text: import os
  
  from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, MaxPool2D, Flatten, Dense, Dropout
  from tensorflow.keras import Model
  from tensorflow.keras.utils import plot_model
  
  inputs = Input(shape=(224, 224, 3))
  x = Conv2D(filters=96, kernel_size=11, strides=4, padding='same', activation='relu')(inputs)
  x = BatchNormalization()(x)
  x = MaxPool2D(pool_size=3, strides=2)(x)
  x = Conv2D(filters=256, kernel_size=5, padding='same', activation='relu')(x)
  x = BatchNormalization()(x)
  x = MaxPool2D(pool_size=3, strides=2)(x)
  x = Conv2D(filters=384, kernel_size=3, padding='same', activation='relu')(x)
  x = Conv2D(filters=384, kernel_size=3, padding='same', activation='relu')(x)
  x = Conv2D(filters=256, kernel_size=3, padding='same', activation='relu')(x)
  x = BatchNormalization()(x)
  x = MaxPool2D(pool_size=3, strides=2)(x)
  x = Flatten()(x)
  x = Dense(units=4096, activation='relu')(x)
  x = Dense(units=4096, activation='relu')(x)
  x = Dropout(rate=0.5)(x)
  outputs = Dense(units=1000, activation='softmax')(x)
  
  
  model = Model(inputs = inputs, outputs=outputs)
  
  plot_model(model, show_shapes=True)
  