code: def __init__(self, num_classes=1000):
      super(AlexNet_v2, self).__init__()
      self.features = Sequential(
          [
              layers.ZeroPadding2D(
                  ((1, 2), (1, 2))
              ),  # output(None, 227, 227, 3)
              layers.Conv2D(
                  48, kernel_size=11, strides=4, activation="relu"
              ),  # output(None, 55, 55, 48)
              layers.MaxPool2D(
                  pool_size=3, strides=2
              ),  # output(None, 27, 27, 48)
              layers.Conv2D(
                  128, kernel_size=5, padding="same", activation="relu"
              ),  # output(None, 27, 27, 128)
              layers.MaxPool2D(
                  pool_size=3, strides=2
              ),  # output(None, 13, 13, 128)
              layers.Conv2D(
                  192, kernel_size=3, padding="same", activation="relu"
              ),  # output(None, 13, 13, 192)
              layers.Conv2D(
                  192, kernel_size=3, padding="same", activation="relu"
              ),  # output(None, 13, 13, 192)
              layers.Conv2D(
                  128, kernel_size=3, padding="same", activation="relu"
              ),  # output(None, 13, 13, 128)
              layers.MaxPool2D(pool_size=3, strides=2),
          ]
      )  # output(None, 6, 6, 128)
  
      self.flatten = layers.Flatten()
      self.classifier = Sequential(
          [
              layers.Dropout(0.2),
              layers.Dense(
                  1024, activation="relu"
              ),  # output(None, 2048)
              layers.Dropout(0.2),
              layers.Dense(
                  128, activation="relu"
              ),  # output(None, 2048)
              layers.Dense(num_classes),  # output(None, 5)
              layers.Softmax(),
          ]
      )
  
imports: from tensorflow.keras import layers, models, Model, Sequential
label_desc: Use image node_id 1 to create a new AlexNet_v2 object from inherited base classes: Model with example: obj = AlexNet_v2(num_classes)