code: import tensorflow as tf
  
  model = tf.keras.models.Sequential([
          # YOUR CODE HERE
          tf.keras.layers.BatchNormalization(input_shape=(32, 32, 3)),
          tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation="relu"),
          tf.keras.layers.MaxPool2D(2, 2),
          tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation="relu"),
          tf.keras.layers.MaxPool2D(2, 2),
          tf.keras.layers.Conv2D(filters=256, kernel_size=(3, 3), activation="relu"),
          tf.keras.layers.MaxPool2D(2, 2),
          tf.keras.layers.Flatten(),
          tf.keras.layers.Dropout(0.5),
          tf.keras.layers.Dense(units=128, activation="relu"),
          tf.keras.layers.Dense(10, activation="softmax")
      ])
  
  model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["acc"])
  
  from tensorflow.keras.datasets import cifar10
  
  (X_train, y_train), (X_test, y_test) = cifar10.load_data()
  
  from cnn_raccoon import inspector
  inspector(model=model, images=X_train[:10], number_of_classes=10, engine="keras")
  
apis: tensorflow.keras.datasets.cifar10.load_data tensorflow.keras.layers.Flatten tensorflow.keras.layers.Dropout tensorflow.keras.layers.BatchNormalization tensorflow.keras.layers.Dense tensorflow.keras.layers.Conv2D tensorflow.keras.layers.MaxPool2D