mMP4RsBPbc7LTXrz5g2dTq+trS0vL2+uSEQIzZgxo66uzsfHh7rk9eDBg589e8ZN/m0nzvldVla2uf5anO8L9+7do9PpuO8J9wepqalJSUlpy8/ggqqqqpSUFDendNKkSZMmTSKLxH79+mlpaVFP0YsXL+rq6vCpaw6H+1ppaSmHTph44ZObN282bqA2F7RixQpqGYWH5/n5+SUmJhoYGAhPdFVVVQsXLqRuSU1NZbFYjYtEDvkF8Fnb8nJzyIqihIRE4y76WO/evYcPH3769OmEhARyjQYOdQze4pzf21xVbnN9OyUlpePeLWtoaPz69au5PvMkHR2dzZs3U+urHOrSHDR3X2MwGC9fvmzu7H39+tXFxWXbtm2NG6jNBY0ePTrx/9LV1TUxMUlMTPT09BSq6IKCgqjnv6am5v3799TeOsJfJP7Gb4kbGhq+f/+uoqKSnp6+ePFivAAJQsjGxubgwYP+/v6jR48uKyuj3sh1dXUvXbp09uxZJSWlwYMH46XVEEKVlZX4Q319fUlJSWJi4vTp0/ESdhs3biwvL8/Nzb1+/TpbAv7888/w8HAGg0GuHYdnqw8MDPTz85syZYqMjMz379/19PQsLCx4/vPXrVt3+/btRYsWhYWFMRiMpUuXiouL45V1LC0txcXFFy9ePHfuXAaD8ezZM+oZQAjt27fP2dmZ7BVWXFxcV1dXWlo6b948JSUl/CqA+4OwWKz+/ftraGi0anQZ9ywsLMTExKhPXpsjJyd36NAhZ2dn/LVv374LFy7cuHGjmZnZwIEDHzx4sGXLlkWLFnFeadDY2Njb23vTpk0EQQwbNqyhoSEnJ2fMmDESEhIGBgaSkpIfP35sPDH9+fPnw8PD8aLw1O1bt2598eJFc0HUlwxsSktLhSc6WVnZs2fP2traGhoa1tfX37t3b9OmTVOnTqWuEpGZmdm7d29yaDEQiHbmZXLav5qaGlwqslis8vLy69evy8rKysjI9OvX79ixY0ZGRjU1NXg2Tip7e3tDQ8Pk5GTqQKxVq1bFx8ePGjUqJCSke/fuP3/+FBMTc3Fx4flv55zfbWxszpw5s2HDBj09vfz8/MzMTLKXna6ubkJCwt27d1ksFq7nVVdXl5aWysnJ3b59e9++fStWrMDPerg/yIwZM86ePXvkyBE8zQRvSUpKDhgwgJsiESEUFRV1//59/Bkvl+Xj43P8+HEPD4/s7OxZs2ZZW1uzrVHJhsN9zdnZOTk5+dKlS40XJP/27Zurq+vgwYPZmo7Tpk0zMzNrLsjV1ZVDSoQnOllZ2YcPH16/ft3S0pJOp7969So4ONjMzCwkJITcJzMzEyHUocsTgha1Py/jjSwWi6wo1tTUfP/+PTU1VVZWdtCgQUwmMyQkxNHR8e3bt5mZmWwjGmbOnOnl5eXt7U32zm2ujtFcu7rNOOd37qvKiBf17ZiYmFmzZvn5+bVtVcsWWVpa3r9/H69FwnnPuXPnnj9/Hs89gTjWpTlo7r62cuXK7du3L1iwgJyHkmr06NESEhKRkZHUFfIGDRq0YsUKDkEckiFU0dXX1+/atWvixIlqamrZ2dlhYWENDQ27+8eJ5gAAIABJREFUd+8mdxD+IlGcD/3Z0tLSYmNjOUeUnp6+dOnS0tJSHR2dO3fu1NfXf//+/datW+rq6u/evbOwsHj27FlsbKyKisrnz58HDRqUmJj4+vXroqKiQ4cOJSYmenh47NixA5c4pqamdDo9PT29uLjYxsbGysoqPz8/IyPDysrK1ta2qKjo+fPneC2lsLAwMTGx7t27n/2f8+fPX7t2Da8/YWJiYmZmlpaWlpKSoqen5+Pj8/nz5w8fPlhbW+NB/AoKCs+ePfP09KT+gwcOHGhvb5+Wlnbnzp309HRZWdnhw4ffvXv33r176urqqampioqKLY5QWrt2rYeHR3PLzGLKyso+Pj4pKSn79u2Li4szNDQ8evQoLguUlZWNjIw+f/786dMnPT09Ly+vt2/fFhQU9OrVy97ensVivXr1qqCgwNbWNj8//+LFi/X19dHR0RcvXuzfv/+xY8dwpxruD6KhoZGdnT1q1ChyEG9z8AyKkydP5rxbZGRkz549yWdXUlJSz549e/369bx58/CWI0eO7Ny5k06n3759W1ZWtk+fPuTf6unpMRgMAwMDPH/g6NGj8aJ/R44cycrKWrZs2aJFixBCe/fuzcrK6tKlS3Z2toODw8GDB9+8eaOkpPT582dHR8cJEyZoaGgkJSXdu3fv27dvenp6VlZWdDo9OTn53bt3rq6ujbsMZWZm5ufn19TUVP9fY8eOLS0tbS6IbQKD4uLirKwsNze3nj17dunSRXiik5OTq6ysvHnz5unTp+Pi4n79+rVu3Tpq/bKiomLp0qUBAQHUZ6v79+9XVlbm/O/mpnDolFrM47W1tfPnz09OTtbS0nrz5k16erq2tvbevXsVFRVzcnI0NDTq6uq2bdsmJydXUlKiqalZVlbWzryso6Ozfv367OxsbW3tf//9FxeJ0dHRly9fzsnJcXR0tLKyGj58+NevX588edKlS5fly5enpKQUFhbi+fZxsqurqxUUFKZOnUr+kB49eri7u3///v3OnTvJycl1dXUjRowoKCg4cOCAoqLi169fKysrzczMOJ8uLouO5vI7Qmjo0KFVVVWvX7+ur693c3OTkZEpKSnJzc0dMmSIubn5p0+fUlNTlZWVBw0aFBkZWVZW9vbt28OHD3/69GnlypWzZ89u7UEQQjU1NTNmzOD8kIjL6//69esfP35cvHgxueXnz58XL16cMWMG7gny8OHD0NBQMTGx+/fvFxUVUTtkysnJ6erqEgQxfPhwhJChoaGTk9O1a9cOHDjw5MmTiRMn7t69W1pa+u7duxxuuA4ODo3va9LS0mVlZQkJCQYGBo1nbSktLU1OTiYIgq0gGjBgQM+ePZsLwjO4kurr658+ferg4IAfKwtPdOLi4l26dHnw4MG5c+fOnz+flZXl7e0dGRlJneAnLCxMQ0NjyZIl5JZbt269efOGXO25OdxUADofbvL4tWvX/vrrL2VlZTqdfu/ePQ0NjWvXrnG4lbc/L79+/Xrnzp3du3dnMBhkRfHChQs3b95UU1P7888/BwwYIC8v/+LFi8+fPzs5ORkbG+fm5ubk5JDVob59+0ZFRUVGRuL+0liTdYwdO3bk5uZ26dLl7du3BgYG5DubJnFZdDSX3xHXVeUhQ4bcuXOnnfXtIUOG4DvXlClTWpyHn5vr/927dxcvXpw/fz45nqtr166HDh2ytLTE76jy8/PxBOPv379PSkpydnYm5x+l0Wj29vYZGRn4tUFzdelPnz5xuOFaWlo2vq+pqKiIi4vjziZNPme8e/eumJgYW0GkpqY2cuRIDkFsB3nx4oWOjg7udCBU0amoqCQnJ8fExJw6dSo1NdXCwuLUqVPUleGPHTuWnJy8f/9+PKofIZSRkYFf8DT/r0aI6woAD/BhoaeOWHo0MDBQSUmJt8dsAxsbG3IVZR5C/FqWEC8PgBep44O2rUtMEMSjR49oNNp///3XYUnjSkNDg7KyclFREUTHZv/+/QoKCmyrN8O6xBzwPI/zOS83Jzg4uA0La7eIb8sSEgRhYGAwceJE/sTV5nWJS0tL1dXV8YsmwRozZszVq1chOjbp6ek0Gu3atWvUjbAuMQcdkcf5mZebU1BQMGzYMJ4flp+3Tj7Xt7m5/huvS0wQhJOTk7Ozc0cmjSt///33okWLIDo2dXV1Ojo6S5YsoW6EdYl5psUxAx2E7K5248YNS0vLrl27CiQZPCSoM8lBdXU1dVY6e3v71atXL1y4EM8IKhBVVVUhISErVqygPuuF6BBCBQUF69atO3z4MLXHdVVVlTDPK9hZCSQvFxYW4i6F379/T0lJaXEUlvATwiKRxWJRVwJXUlI6efLkjh07srKyBJWkhoaG06dPS0tLc+5+LILRMZnMuXPn4mn5yY319fVkz1vANwKvKG7fvp3s4Pb7EsIiETWaIOPYsWNv377FrSxBefbsWXx8/PLlyyE6Nhs3blRTU9uwYQO5hSAIPkwy0iq/8VhiQdm1a5e1tbWkpOSmTZvu3bsn6OR0QlZWVt++fRszZoyWllZwcDDuhbhu3Tp5eflJkyYdP36cP61ENjdu3HB2dh41ahRER5WRkeHv77937148sgghFBcXd+7cuYKCAnV1dVHr+yeaHj9+nJSUNGrUqLCwsE2bNgk6OZ1Q3759DQwM3Nzc1NXVJ0yYgFd9HDVq1Llz5zw9PQ8cOGBubs7/VL1+/bqysvLChQvtWUO480VXXl4+c+bMwYMHk8MR09PT//nnn69fvzKZTDs7uw5ILBA68+bNW7Ro0evXr9++fbtt2zZBJ6ezUVdXd3BwWLp0qYKCgoWFBe55q6Gh8fTpUy8vr9raWlxI8ll9ff21a9fwkBOIjtTQ0LB58+aUlJSEhATcab+2tjYkJOTLly/V1dUODg4dk962oBEdsD4km5iYGE9PTx5GdO7cuejo6F+/fhkZGU2ZMqWjJ7JngweTqKiorF69mjqKlYdoNNr58+c7ut/8ixcvdu7cmZeX16tXr1GjRvGhBPHw8ED/GxXQNm/fvs3LyyNnvAACd/bs2WHDhrV5CkGeFw6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zM7NZs2aVl5fn5OTU1tYeOHDA19fX0NAQIeTn5ycvLx8RESFitGPHjnV3d9+2bdvatWtdXFwa7/D77783vkX83aOio6NzcnLCwsLwYnZ29u7du3fs2NF4T2tr64ULF65evdra2nrIkCE1NTUCN6ygdGo7kpQXNQmSJQTJEiRLnRNUidvQ0KFDX/2fBQsWNLebhYUF+b+0tLQo7T2ePn2KEMLpCEKod+/eCKGMjIzGe8rLy4ulw8br168rKytxEY/p6+srKyvjweJ/mJ2dnZSUVJs2YRKjoqIi/nFrGhs0aNDSpUufPXu2fft2/vUPHz7kf+kQQv3792/ypZOXl0cI4bfs6dOn5FuMEOrduze+4SNKqKGhoWlpad++fdPU1DQ3N4+KiuLfWl1dvW/fPjx0jehH8Xi8WbNm+fn5zZs3D6/ZsmXLqFGjBgwY0DiAc+fOjR8/Pi4uLjMz8+vXr2fPnh03bhzZ6hIhhF9J3AMHtKnz58/jUuj58+cyMjJN7iMrK2tkZIT/xwOHiFgQiXKJ8l/VbQF/lPg/Ynhc01aWTg4ODgihTlE64XtHwkunpUuX2tvb79+/H99zwFpUsJPv47t376qrq8m3XlpaWl1dvckvoCbFxMRkZGSkpaXdv3/f1NT08+fP/FsfP3788eNHPz+/Fh1VWFg4f/78gwcP4noUQRABAQF79uzB+aiAxYsXnzlz5uPHj+np6cXFxRMnTrS3tydnUkFQOrUlScqLmgTJEoJkCZKlzkma6gC6isDAwCZ/MCMIIjw8/OLFi5qamvLy8vinte+eraKiAiG0dOlSso++q6urqqqqeGPmh4f3xGUQSVFRsZWjmNLpdCaTiVu1dXxVVVVycnLC99m8efPly5dDQ0NHjhyJ13A4nKqqKoGXTklJqaqqSvi4IBUVFc+fP3dzcyPXODg4NDQ0iD4+p4yMTHBw8KlTp1auXMliscj1R48edXBw4B8H8rtHEQQxe/bsHj16kPeCUlNTDx8+vHv37vPnz+M15eXlXC73/PnzlpaW69atc3BwIIMfN26cnZ3dhg0bJk6ciNdISUkhhCorK0V8LqD1GAzGxo0bm9z05s2b9evXFxUVaWpqiv6hbu4SbX2oLdK4dFJSUkIItbJ0wh1QO0XpVFVVhf7vM9UcOp0eERFhY2Mza9YsMg39sYIdfwH9/fffuCkjQkhfX9/AwKBFMauqqh44cEBLS2vbtm24rT62bdu2X375pbnn0uRRJSUlI0eOXLNmzaxZs/Ca/fv319TU5OXl4dIJB/z27dvz588bGxsfOHDg119/1dbWxi9LaGjo7t27//jjj8OHD+PDoXRqH509L2oSJEsIkiVIljonqBK3Ez09vSbX//vvv6tXr3706BHunc9isT58+PDds+EfjQ4dOmRjYyPWMJvVrVs3hFB1dTX/ysrKSrz+h3G53LKyMiHt/ToURUXF75ZKuAGhm5tbYGAgXiMtLa2oqNj4pVNSUhKewjKZTAcHh6tXr7YybG1t7du3b5OLHA7njz/+wEP8i3gUh8OZMWOGmprarl27yPFdU1NT8/LyyAZOpAkTJoSFhX38+BHfruE/4ZMnTwiCwGeora1F/1d1Ae3G2tq6yfXe3t5mZmZJSUk0Gu3JkydkVUc4cV2irdS4dMKf01aWTnhM405ROuF7ofgzJYS5ufn69es3btwYGhqqo6ODfrRgx19Ac+fObXzzpEV69+4tJSXF/5X39u3bpKSkkydPin5UQUEBi8VasWIFf1n05MmTO3fu8N8PRwhduHDhwoULO3bs4PF4vXr1ItcrKCj06NGDf/QaKJ3aR2fPi5oEyRKCZAmSpc4JGk5T7Pnz59LS0k2OVicwdyj/Im59gZsJtQ8LCwslJSX+7oWZmZmVlZX29vYIIVlZ2bq6uiYPFD4DampqKh4SozUnaTfq6uqi/FDn4uIyd+5c/o4fgwcPFuiZ+fLlS/zSCTFgwIDnz5+3dNTTUaNGCazJyMjgzzxOnTrVs2dPZ2dnEY+qq6ubMGGCubn57t27+d+IGTNmCIxMQI4YsXLlSi0tLYHfs/Pz87W1tckz4PtaAv1zACUqKyszMjLwsKUCm4SUQuhHL1GxwwN48n/E8EizrSydnj59SqPRBg4c2JqTtA9VVVUajSZK6bRmzRpLS0uydBJesDfHyMhIXl6+pV9AxcXFeEQcUlZWFpfL5S+dtm/fPnv2bP7kT/hR2dnZo0aN2rp1q0C6eeLECf6iiX94LV9fX/S/N//r6+tLSkr4B4aF0olanSUvahIkSwiSJUiWOieoElNMV1eXw+FcunSpoqLi3r17Hz9+JDf16NHjxYsXdXV1uDkQ/+KQIUMsLS03btyYmJjI5XK5XG5WVpbAT2vixWAwli9fHhkZiUsrDoezdu1adXX1OXPmIITw+HhHjx7Nzc1NS0t78+YN/7P4+vXrly9fampqBLoDcTicTZs2aWtr49Zuop/k+vXrTCZz8+bNbfd8m2RtbV1dXc0/6WVztm/fjm/CYGvWrElPT8eDVSKE9u/fn52dvXbtWuEnWbhw4efPnxcvXoz7kFRUVGRlZeHDlZSU8PicjX379u3Jkyf4f9z8LDMzc9OmTeSasLCwxkPXCDlq/Pjx+fn5ysrK+/g0N5cPKSgo6O7duy9evMCLycnJd+/exaNQYriLF7U/5wNMSUlJVVU1Li6uuLg4JyeH/83FkyU+evSIw+HU1NQILDZ3ibYzJycnV1dXPIAwQqiwsDA8PNzV1RWnMsbGxomJiXfu3MnNzU1JScnLy+N/dvxlLL/c3Nx9+/bhkcladJJNmzYxmUwRb7OLi4KCgrGx8adPn767p4yMzLFjx8hbLsIL9uYwGIyAgIAzZ84cPXq0pqYG1zlxwTh58mQNDY0mqyWKioq3bt0qKSnBi1VVVStXrmQymcuXL8drsrOzz507t2jRIhGPKi4udnZ2Njc3f/v2LX/plJqaKiR4fX19T0/P06dP4wauCCE8OC15qwpB6US1zpIXNQmSJQTJEiRLnZQYRq3uSkQZuPzixYv4ZyR1dfUpU6YcPHiQf+vevXuHDBmCEJo8efLly5crKipGjx4tLy/fu3fvFStWzJ8/H08iRxDE33//3a1bNw0NDX9//8aLX79+9ff3V1dXl5eXNzQ0DAwMLC0t3b9/v4aGhpqaGh5z//DhwwYGBgwGY8OGDUICFnFeAR6PFx4e3r9/f0dHRzMzs3Hjxr19+xZvKikpGT58uLy8fN++fVesWIEHNZ07d25RUVFqaqqenh6TyXR2ds7LywsKCpKTk2OxWF5eXv369RszZgyexqNFJ4mOjlZWVg4NDf1uzEis8wpcunQJIRQbG4sXv3z5EhAQoKWlNWDAgAULFjQ0NPDvHBMT061bN3IxKirKwcHBzs7O2trayckpJiaGIIj09HQ8jyiLxXr48OHHjx/x/RAXF5f79+8TBPHPP/9YWVnJycmpq6s7OTldvHiRIIj9+/fjfZoMcuPGjebm5gMGDHBwcLCxsRk5cmRSUhL/U9DX1+dwOKIf1WRfrNGjRzd+aP55BTgczp9//mlnZ+fu7j5o0KD+/fv//vvvXC6X3Dk8PJxGo5HvvlhI5LwCP3YNV1VVBQUF4VZ2Li4u8+bNw8ONYK9evVqyZAmelWft2rXfvn2LjIzs06cPg8FwdnbGX+Genp4pKSkVFRVOTk7y8vIWFhaJiYkCi0RTl+h3r+oWEfE9LS0tnTdvnpmZmZOTk6mp6cKFC/GETARBPHv2zMLCgsFg2NjY7Ny5c9SoUSoqKr/99hvRqFA1Njbu3bv3qFGj3NzcDA0NFy5cSM6fIfpJ8CRM8fHxwgMW+yRMv/zyi4yMTHl5OV5MSkqaMmWKvLy8l5fX7t27BXZevXo1ORdOcwV7XFwcbma/aNGi/Pz8GzduuLq6IoSmT5+em5tbX1+/bt06PT09WVlZbW1tb29vPGsXHvS1uVlJfHx8jIyMbG1tHRwcrK2tZ86c+fHjR/6nMH36dNGPwm0BGgsPDxc4g8AkTN++fVu+fLmtra27u/uAAQOcnJwuXbrEv//o0aN1dHT4y6vWE++XEVV+uIztdHlRkyBZEhEkS5AsdUZQJW4ZibyMxJ6cCREUFKSqqto+j0WIu5TncDiGhoazZs0S1wl/2Jo1a6ZNm0Z1FK1iZ2fn4+Mj3nNK5MdTMjLpH9ae76mxsfH48ePb57HEXuqmp6fLyMicPHlSjOf8ARwOx9nZOSIigtowWqO4uFhOTm7nzp3iPa1kfJAlsowVHSRLIoJkSYwgWWo30HAatDdRZlPomKSkpE6cOHH69GlR5khsO5WVlSkpKa0c2IZa58+fz8jIOHDgANWBAPA/Om/pZGhouGXLltDQ0JqaGgrDyMzMrK+vbzyQTCeyceNGW1vbxYsXUx0I6Oo6b3EEyZK4QLLUnqBKDEALODk5nT9/fvLkyfzDk7anhoYGf3//zZs3CwxR2Incvn07ODg4MTGxT58+VMcCgORYtWqVv7+/n59f467R7SMlJQVPrELOgtPp7Nq1682bN9euXRM+wi0AQDhIlloPkqV2BlVi0H5Onz6dlJRUXV29aNGiBw8eUB3ODxo1atSlS5fIMRjamYyMzOXLlx0cHCh59Nb78uXL6dOnk5OT8ahFAHQQISEhubm5T58+Xb16dW5uLtXh/KCNGzeuWLHi999/p+TRBw4c+Pfff/PPb9S5XL16lcvlxsXFNTlZLgDtBpKl1oNkCbQUzEsM2s+UKVOmTJlCdRRi0Ldv3+9OVQea1KdPn4MHD1IdBQCCgoODg4ODqY5CDNzd3d3d3amOolPy8fGhOgQAEIJkCUCyRAW4SwwAAAAAAAAAoIuCKjEAAAAAAAAAgC4KqsQAAAAAAAAAALooqBIDAAAAAAAAAOiqqJ4YuZPx8/Oj+h0DADRL8mafp/oVBQCIx9mzZ6kuTloLUiAAJIPkJUutRyMg5WqJ+/fvZ2dnUx0FAB3Oy5cvt2zZoqKiMmDAAGtr6wEDBigqKrZ/GNra2oMHD27/x207kZGRVIcA2lZ0dPSzZ8/WrVtHdSCgbTk6OmppaVEdRat0nBSooaHhzZs3z549S01Nzc3N9fPzmzBhAtVBAdBpSF6y1HpQJQYAiEdGRgabzY6Kirp9+zaXy7WysmKxWN7e3jY2NjQajeroAOigNm3adO7cudevX1MdCAAdXWZmZlxcXEJCQmxsbEVFRd++ffG3jIuLi6ysLNXRAQA6MagSAwDErKqq6ubNm1FRUdeuXfvy5YuGhsbw4cO9vb29vLxUVFSojg6AjgWqxAAIweFwHjx4EBUVlZCQkJKSoqCg4OjoyGKxxo0bp6OjQ3V0AAAJIU11AAAASaOoqOjt7e3t7Y0Qev36NU5lfvrpJ4SQvb29t7e3h4fHwIEDqQ4TAABAB5Wfn3/9+vWoqKi4uLiysrK+fft6eHgEBwd7eXnJyclRHR0AQNLAXWIAQHsoLi6+efNmQkLC1atX8/Ly9PX1PT09PTw8Ro4cqaSkRHV0AFAG7hIDgHG53NTUVNwB5+nTpwwGw8nJicVijR07VldXl+roAACSDKrEAIB21WTS4+HhMWbMGBMTE6qjA6C9QZUYdHEFBQVJSUlsNpvNZpeWluIbwiwWy9PTk8FgUB0dAKBLgCoxAIAyBQUFsbGxAk3jWCwWNI0DXQdUiUEX1Nxvoz4+PqamplRHBwDocqBKDACgHpfLvX//fuMBVHx9fbW1tamODoA2BFVi0HUUFhYmJibimnBJSQnZg2bEiBHKyspURwcA6LqgSgwA6FgEptkwMzPDI3K5urrKyMhQHR0AYgZVYiDZeDzes2fPEhIS2Gz2/fv36XQ6jLMIAOhooEoMAOigampq7t69i0fkSktL69Gjx7Bhwzw8PLy9vXv16kV1dACIB1SJgUQSGFJRT0/Py8vLw8Nj+PDhTCaT6ugAAOB/QJUYANAJZGRk4JsM8fHxDQ0N1tbWuNexo6MjnU6nOjoAfhxUiYHEIG8IJyQkJCYmSVnWiQAAIABJREFUIr6J92xsbGg0GtUBAgBA06BKDADoTKqrq+/du8dmsy9dupSdna2uru7m5sZisby9vbt37051dAC0GFSJQWf37du3GzduJCQkREVFff36VVNT08vLy9vb28vLS0VFheroAADg+6BKDADorDIyMvAwLUlJSTwez8rKCteN4XYE6ESgSgw6qdevX+MxEaEEBgB0dlAlBgB0epWVlbdu3YqKihK4RwGd1kDHB1Vi0IlUVVXdvHkzKioqOjo6JydHQ0PD1dWVxWL5+Ph069aN6ugAAOAHSVMdAAAAtJaSkpK3t7e3tzf/0KaTJ0+GoU0BAKD1yCY5t2/f5nK5VlZWU6dOZbFYTk5OcEMYACAB4C4xAEAyFRUV3bp1i5wAs2/fvnhELk9PTwaDQXV0APw/cJcYdEwCAzeoqam5u7vDwA0AAIkEVWIAgITjcrmpqam4bvz06VMGg+Hk5MRiscaOHaurq0t1dKCrgyox6FDIG8LJyckwvD8AoIuAKjEAoAspKCiIjY2NioqKi4srKyvr27cvvunh4uIiKytLdXSgK4IqMaAceUP4ypUrWVlZqqqqQ4cOhUngAQBdB1SJAQBdEYfDefDgAR4uNSUlRVFR0d3d3dvbe9SoUVpaWlRHB7oQqBIDqvDP915XV2dmZoZHXnBzc5OWhrFmAABdCFSJAQBd3adPn+Lj4xMSEmJiYiorK8m80NXVVUZGhurogISDKjFoTzU1NXfv3k1ISLh69WpaWlqPHj2GDRuGm0b37t2b6ugAAIAaUCUGAID/h0wWr1y58vbtW7L1oI+PT8+ePamODkgmqBKDdoBvCMMPfwAA0CSoEgMAQBP4mxTyjzEDk44A8YIqMWgjtbW1d+7cwTVh6B4CAABCQJUYAACEqa6uvnHjRlRUVHR0dE5Ojrq6upubG4vF8vHx6datG9XRgU4PqsRAvDIzM+Pi4hISEmJjYysqKmAQQQAA+C4YPgEAAIRRUFDw9vb29vZGCL1+/RqPyBUYGMjj8aysrHCuaWNjA7eOAQBUERgvUEFBwdHRMTQ0dNy4cTo6OlRHBwAAHR3cJQYAgBb79u3bjRs3cMvq3NxcXV3d4cOHe3h4jBgxQllZmeroQGcCd4nBD8vPz79+/Tr/rHK4f4eXl5ecnBzV0QEAQKcBVWIAAPhxPB7v2bNnuG58//59WVlZZ2dnPJ+nmZkZ1dGBTgCqxKBFuFxuamoqm82Oiop6+vQpg8FwcnJisVhjx47V1dWlOjoAAOiUoEoMAADiUVhYmJiYyGaz2Wx2aWkp3LEBooAqMRBFQUFBUlJS4+LF09OTwWBQHR0AAHRuUCUGAAAxE7iNIy8v7+joyGKxoF8faAyqxKA5Td4QxtPCmZqaUh0dAABIDqgSAwBAGyI7+12/fr28vBxGfwUCoEoMBJDtTaKiokpKSvT19T09PWGoAgAAaDtQJQYAgPYgMCQsOUfo6NGj+/TpQ3V0gDJQJQao0agEdDrd3t7e29vbw8Nj4MCBVEcHAAASDqrEAADQ3jIyMnDum5CQUFtba2ZmhnNfNzc3aWmYG69rgSpxV1ZcXHzz5s2EhISrV6/m5eXp6el5eXl5eHgMHz6cyWRSHR0AAHQVUCUGAADK1NTU3L17l81mX7lyJSsrS1VVdejQobivYM+ePamODrQHqBJ3QeQM54mJiQgh8oYwzHAOAACUgCoxAAB0CBkZGbj34O3bt7lcrpWVFe51DFmyhHn58uWpU6fIxTt37rx79y4gIIBc079//6lTp1IRGmhD5GTmUVFRX79+1dTU9PLy8vb29vLyUlFRoTo6AADo0qBKDAAAHUtVVdXNmzejoqKuXbv25csXDQ2N4cOHQ+osMQoLC3v16oUQwo3k8bcw/tWDIIj6+vrDhw/PmTOH2iCBuJA3hJOSkng8HvzUBQAAHRBUiQEAoOPib2BJEISDgwOMuCMBhg8ffuPGDS6X23iTlJRUfn6+qqpq+0cFxIX8VSs6OjonJ0ddXd3NzY3FYvn4+HTr1o3q6AAAAAiCKjEAAHQCzQ3DA/OydEb//PPPjBkzeDyewHopKakRI0ZERUVREhVopcZ9Hzw8PFgslpOTE9wQBgCAjgyqxAAA0JnwT9Zy7949OTk5Z2dnPCKXqakp1dEBkVRUVKirq9fV1Qmsp9Ppp06dmjx5MiVRgR9QXV197949Npt96dKl7OxsNTU1d3d33DS6e/fuVEcHAABAJFAlBgCAzqqwsDAxMZHNZrPZ7NLS0r59++K7Ul5eXnJyclRHB4Tx8/O7evVqQ0MD/0oGg1FYWKikpERVVEBE5A3h5OTkhoYGa2tr/NFzdHSk0+lURwcAAKBloEoMAACdHpfLTU1NxTn606dP5eXlHR0dWSyWr6+vtra26OcpLi4uLy/X19dvu1ABdunSpfHjx/N/BUtLS/v5+Z0+fZrCqLqO4uLi6urqFn06yBvCAlOmeXt74/HSAAAAdFJQJQYAAImSmZkZFxeXkJAQGxtbUVHRt29f3IzT1dVVRkZG+LEnTpxYtGjRwYMH/f392yfaLqu+vl5NTa2iooJcQ6PRrl69ymKxKIyqi0hISJg2bdrChQvXrVv33Z0zMjJwP4X4+Pi6ujozMzM8xJ2bmxseMxwAAEBnB1ViAACQTLW1tXfu3MHZ/Js3b5SUlNzc3Ly9vVksVu/evZs8xM/P7+LFiwRBTJo06fDhwzDnU5uaNWvWqVOn6uvr8aKysnJRUZGsrCy1UUm2urq6X3/9ddeuXQghOzu7hw8fNrlbTU3N3bt38Wh2aWlpPXr0GDZsGG4a3dxnBwAAQOcFVWIAAJB8/He6muv6yOFwunfvXllZiRCSlpZWV1c/c+aMi4sLpYFLsoSEBE9PT/y/jIzMzJkzDx8+TG1Iki0tLW3SpElpaWkcDgchRKfTCwoK+Oe7wh+ThISEmJiYyspK8oawKC0sAAAAdF5QJQYAgC6E7A95+fLlz58/8w+Q+/z5c3d3d3JPOp1OEMSiRYvCw8Ph1mVb4PF4mpqaRUVFeDExMdHV1ZXakCQVQRBHjhz55ZdfOBwOrg8jhOh0+n///TdmzBjcmCIhISElJUVRUdHd3d3b23vUqFFaWlrUhg0AAKB9QJUYAAC6qBcvXsTExERHR9+7dw8h1LNnz7y8PLLCgElJSfXv3z8yMrJfv34UhSnJlixZcvDgwfr6enV19by8PBisuC0UFBTMnDkzNjZWYCJoKSmpXr16ffv2rbq62tLScuTIkSNHjnRycoIewgAA0NVAlRgAALq60tLSuLi42bNnl5eXN94qLS1Np9PDwsJ++eWX9o9Nsj18+NDBwUFKSmrJkiU7duygOhwJFBcXN3Xq1LKyMoH5rjAZGZl9+/bBDWEAAOjioEoMAAAAZWdn6+joCNmBRqP5+PhERETw970Eraenp5eVlZWSkmJjY0N1LBKltrZ21apV+/bto9FoAveH+T1+/NjW1rY9AwMAANDRQBstAAAAKCoqSkpKSsgOBEFER0ebmJjExsa2W1Rdgb+/v4GBAdSHxevVq1c2NjYHDx4kCEJIfVhWVjY6Oro9AwMAANABQZUYAAAAioqKEt5oSEpKiiCIoqKi0aNHr1mzhpw6CLTS1KlTp02bRnUUkoPH44WHh9vY2KSlpSGEhHfPrq+vv3r1anuFBgAAoIOChtMAgGZNmDCB6hDA/8fefcdFcfz/A5+jN6mCBRAUlC6KIqAIiiLYJSCKoknUCPaGIjawx4YlKsYekaAUjWLBEiVqAgQb0gUUpaggRTrH3e3vj/3lPvelnEg74F7PP/K4ndmbfc+45O59OzvbHths9vXr19lsNrdEVFRUVFRUTExMXFxcQkJC/D90ibi4uIqKCp5a3FqYTCbW9G4tZWVlBQUFtf8Xk8mkX9ArTvN+82EwGJMmTZKUlBRgzNA+Vq9ebWVlJegoAKAjQkoMAI1iMBiWlpZYeKblYmJiCCGWlpaCDqRhLBarpKSEN+NlMBhNeWNOTk5MTIyLi0tbRwjQithsNovF4ibMioqK+EmiywsLC7t8+bKrq6ugAwGAjghPGgAAflatWoXvEC1HX28PDQ0VdCCtLCQkZMaMGV2vXwDQxTTxZz4AEE64lxgAAAAAAACEFFJiAAAAAAAAEFJIiQEAAAAAAEBIISUGAAAAAAAAIYWUGAAAAAAAAIQUUmIAAAAAAAAQUkiJAQAAAAAAQEghJQYAAAAAAAAhhZQYAKAjys3NVVVVXbt2raADaQWenp5iYmJmZmaurq7BwcG8VQ8fPty4ceOjR4/c3d1lZGQYDMacOXN4d1i3bt3w4cN79Ogxb9689PT09gx7z549BgYGBgYG5ubmJiYmPj4+X758oatGjRrFqEdJSYn/u7Zv325iYmJiYmJqampgYLB58+bq6ur6x62srBwwYMCmTZu4JRcuXBg2bJiNjc2QIUPmzp2bm5tLCLl+/fqBAwdYLFYzuoZhr39cDHvnGvaUlJR58+bZ2NgoKip279697UYGAIQCBQDQCELI5cuXBR1FV+Di4uLi4vJNb8nNze3Zs6e3t3cbhdQqLl++3JTPEQ8PD3V19frl27dvnzhx4pcvX+hNHR2dgQMHEkJu3LjBu9vt27enTp3aKgF/k4kTJ165coV+nZqaKi8vP3nyZHrT1tZ269atv/CYNGnSwoULv/que/fu0a+fPXsmJSU1ffr0+sf19PQkhGzcuJHeDAoKIoT8+eefFEVVVlYOHz584MCBTCaToqiDBw+OHj26pKTkm/qFYcew19d5h33FihUqKipf7SA+zgCAD6TEANCoTvQdYsqUKTk5OR225WakxO2g5V1rSUq8b9++/v37V1RUcEt0dHQePXrUrVs3dXV13i++gkoSnJycampquJv0Bb3i4mKKoiZNmsRms7lVbDbbwMAgNTX1q+/icDjcqunTpxNCPn78yHvQ27dvOzg48CYJo0eP1tLS4u5w+vRpQsijR4/ozSVLlowYMYLFYjWxUxh2DHuDOu+wIyUGgJbDxGkA6PQSExMjIiIoiupELQucYLuWkZGxYcOGzZs3y8jI8JZramru3bs3NzfXy8tLIIHxunLlioSEBHdTWVmZEFJYWEgIiYiIEBH53wfo1atXdXV19fT0vvouBoPBrerZsychJD8/n1tSWFi4efPmX375hTeM7OxsuhEaPUc0Ozub3ty6deuzZ8+OHDnSlB5h2AmGvRFdb9gBAJoOKTEAtI7o6GgHBwdjY+MRI0b069dvypQpdPnt27etra2HDh1qbGw8fPjwW7duEUIyMjKcnJwYDMbZs2cXLFjQo0cPZWVlb29v/q2VlZXZ2NgMGzbM0dFRT0/v4MGDdPuzZ8+mKGrKlCmWlpYFBQWEkEuXLg0aNGjYsGFmZmbbtm1jsVj8j9iSlttiMCsrK729vbt16+bi4sJ/uF6/fu3g4MBgMJYtW2ZkZCQtLd2/f//z588TQh4/fkzfBJiamkoIiYqKmj59OoPBCAsLq9+1Dx8+aGho0Fds2sGxY8cYDMa0adPqV3l4eNja2p4+ffr+/fv1aymK8vf3HzRokKWl5YABA6ZNm0b37u7du2ZmZjIyMpGRkZMmTerWrZumpmZISAj9LjabvWXLFn19/eHDh5ubm4eHhzcj5tTUVDU1NS0trfpVe/bsaSyr4fOuoqIiERERDQ0NbsmiRYu2b99O36XJpa+v/+HDB+6PF3R6oK+vT2+qqKjY29sfOnSIw+F8tQsYdoJhb5ouMOwAAN9AMBenAaAzIE2eaRYTEyMuLu7n50dvlpWVDRw4kKKo+/fvi4iIBAcH0+WHDx9mMBh3796lKOrevXuEkG3btqWlpb17927u3LmEkJiYGD6tFRQUTJs2jZ6MFxAQQAhJSEigKIq+zpCdnU3vHxERQQgJCQmhKOrt27dSUlL79+/nf8QWtvxVzZs4raOj4+zsTL/mEzy9YFV4eDiLxfr8+fNPP/1ECKHv4jt16hQhJCUlhW4kLi6OEBIaGlq/a3l5eerq6mPHjv2mCJs9cVpPT2/w4MH1+/v27VuKotLT06WlpbW1tcvLy6n/O5V0y5YtSkpKycnJFEXV1ta6urp2796dno25ceNGQsjNmzezsrKSkpIGDRokLy9Pz+r08vKSl5dPT0/nDldcXFwT+1hdXZ2VleXv7y8vL09fV6/j3r17Q4cO/dZ3URTVr18/e3t77uaFCxc8PT0piqJ/fOFOJX327JmsrKy/v39ZWVlCQkL//v1nzJjB286OHTsIIYmJiV/tC4adwrDz1RmHHROnAaDlkBIDQKOa/h3C0dFRTU2NXgSFdujQIYqi7Ozs9PT0ePfU0tKys7Oj/svxbt++TZe/evWKEHLmzBk+rdXU1KSlpdEl0dHRjWV3dnZ22tra3PeOHTtWX1+f/xFb2PJXtVZK3GDw9LfeFy9e0FVVVVVKSkr0t8+mp8TN07yUuKKigsFgcJfh4e0vnSRQFLV//35CyNKlSymeJKGyslJOTs7Dw4P7ljdv3tC/FFD/JQlVVVV0FT27MjMzs6qqSkZG5ocffqDLa2trpaWl6a/jTeHo6Ni3b19VVdXdu3dz10biNWbMmEuXLn3ru27evCkhIcH9V3v//r2ZmRmdFNVJEjgcjre3t7i4uK6urqKiooaGxuvXr3mboicFBAYG8u8Ihp3CsH9NZxx2pMQA0HKYOA0AreDJkyempqbi4uLckhUrVhBCYmNjDQwMePc0MTGJjY2t34K0tDQhhMlk8mlNQkJiwIABdImYmBghpMF5y8+fP9fV1eVu9u7dm/7qyeeILWm5/m7tgDf4OqSkpExNTRMTE9s9qKYqLCykKEpeXp7PPqtWrbKwsDh27NiTJ0+4hUlJSeXl5bxnVN++fbt168b/jEpLS6usrOT+w4mJiamqqtLZRVPcvn37zZs3KSkp0dHRBgYG79+/562Ni4vLzMyk57c3/V0FBQWLFy8OCAgYNGgQIYSiqPnz5x8+fFhWVrZ+AMuXL7906VJmZmZ6enphYaGrq6uFhcXr16+5O9Aj+fnzZ/4dwbBj2L+qyww7AMA3QUoMAC3FYrHKy8t510ThlldUVNDf1bjk5OQqKirYbPa3tkYISU5O/u6772xsbKZPn75hw4bGWigrK4uPjx/1n3fv3llaWtbW1vLpQkta5tOsoCgpKXXkr4wVFRWEEFFRUT77iIiInDlzRlxcfN68eVVVVXRhSUkJ+e/bP5esrCz3UagNKisrI4T89ttv3H+4vn376ujofFPMKioqx48fz8vL2717N2/57t27V6xY0VhfGnxXcXHx+PHj169fP2/ePLrk2LFjVVVVHz9+DAsLCwsLo+fnp6amhoWFJSQkHD9+fM6cOZqamvSwbN++/cuXLwcOHOA2SB+9vLycfxcw7Bj2JuoCww4A8E3EBB0AAHR6YmJicnJy9Be4OuWysrKVlZW8heXl5XJycny+IDbWGiFk8uTJhoaGf/31F4PBePr0KT2XuD55eXlLS8vr1683vQtt17JAFBYWqqurCzqKRtFXh6qrq/nvZmRktGnTpi1btmzfvr1Pnz6EEEVFRUJI/TOKLm8MfVnJ09Nz9erVLQm7d+/eoqKiGRkZ3JLU1NS//vrrwoULTX9Xfn7+pEmTvLy8Zs6cyd3n6dOnT5484b1CSAgJDw8PDw/fv38/h8Pp1asXt1xGRkZZWTk9PZ1bQo+knJwc//gx7Bj2puvsww4A8E1wlRgAWsHw4cNjYmLo6yG8rKys6CVSuRISEiwsLJrRWnl5+Zs3bywsLHgf7EGrU2JqahofH9/0JUnbrmWBYDKZiYmJ5ubmhBD6+Sg1NTX1d6vf2XajoqLCYDCacp1n/fr1AwcOfPHiBb1pbGwsJyfHe0ZlZWWVl5fzP6MGDBggLS39/PnzbwqysLCQXsOM6927d2w2W1tbm1uyZ8+en376iffbOf93ZWdnT5gwYdeuXbwZAiHk/PnzvHc08d5d+d133xFC6BIak8ksLi7mXdSX/kuhH1fDB4Ydw96YrjfsAADfBCkxALSCTZs2lZeXT5s27dWrVywWi81mZ2RkMJnM9evXp6en04swEUKOHTuWnZ3t4+PTjNYkJCRUVFTu3r1bWFiYk5Nz8+ZN7v70LOt///2XxWJVVVUtXbr0/fv3y5cvpycPl5WVvXv3js/h5OTk2qhlgfD39y8tLd20aRMhhH526LFjx3Jyct68eRMTE8PdrU7XPnz4oKmpaW9v3w4RysjI6OnpNeU2bHFx8bNnz3LnFEhJSa1ZsyYkJITOE1gslo+Pj6qq6sKFC/k0IiUlNX/+/EuXLp0+fZpejqigoIB+PurMmTPV1NQazB9kZWUfPnxYXFxMb1ZUVKxdu1ZeXn7NmjV0SXZ2dmho6LJly5r4rsLCQmtrayMjo9TU1KM8Xr58ySf4vn372tvbBwcHc+dN0IucL1iwgLsPfaeomZkZ/x5h2DHsXWzYAQBaTRss2QUAXQT5liU6Hz58aGdn161bNzk5OT09vSVLllRXV1MUdePGDUtLS3Nz88GDB48YMYJeMzk9PZ1+SuekSZNiY2MzMzPpqw02NjbR0dGNtRYSEqKuri4lJWVtbU0nrvb29s+ePSsrKxsxYoS0tLSxsXFUVBRFUYGBgYMGDZKUlFRVVR0xYsSVK1f4H7ElLTdlcL51xenU1NTVq1fLyclpaWn5+PjExMTwCZ5ecXrUqFHjx48fOHCghYUF/Zgr2ooVKxQUFFRVVd3d3c+dO0cIcXBwqN81+iFM48aNa3qQVAsewrRixQpxcfHS0lJ686+//nJzc5OWlh43bhy9ujgvb29v7mNpOBzOvn37TExMhg8fbmho6OTklJqaSlHU3bt3Bw8eTAhZtmzZp0+f/vzzT1tbW0LI3LlzP3z4wGQyN27cqK2tLSEhoampOXnyZPohW9OnTyeEbNmypcGwp0yZMmDAgKFDh1paWg4ePPjHH3/MzMzk7cLcuXOb/q6EhIQGP4X37dtXp4U6a/AWFRWtWbNm6NCho0ePNjU1HTFixNWrV3n3nzhxYp8+fdhs9ld7hGHHsHelYefGhhWnAaCFkBIDQKPwHaK1NO8hTE1Ep8T0t9521uyUOD09XVxc/MKFC20WWpOwWCxra2v6WVadVGFhoaSkpL+/P73Jv0cY9taCYReIOsNOQ0oMAC2HidMAAF1Bg0+N6rB0dXV37Nixfft27vq6ApGVlcVkMuvc69i5bNmyZejQocuXL6c3+fcIw95aMOwCUWfYAQBaC1acBgCANvfx48chQ4bo6upOmzbNzc2NELJu3brq6moXF5dLly5169at/UN69uzZkSNH/vjjDxkZmfY/eqs4ePBgcnLyzZs36RtQm9IjDHvLYdgFos6wp6Sk7N+/PyMjIz4+nn6YPABAszEoihJ0DADQQTEYjMuXL7u6ugo6kE6PvosvNDS01VuOi4vbsGHD/fv3nZ2dnZ2d6Wyz3YSEhMyYMaMlnyMPHz68f//+zp07WzEqIXH9+vXXr1+vXLmyGfkAhr3ZMOwC0ZJhp+HjDAD4QEoMAI3Cd4jW0nYpsWC1PCUGAGgH+DgDAD5wLzEAAAAAAAAIKaTEAAAAAAAAIKSQEgMAAAAAAICQQkoMAAAAAAAAQgrLawFAoxgMhqBDAAAAaAVYXgsAGoMnuQEAPytXrrSyshJ0FJ3ewYMHCSGrVq0SdCCtLDo6+tChQ5cvXxZ0IAAA/MyYMUPQIQBAx4WUGAD4sbKyws/qLUc/fqlLjuShQ4e6ZL8AoCtBSgwAfOBeYgAAAAAAABBSSIkBAAAAAABASCElBgAAAAAAACGFlBgAAAAAAACEFFJiAAAAAAAAEFJIiQEAAAAAAEBIISUGAAAAAAAAIYWUGAAAAAAAAIQUUmIAaAX//POPvb39gAEDFi5cuGHDhvnz55ubm0tJSa1du1bQoQEIOxaL5efnt2HDhrdv354+fVpFRYXBYFhYWLDZbO4+ERERnp6eRkZGFy5cKCwsbP8gP378OHXq1EGDBrW86v379yNHjjQ2Nl6zZo2NjY25uXlWVhZdNWrUKEY9mpqadZp99+6dgoLCpk2b6h+Rt+rhw4fTp0/PyclpVo8BAKCjEBN0AADQ6V28ePHHH39ct25dZGSkqKgot/yvv/4KCAhoz0jevXvXq1cvCQmJTtQyQJuqqKhwcXGxtLTctWsXIWTBggU///zzqFGjrly54u/vz/3RavLkyeLi4h8/fpw7d277BxkUFLRy5crS0lIDA4MWVlEUNW3atOrq6mfPnklLS7PZ7JEjRzo7Oz99+pTBYBBCHjx4oKqqyt3f39+/X79+vM1yOJwff/yxtLS0fpx1qkaPHi0jIzNq1Kjw8HBTU9MWDAAAAAgSrhIDQIu8e/fup59+srW13blzJ28+TAgZOXKkoaFhewYzderU/Pz8ztVyy129enXBggUdoRHogDw9PWtra319fXkLd+zYoauru2XLltevXwsqMK74+PicnJwXL14MHjy45VWxsbEvXryYM2eOtLQ0IURUVHThwoXPnz9/8uQJIWTkyJGjR482/k/fvn3v37+/aNEi3hb8/f0bvCLdYJWFhcXKlSsnTZpUVFTUrN4DAIDg4SoxALRIQEBAdXW1h4dH/SoREZEtW7ZwN8vLy1NSUiiKMjAw6NatGyHk8+fPiYmJhBBzc3MZGZnU1NTa2lojIyPe1LqmpiY5ObmqqkpJSalv375SUlJ0YUZGRmlpac+ePfv27UsIoSgqMDAwPj4+OjpaVVXVyspKUlKSEFJQUJCZmamhoaGhodGUIza75VaUkZGRn5+vqqrav39/uiQqKooQoqamZmhoyOFwHj16RAiRlZU1NzcvLCzctWuXmppaVFRUr169SktLKyoqevbs2a9fv/T09Orq6oEDB4qLi39TI3p6elVVVampqfXzEOhcHj16dPEbwFwDAAAgAElEQVTixfv379cpl5aWPn369OjRo+fPn//o0SP68ml9Hz9+fPv2rYyMjJGRkZiYGCEkIyMjJydHXFx8xIgRTCYzISFBUVFRR0eH911v3rz5/Plz//79lZSUmhKkqalpY5dYm1GVnp5OCNHS0uKWmJubE0KioqJGjhy5fft23p1Pnjw5efJkFRUVbklCQsKNGzcuX7588ODBOi03VuXh4bF169atW7cePnyYTzcBAKDDwlViAGiRhw8fEkIsLS357ENRlI+Pj56e3tmzZ0+cOKGtrb1+/XqKosTExKKiokaPHr127VpbW9uJEyeampra2NiwWCz6jQcOHNDU1Dx48ODVq1c9PT3pFLGoqEhVVXXfvn13794dO3bs1KlTORzO3bt3L168SAiJioqKjIysqalhs9mLFi0aNGhQcHDwd999Z2dn9/nzZ/5HbEnLrTKYCQkJxsbGCxcuDA8Pd3d3NzIyio+PJ4TU1NQ4OTlt27aNEMJgMFgs1sqVK3/88Ucmk3nw4MGkpKScnJzIyMikpCQWi+Xm5jZlyhR7e/vFixfb29v36dPn8ePH39QIIWT+/PlmZmbnz59vlX6BoJw4cUJBQcHW1rZ+la2trYeHx5MnT44ePVq/tqSkZMqUKXZ2diEhIX5+fr1797506RIhRERE5PDhw6NHj960aZOlpeWkSZN0dXXXrVtHvys/P9/GxsbZ2fn33383NTVdvnw5RVFt2sH6FBUVCSG1tbXcEvpW4YyMjDp71tbWHj58eNWqVdwSJpPp4eFx6tSpOhNe+FeJi4tPmDDh3LlzNTU1rdgRAABoPxQAQCMIIZcvX+a/j5qaGiGEzWbz2ef48eOioqJJSUn05pMnTxgMxrFjxyiKunfvHiHk6tWrdBV9+eXWrVsURQUGBhJCbty4wW3H2tqaoqiCggJPT0+6JDw8nBDy6NEjiqJ++eUXQkh2djZvUwkJCRRFVVVVqaioLF26lP8RW9gyHy4uLi4uLvz3qa6u7tOnj5ubG73JYrHGjx+vqalZXV1NUZS6uvqMGTO4O0+cONHIyIh+raKiMn/+fG6Vnp7e1KlT6ddFRUUmJiba2tpMJvObGgkLC5s8eTJ9VZ+Py5cv43OkI+vdu7eVlVWdQh0dnbdv31IUVVpaqqmpKSsrS2/evn2be+a4urrq6uqWl5fTmzt37hQTE4uPj6coauPGjYSQ4uJiiqLYbLaTk5OoqGhZWRlFUVOnTu3Tp09FRQVFUf/++y8hJCwsrOnRWlhYmJqatrDq3bt3oqKiixcv5pZwOBxCCLdrXGfOnHFycuItWbdu3S+//EJRVEFBASFk48aNTamiKOrAgQOEkOjo6KZ0EwSiKR9nACC0cJUYAFqksrJSTExMRITf/0xOnjxpZmbGva94xIgRJiYmJ0+e5O5AT4cmhDg6OhJCsrOzCSGHDx/u37//xIkTubvR07BVVFQOHTpEl9DXf/Ly8uof9MKFCwMHDjQ2Nqbbt7a2vnbtGv8jtkrLzRYZGfn+/Xt3d3d6U1RU9KeffsrOzr59+3az21RSUlq1alVWVtbdu3e/6Y3Ozs7Xr1/X19dv9qFB4EpKSvLy8nr16tXYDt26dfv1118rKirq3EZeVFQUFhbm5OQkKytLlyxatIjFYp09e5a7D/0XJCIiMm7cODabnZeXV1JSEhERMW3aNBkZGUKIubm5goJCq/xpfJM+ffosWbLkzJkzBw8efPHiRVRU1I4dOwghdGLMxeFw9u7d6+XlxS158uTJy5cvlyxZUr9NPlW0nj17EkLoGRYAANDp4F5iAGgRJSWl7OzsqqoqejGbBiUmJjo5OfGW6OrqRkRE1N+Tvl+R/vIaHx8/btw43lp7e3tCCIPBEBcXz8zMLCwspOcVUw1NzkxLSzMyMqJvoCWE1NTU5OXl8T51pv4RW9JyY31vuoSEBEKItrY2t0RXV5cQkpSUNG3atGY3S6e1mZmZLY0POpvi4mJCCJ2gNmb8+PFz5swJDAw8deoU91lEycnJHA6H91RUUlJSVlZuMOXj/gWlp6dzOJyqqirun4a0tPT79+9bpzPf4uDBgyYmJjdv3nz48KGBgYGLi8uWLVsUFBR497l69aqysvLw4cPpzbKysuXLl1+7dq3+bdV8qrjo3w7oAQcAgE4HKTEAtIiZmVl2dnZycvKQIUPq16ampvbv35/FYtHfm7kkJCRqa2vrJ6hcbDa7trZWTk6uftXly5dXrlw5btw4Y2NjPjfxMpnMyspK7rdzCwsLCwuLBlPcVmmZT7NNRN+IyDtQ9DOfWniDIv1TRVVVVcuig86HyWQSQvgkcrRDhw7dvXvXy8uLnv1LGjoVCSESEhL8T0X6cHl5edw/DQ8Pj1ZffK4pREREFixYwL30nZaWRggZMGAA7z4///zz+vXruZs7d+7kcDh79uyhN6urqwkhd+7cKSkpKSoqaqxq3bp1ffr04TZCjwAAAHQ6SIkBoEVmz5597dq1q1evNpgSr169+tatW6qqqiUlJbzlhYWFqqqq9Req4RIVFVVVVc3Nza1TzuFwPDw8Zs6ceeLECULI06dP9+7d22ALqqqqGhoafn5+TexI27XcRPQEV96BKiwsJP/NyWw2+tlRvFf8QEjIy8sTQiorK/nvpqysfOzYMRcXl40bN1pZWZGGTkWKooqLi/mfivTDfq2trXlTzY4gJiaGEMI75eT+/fvFxcW8U1cUFRUVFRXp5ejJf6tz5efnJyYm9u3bt7Eq7i9N9CDTAw4AAJ0O7iUGgBZxdnYeO3bs4cOH6Wef8GIymfQ9uvb29s+fP+deE66pqXn69OnYsWP5t+zo6BgbG5uVlcVbWF1d/eXLl969e9ffn76fmXuU4cOHx8bGfvnypYkdabuWm4gekLi4OG4J/STVMWPGEEJkZWUbm5YpIiLC53r7/fv3paWlbWxsvqmRqqqq58+fN6cb0GF0795dQkKiKSeqs7Ozs7Mz98nb+vr6mpqavKdiXFxcTU0N/79ZXV1dNTW1Bu9aLyoqas/7bFNSUri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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.utils.plot_model(model, 'multi_input_and_output_model.png', show_shapes=True, show_layer_activations=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-06 15:29:25.389379: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8907\n",
      "2024-01-06 15:29:25.836174: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 939.37MiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2024-01-06 15:29:26.267938: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 939.37MiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2024-01-06 15:29:26.307311: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 939.37MiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2024-01-06 15:29:26.411039: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 678.62MiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2024-01-06 15:29:27.972875: W external/local_xla/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc:504] Can't find libdevice directory ${CUDA_DIR}/nvvm/libdevice. This may result in compilation or runtime failures, if the program we try to run uses routines from libdevice.\n",
      "Searched for CUDA in the following directories:\n",
      "  ./cuda_sdk_lib\n",
      "  /usr/local/cuda-12.2\n",
      "  /usr/local/cuda\n",
      "  /home/andrew/repo-projects/projects/microphone_direction/venv/lib/python3.11/site-packages/tensorflow/python/platform/../../../nvidia/cuda_nvcc\n",
      "  /home/andrew/repo-projects/projects/microphone_direction/venv/lib/python3.11/site-packages/tensorflow/python/platform/../../../../nvidia/cuda_nvcc\n",
      "  .\n",
      "You can choose the search directory by setting xla_gpu_cuda_data_dir in HloModule's DebugOptions.  For most apps, setting the environment variable XLA_FLAGS=--xla_gpu_cuda_data_dir=/path/to/cuda will work.\n",
      "2024-01-06 15:29:28.005099: I external/local_xla/xla/service/service.cc:168] XLA service 0x7f545c770230 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2024-01-06 15:29:28.005115: I external/local_xla/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 3050, Compute Capability 8.6\n",
      "2024-01-06 15:29:28.008459: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1704529768.055212   21338 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/3 [===================>..........] - ETA: 0s - loss: 2.5110 - accuracy: 0.3125 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-06 15:29:31.631731: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 939.37MiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2024-01-06 15:29:31.651058: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 939.37MiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 11s 2s/step - loss: 2.6148 - accuracy: 0.3256 - val_loss: 2.4854 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/35\n",
      "3/3 [==============================] - 1s 226ms/step - loss: 1.9326 - accuracy: 0.2791 - val_loss: 1.5971 - val_accuracy: 0.5000\n",
      "Epoch 3/35\n",
      "3/3 [==============================] - 1s 227ms/step - loss: 1.7404 - accuracy: 0.3837 - val_loss: 1.6979 - val_accuracy: 0.6000\n",
      "Epoch 4/35\n",
      "3/3 [==============================] - 1s 226ms/step - loss: 1.7365 - accuracy: 0.3605 - val_loss: 1.7422 - val_accuracy: 0.5000\n",
      "Epoch 5/35\n",
      "3/3 [==============================] - 1s 224ms/step - loss: 1.5905 - accuracy: 0.3837 - val_loss: 1.6998 - val_accuracy: 0.5000\n",
      "Epoch 6/35\n",
      "3/3 [==============================] - 1s 225ms/step - loss: 1.5861 - accuracy: 0.3837 - val_loss: 1.8070 - val_accuracy: 0.5000\n",
      "Epoch 7/35\n",
      "3/3 [==============================] - 1s 226ms/step - loss: 1.5134 - accuracy: 0.4419 - val_loss: 1.7666 - val_accuracy: 0.5000\n",
      "Epoch 8/35\n",
      "3/3 [==============================] - 1s 227ms/step - loss: 1.4363 - accuracy: 0.4535 - val_loss: 1.5703 - val_accuracy: 0.5000\n",
      "Epoch 9/35\n",
      "3/3 [==============================] - 1s 224ms/step - loss: 1.3865 - accuracy: 0.3837 - val_loss: 1.5955 - val_accuracy: 0.5000\n",
      "Epoch 10/35\n",
      "3/3 [==============================] - 1s 227ms/step - loss: 1.3358 - accuracy: 0.4767 - val_loss: 1.6919 - val_accuracy: 0.5000\n",
      "Epoch 11/35\n",
      "3/3 [==============================] - 1s 227ms/step - loss: 1.2392 - accuracy: 0.5349 - val_loss: 1.6217 - val_accuracy: 0.5000\n",
      "Epoch 12/35\n",
      "3/3 [==============================] - 1s 227ms/step - loss: 1.2166 - accuracy: 0.4651 - val_loss: 1.6172 - val_accuracy: 0.5000\n",
      "Epoch 13/35\n",
      "3/3 [==============================] - 1s 227ms/step - loss: 1.1474 - accuracy: 0.6512 - val_loss: 1.6736 - val_accuracy: 0.5000\n",
      "Epoch 14/35\n",
      "3/3 [==============================] - 1s 228ms/step - loss: 1.0750 - accuracy: 0.6047 - val_loss: 1.5953 - val_accuracy: 0.5000\n",
      "Epoch 15/35\n",
      "3/3 [==============================] - 1s 228ms/step - loss: 1.0376 - accuracy: 0.6279 - val_loss: 1.7004 - val_accuracy: 0.5000\n",
      "Epoch 16/35\n",
      "3/3 [==============================] - 1s 226ms/step - loss: 0.9530 - accuracy: 0.7442 - val_loss: 1.6402 - val_accuracy: 0.5000\n",
      "Epoch 17/35\n",
      "3/3 [==============================] - 1s 227ms/step - loss: 0.8948 - accuracy: 0.8023 - val_loss: 1.6450 - val_accuracy: 0.5000\n",
      "Epoch 18/35\n",
      "3/3 [==============================] - 1s 228ms/step - loss: 0.8305 - accuracy: 0.7674 - val_loss: 1.6605 - val_accuracy: 0.5000\n",
      "Epoch 19/35\n",
      "3/3 [==============================] - 1s 227ms/step - loss: 0.7750 - accuracy: 0.8372 - val_loss: 1.6815 - val_accuracy: 0.5000\n",
      "Epoch 20/35\n",
      "3/3 [==============================] - 1s 229ms/step - loss: 0.7207 - accuracy: 0.9186 - val_loss: 1.7128 - val_accuracy: 0.5000\n",
      "Epoch 21/35\n",
      "3/3 [==============================] - 1s 227ms/step - loss: 0.6768 - accuracy: 0.9070 - val_loss: 1.7582 - val_accuracy: 0.4000\n",
      "Epoch 22/35\n",
      "3/3 [==============================] - 1s 227ms/step - loss: 0.6131 - accuracy: 0.9070 - val_loss: 1.6979 - val_accuracy: 0.5000\n",
      "Epoch 23/35\n",
      "3/3 [==============================] - 1s 228ms/step - loss: 0.5698 - accuracy: 0.9535 - val_loss: 1.6839 - val_accuracy: 0.5000\n",
      "Epoch 24/35\n",
      "3/3 [==============================] - 1s 227ms/step - loss: 0.5107 - accuracy: 0.9535 - val_loss: 1.8248 - val_accuracy: 0.4000\n",
      "Epoch 25/35\n",
      "3/3 [==============================] - 1s 227ms/step - loss: 0.4755 - accuracy: 0.9767 - val_loss: 1.8294 - val_accuracy: 0.4000\n",
      "Epoch 26/35\n",
      "3/3 [==============================] - 1s 228ms/step - loss: 0.4425 - accuracy: 0.9651 - val_loss: 1.8237 - val_accuracy: 0.4000\n",
      "Epoch 27/35\n",
      "3/3 [==============================] - 1s 227ms/step - loss: 0.4269 - accuracy: 0.9884 - val_loss: 1.8467 - val_accuracy: 0.5000\n",
      "Epoch 28/35\n",
      "3/3 [==============================] - 1s 228ms/step - loss: 0.3478 - accuracy: 1.0000 - val_loss: 1.7258 - val_accuracy: 0.5000\n",
      "Epoch 29/35\n",
      "3/3 [==============================] - 1s 227ms/step - loss: 0.3270 - accuracy: 0.9884 - val_loss: 1.8014 - val_accuracy: 0.5000\n",
      "Epoch 30/35\n",
      "3/3 [==============================] - 1s 226ms/step - loss: 0.2907 - accuracy: 1.0000 - val_loss: 1.9573 - val_accuracy: 0.5000\n",
      "Epoch 31/35\n",
      "3/3 [==============================] - 1s 226ms/step - loss: 0.2591 - accuracy: 1.0000 - val_loss: 1.9759 - val_accuracy: 0.5000\n",
      "Epoch 32/35\n",
      "3/3 [==============================] - 1s 225ms/step - loss: 0.2457 - accuracy: 1.0000 - val_loss: 1.8157 - val_accuracy: 0.5000\n",
      "Epoch 33/35\n",
      "3/3 [==============================] - 1s 224ms/step - loss: 0.2268 - accuracy: 1.0000 - val_loss: 2.0477 - val_accuracy: 0.5000\n",
      "Epoch 34/35\n",
      "3/3 [==============================] - 1s 226ms/step - loss: 0.2020 - accuracy: 1.0000 - val_loss: 1.9900 - val_accuracy: 0.4000\n",
      "Epoch 35/35\n",
      "3/3 [==============================] - 1s 223ms/step - loss: 0.1840 - accuracy: 1.0000 - val_loss: 1.9450 - val_accuracy: 0.5000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-06 15:29:59.818066: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 939.37MiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2024-01-06 15:29:59.827832: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 939.37MiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2024-01-06 15:30:00.115799: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 939.37MiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 - 2s - loss: 1.9636 - accuracy: 0.5417 - 2s/epoch - 2s/step\n",
      "Test loss: 1.9635595083236694\n",
      "Test accuracy: 0.5416666865348816\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer=keras.optimizers.Adam(learning_rate=5e-5), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "history = model.fit([x_a_train_norm, x_b_train_norm, x_c_train_norm], y_train, epochs=35, batch_size=32, validation_split=0.1)\n",
    "test_scores = model.evaluate([x_a_test_norm, x_b_test_norm, x_c_test_norm], y_test, verbose=2)\n",
    "print(\"Test loss:\", test_scores[0])\n",
    "print(\"Test accuracy:\", test_scores[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"model.keras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "epochs = range(1, len(acc) + 1)\n",
    "\n",
    "plt.plot(epochs, acc, '-', label='Training Accuracy')\n",
    "plt.plot(epochs, val_acc, ':', label='Validation Accuracy')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "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": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}