diff --git a/VGG19.ipynb b/VGG19.ipynb
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+++ b/VGG19.ipynb
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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<h1 style=\"text-align: center;\">VGG19</h1>"
+      ],
+      "metadata": {
+        "id": "IVs7HLFkrKkM"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Loading the required libraries"
+      ],
+      "metadata": {
+        "id": "HRdvHkJUrEn0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oWgRRd5xXA6_"
+      },
+      "outputs": [],
+      "source": [
+        "import tensorflow as tf\n",
+        "from tensorflow.keras.models import Sequential, Model\n",
+        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
+        "from tensorflow.keras.applications import VGG19\n",
+        "from tensorflow.keras.datasets import cifar10\n",
+        "from tensorflow.keras.utils import to_categorical"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Loading the CIFAR-10 dataset"
+      ],
+      "metadata": {
+        "id": "npvMLpc7rA1k"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()\n",
+        "train_images, test_images = train_images / 255.0, test_images / 255.0\n",
+        "train_labels = to_categorical(train_labels, num_classes=10)\n",
+        "test_labels = to_categorical(test_labels, num_classes=10)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "riMP32--XOaZ",
+        "outputId": "9526925d-260d-40f8-cda3-36b1376a0e88"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
+            "170498071/170498071 [==============================] - 6s 0us/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Creating the VGG19 model from a scratch"
+      ],
+      "metadata": {
+        "id": "IqHcT-08q56w"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def create_vgg19_scratch():\n",
+        "    model = Sequential([\n",
+        "        Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=(32, 32, 3)),\n",
+        "        Conv2D(64, (3, 3), activation='relu', padding='same'),\n",
+        "        MaxPooling2D((2, 2), strides=(2, 2)),\n",
+        "\n",
+        "        Conv2D(128, (3, 3), activation='relu', padding='same'),\n",
+        "        Conv2D(128, (3, 3), activation='relu', padding='same'),\n",
+        "        MaxPooling2D((2, 2), strides=(2, 2)),\n",
+        "\n",
+        "        Conv2D(256, (3, 3), activation='relu', padding='same'),\n",
+        "        Conv2D(256, (3, 3), activation='relu', padding='same'),\n",
+        "        Conv2D(256, (3, 3), activation='relu', padding='same'),\n",
+        "        Conv2D(256, (3, 3), activation='relu', padding='same'),\n",
+        "        MaxPooling2D((2, 2), strides=(2, 2)),\n",
+        "\n",
+        "        Conv2D(512, (3, 3), activation='relu', padding='same'),\n",
+        "        Conv2D(512, (3, 3), activation='relu', padding='same'),\n",
+        "        Conv2D(512, (3, 3), activation='relu', padding='same'),\n",
+        "        Conv2D(512, (3, 3), activation='relu', padding='same'),\n",
+        "        MaxPooling2D((2, 2), strides=(2, 2)),\n",
+        "\n",
+        "        Conv2D(512, (3, 3), activation='relu', padding='same'),\n",
+        "        Conv2D(512, (3, 3), activation='relu', padding='same'),\n",
+        "        Conv2D(512, (3, 3), activation='relu', padding='same'),\n",
+        "        Conv2D(512, (3, 3), activation='relu', padding='same'),\n",
+        "        MaxPooling2D((2, 2), strides=(2, 2)),\n",
+        "\n",
+        "        Flatten(),\n",
+        "        Dense(4096, activation='relu'),\n",
+        "        Dense(4096, activation='relu'),\n",
+        "        Dense(10, activation='softmax')  # Output layer for 10 classes in CIFAR-10\n",
+        "    ])\n",
+        "    return model"
+      ],
+      "metadata": {
+        "id": "8J9-x0sjXRFV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Training the model"
+      ],
+      "metadata": {
+        "id": "W7EP2SP4qv39"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "vgg19_scratch = create_vgg19_scratch()\n",
+        "optimizer = tf.keras.optimizers.Adam(learning_rate=0.00001)\n",
+        "vgg19_scratch.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n",
+        "scratch_history = vgg19_scratch.fit(\n",
+        "    train_images, train_labels,\n",
+        "    epochs=70,\n",
+        "    validation_data=(test_images, test_labels)\n",
+        ")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DHMF1fMTXTQi",
+        "outputId": "89b176ad-1406-4e07-89a8-5ee6890b0c2b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/70\n",
+            "1563/1563 [==============================] - 81s 45ms/step - loss: 1.9639 - accuracy: 0.2097 - val_loss: 1.7949 - val_accuracy: 0.2813\n",
+            "Epoch 2/70\n",
+            "1563/1563 [==============================] - 69s 44ms/step - loss: 1.6663 - accuracy: 0.3465 - val_loss: 1.5634 - val_accuracy: 0.3866\n",
+            "Epoch 3/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 1.5258 - accuracy: 0.4094 - val_loss: 1.4458 - val_accuracy: 0.4520\n",
+            "Epoch 4/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 1.4298 - accuracy: 0.4576 - val_loss: 1.3861 - val_accuracy: 0.4803\n",
+            "Epoch 5/70\n",
+            "1563/1563 [==============================] - 71s 46ms/step - loss: 1.3467 - accuracy: 0.4931 - val_loss: 1.3204 - val_accuracy: 0.5016\n",
+            "Epoch 6/70\n",
+            "1563/1563 [==============================] - 71s 46ms/step - loss: 1.2736 - accuracy: 0.5266 - val_loss: 1.2891 - val_accuracy: 0.5179\n",
+            "Epoch 7/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 1.2024 - accuracy: 0.5552 - val_loss: 1.2442 - val_accuracy: 0.5431\n",
+            "Epoch 8/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 1.1329 - accuracy: 0.5864 - val_loss: 1.1586 - val_accuracy: 0.5744\n",
+            "Epoch 9/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 1.0770 - accuracy: 0.6056 - val_loss: 1.1315 - val_accuracy: 0.5835\n",
+            "Epoch 10/70\n",
+            "1563/1563 [==============================] - 69s 44ms/step - loss: 1.0084 - accuracy: 0.6335 - val_loss: 1.1725 - val_accuracy: 0.5853\n",
+            "Epoch 11/70\n",
+            "1563/1563 [==============================] - 68s 44ms/step - loss: 0.9437 - accuracy: 0.6586 - val_loss: 1.1610 - val_accuracy: 0.5986\n",
+            "Epoch 12/70\n",
+            "1563/1563 [==============================] - 68s 44ms/step - loss: 0.8826 - accuracy: 0.6840 - val_loss: 1.0822 - val_accuracy: 0.6276\n",
+            "Epoch 13/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.8199 - accuracy: 0.7046 - val_loss: 1.1908 - val_accuracy: 0.5996\n",
+            "Epoch 14/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.7528 - accuracy: 0.7333 - val_loss: 1.0764 - val_accuracy: 0.6382\n",
+            "Epoch 15/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.6955 - accuracy: 0.7498 - val_loss: 1.1096 - val_accuracy: 0.6473\n",
+            "Epoch 16/70\n",
+            "1563/1563 [==============================] - 68s 44ms/step - loss: 0.6335 - accuracy: 0.7745 - val_loss: 1.2055 - val_accuracy: 0.6227\n",
+            "Epoch 17/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.5781 - accuracy: 0.7949 - val_loss: 1.2069 - val_accuracy: 0.6390\n",
+            "Epoch 18/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.5224 - accuracy: 0.8143 - val_loss: 1.2445 - val_accuracy: 0.6444\n",
+            "Epoch 19/70\n",
+            "1563/1563 [==============================] - 69s 44ms/step - loss: 0.4715 - accuracy: 0.8321 - val_loss: 1.2478 - val_accuracy: 0.6387\n",
+            "Epoch 20/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.4298 - accuracy: 0.8490 - val_loss: 1.3028 - val_accuracy: 0.6462\n",
+            "Epoch 21/70\n",
+            "1563/1563 [==============================] - 69s 44ms/step - loss: 0.3844 - accuracy: 0.8649 - val_loss: 1.4008 - val_accuracy: 0.6401\n",
+            "Epoch 22/70\n",
+            "1563/1563 [==============================] - 69s 44ms/step - loss: 0.3492 - accuracy: 0.8770 - val_loss: 1.3167 - val_accuracy: 0.6428\n",
+            "Epoch 23/70\n",
+            "1563/1563 [==============================] - 69s 44ms/step - loss: 0.3204 - accuracy: 0.8884 - val_loss: 1.4801 - val_accuracy: 0.6511\n",
+            "Epoch 24/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.2864 - accuracy: 0.9012 - val_loss: 1.4595 - val_accuracy: 0.6487\n",
+            "Epoch 25/70\n",
+            "1563/1563 [==============================] - 69s 44ms/step - loss: 0.2539 - accuracy: 0.9127 - val_loss: 1.7098 - val_accuracy: 0.6355\n",
+            "Epoch 26/70\n",
+            "1563/1563 [==============================] - 69s 44ms/step - loss: 0.2380 - accuracy: 0.9183 - val_loss: 1.4833 - val_accuracy: 0.6458\n",
+            "Epoch 27/70\n",
+            "1563/1563 [==============================] - 69s 44ms/step - loss: 0.2111 - accuracy: 0.9282 - val_loss: 1.7273 - val_accuracy: 0.6432\n",
+            "Epoch 28/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.1888 - accuracy: 0.9360 - val_loss: 1.6997 - val_accuracy: 0.6493\n",
+            "Epoch 29/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.1830 - accuracy: 0.9384 - val_loss: 1.6955 - val_accuracy: 0.6536\n",
+            "Epoch 30/70\n",
+            "1563/1563 [==============================] - 71s 46ms/step - loss: 0.1584 - accuracy: 0.9473 - val_loss: 1.7181 - val_accuracy: 0.6554\n",
+            "Epoch 31/70\n",
+            "1563/1563 [==============================] - 69s 44ms/step - loss: 0.1554 - accuracy: 0.9479 - val_loss: 1.8023 - val_accuracy: 0.6508\n",
+            "Epoch 32/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.1372 - accuracy: 0.9545 - val_loss: 1.8693 - val_accuracy: 0.6439\n",
+            "Epoch 33/70\n",
+            "1563/1563 [==============================] - 71s 46ms/step - loss: 0.1328 - accuracy: 0.9569 - val_loss: 1.8244 - val_accuracy: 0.6584\n",
+            "Epoch 34/70\n",
+            "1563/1563 [==============================] - 69s 44ms/step - loss: 0.1263 - accuracy: 0.9583 - val_loss: 1.7487 - val_accuracy: 0.6574\n",
+            "Epoch 35/70\n",
+            "1563/1563 [==============================] - 69s 44ms/step - loss: 0.1146 - accuracy: 0.9620 - val_loss: 1.8116 - val_accuracy: 0.6535\n",
+            "Epoch 36/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.1064 - accuracy: 0.9655 - val_loss: 2.0310 - val_accuracy: 0.6449\n",
+            "Epoch 37/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.1053 - accuracy: 0.9654 - val_loss: 1.9424 - val_accuracy: 0.6543\n",
+            "Epoch 38/70\n",
+            "1563/1563 [==============================] - 68s 44ms/step - loss: 0.0983 - accuracy: 0.9682 - val_loss: 1.8256 - val_accuracy: 0.6615\n",
+            "Epoch 39/70\n",
+            "1563/1563 [==============================] - 68s 44ms/step - loss: 0.0940 - accuracy: 0.9685 - val_loss: 1.9305 - val_accuracy: 0.6640\n",
+            "Epoch 40/70\n",
+            "1563/1563 [==============================] - 68s 44ms/step - loss: 0.0903 - accuracy: 0.9707 - val_loss: 1.9351 - val_accuracy: 0.6324\n",
+            "Epoch 41/70\n",
+            "1563/1563 [==============================] - 69s 44ms/step - loss: 0.0897 - accuracy: 0.9707 - val_loss: 2.1238 - val_accuracy: 0.6465\n",
+            "Epoch 42/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.0867 - accuracy: 0.9719 - val_loss: 1.9466 - val_accuracy: 0.6526\n",
+            "Epoch 43/70\n",
+            "1563/1563 [==============================] - 69s 44ms/step - loss: 0.0797 - accuracy: 0.9735 - val_loss: 1.9463 - val_accuracy: 0.6611\n",
+            "Epoch 44/70\n",
+            "1563/1563 [==============================] - 69s 44ms/step - loss: 0.0824 - accuracy: 0.9734 - val_loss: 1.8838 - val_accuracy: 0.6601\n",
+            "Epoch 45/70\n",
+            "1563/1563 [==============================] - 69s 44ms/step - loss: 0.0720 - accuracy: 0.9768 - val_loss: 2.0007 - val_accuracy: 0.6595\n",
+            "Epoch 46/70\n",
+            "1563/1563 [==============================] - 70s 45ms/step - loss: 0.0725 - accuracy: 0.9749 - val_loss: 2.0515 - val_accuracy: 0.6632\n",
+            "Epoch 47/70\n",
+            "1563/1563 [==============================] - 68s 44ms/step - loss: 0.0672 - accuracy: 0.9782 - val_loss: 2.1421 - val_accuracy: 0.6633\n",
+            "Epoch 48/70\n",
+            "1563/1563 [==============================] - 68s 44ms/step - loss: 0.0705 - accuracy: 0.9768 - val_loss: 2.0357 - val_accuracy: 0.6592\n",
+            "Epoch 49/70\n",
+            "1563/1563 [==============================] - 68s 44ms/step - loss: 0.0720 - accuracy: 0.9761 - val_loss: 2.0108 - val_accuracy: 0.6585\n",
+            "Epoch 50/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.0606 - accuracy: 0.9811 - val_loss: 2.0068 - val_accuracy: 0.6638\n",
+            "Epoch 51/70\n",
+            "1563/1563 [==============================] - 68s 44ms/step - loss: 0.0691 - accuracy: 0.9778 - val_loss: 1.9444 - val_accuracy: 0.6603\n",
+            "Epoch 52/70\n",
+            "1563/1563 [==============================] - 68s 44ms/step - loss: 0.0620 - accuracy: 0.9797 - val_loss: 1.9663 - val_accuracy: 0.6658\n",
+            "Epoch 53/70\n",
+            "1563/1563 [==============================] - 68s 44ms/step - loss: 0.0562 - accuracy: 0.9816 - val_loss: 2.1736 - val_accuracy: 0.6540\n",
+            "Epoch 54/70\n",
+            "1563/1563 [==============================] - 69s 44ms/step - loss: 0.0581 - accuracy: 0.9805 - val_loss: 2.0302 - val_accuracy: 0.6696\n",
+            "Epoch 55/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.0545 - accuracy: 0.9824 - val_loss: 2.3498 - val_accuracy: 0.6616\n",
+            "Epoch 56/70\n",
+            "1563/1563 [==============================] - 69s 44ms/step - loss: 0.0583 - accuracy: 0.9806 - val_loss: 2.0120 - val_accuracy: 0.6667\n",
+            "Epoch 57/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.0526 - accuracy: 0.9827 - val_loss: 2.0945 - val_accuracy: 0.6697\n",
+            "Epoch 58/70\n",
+            "1563/1563 [==============================] - 68s 44ms/step - loss: 0.0534 - accuracy: 0.9826 - val_loss: 2.2315 - val_accuracy: 0.6702\n",
+            "Epoch 59/70\n",
+            "1563/1563 [==============================] - 70s 45ms/step - loss: 0.0494 - accuracy: 0.9835 - val_loss: 2.4048 - val_accuracy: 0.6433\n",
+            "Epoch 60/70\n",
+            "1563/1563 [==============================] - 69s 44ms/step - loss: 0.0481 - accuracy: 0.9848 - val_loss: 2.1883 - val_accuracy: 0.6684\n",
+            "Epoch 61/70\n",
+            "1563/1563 [==============================] - 68s 44ms/step - loss: 0.0506 - accuracy: 0.9835 - val_loss: 2.1281 - val_accuracy: 0.6671\n",
+            "Epoch 62/70\n",
+            "1563/1563 [==============================] - 68s 44ms/step - loss: 0.0482 - accuracy: 0.9842 - val_loss: 2.1164 - val_accuracy: 0.6606\n",
+            "Epoch 63/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.0470 - accuracy: 0.9840 - val_loss: 2.1156 - val_accuracy: 0.6744\n",
+            "Epoch 64/70\n",
+            "1563/1563 [==============================] - 70s 45ms/step - loss: 0.0500 - accuracy: 0.9833 - val_loss: 2.0449 - val_accuracy: 0.6776\n",
+            "Epoch 65/70\n",
+            "1563/1563 [==============================] - 68s 44ms/step - loss: 0.0451 - accuracy: 0.9855 - val_loss: 2.0069 - val_accuracy: 0.6700\n",
+            "Epoch 66/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.0462 - accuracy: 0.9848 - val_loss: 2.0895 - val_accuracy: 0.6831\n",
+            "Epoch 67/70\n",
+            "1563/1563 [==============================] - 73s 46ms/step - loss: 0.0431 - accuracy: 0.9853 - val_loss: 2.1603 - val_accuracy: 0.6723\n",
+            "Epoch 68/70\n",
+            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.0448 - accuracy: 0.9854 - val_loss: 1.9228 - val_accuracy: 0.6750\n",
+            "Epoch 69/70\n",
+            "1563/1563 [==============================] - 68s 44ms/step - loss: 0.0411 - accuracy: 0.9869 - val_loss: 2.2212 - val_accuracy: 0.6759\n",
+            "Epoch 70/70\n",
+            "1563/1563 [==============================] - 68s 43ms/step - loss: 0.0422 - accuracy: 0.9862 - val_loss: 2.2184 - val_accuracy: 0.6535\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Printing the training and testing accuracy"
+      ],
+      "metadata": {
+        "id": "iZU4To80qs0E"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_loss = scratch_history.history['loss']\n",
+        "train_acc = scratch_history.history['accuracy']\n",
+        "val_loss = scratch_history.history['val_loss']\n",
+        "val_acc = scratch_history.history['val_accuracy']\n",
+        "\n",
+        "print(f\"Final Training Accuracy: {train_acc[-1]*100:.2f}%\")\n",
+        "print(f\"Final Testing Accuracy: {val_acc[-1]*100:.2f}%\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vAh60GtGgJLD",
+        "outputId": "f9256a30-4d8c-4644-a4df-d0ab955642f8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Final Training Accuracy: 98.62%\n",
+            "Final Testing Accuracy: 65.35%\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Plotting the accuracy and loss"
+      ],
+      "metadata": {
+        "id": "N9x5QQZPqlKR"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "epochs = range(1, len(train_loss) + 1)\n",
+        "\n",
+        "plt.figure(figsize=(12, 5))\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.plot(epochs, train_acc, 'b-', label='Training accuracy')\n",
+        "plt.plot(epochs, val_acc, 'r-', label='Testing accuracy')\n",
+        "plt.title('Training and Testing Accuracy')\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('Accuracy')\n",
+        "plt.legend()\n",
+        "\n",
+        "plt.subplot(1, 2, 2)\n",
+        "plt.plot(epochs, train_loss, 'b-', label='Training loss')\n",
+        "plt.plot(epochs, val_loss, 'r-', label='Testing loss')\n",
+        "plt.title('Training and Testing Loss')\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.legend()\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 494
+        },
+        "id": "hjLK5nyGgK6K",
+        "outputId": "557b38af-2e4c-4f5f-8013-b54273cb814f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x500 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file