code: def __init__(self):
      super(LeNet, self).__init__()
      self.conv1 = Conv2D(
          filters=16,
          kernel_size=3,
          padding="valid",
          strides=(2, 2),
          input_shape=(None, 28, 28, 1),
          data_format="channels_last",
          trainable=True,
      )
  
      self.bn1 = BatchNormalization()
      self.conv2 = Conv2D(
          filters=16,
          kernel_size=3,
          strides=(2, 2),
          data_format="channels_last",
          padding="valid",
          trainable=True,
      )
      self.bn2 = BatchNormalization()
      self.flatten = Flatten()
      self.fc = Dense(10, trainable=True)
  
file_path: sagemaker-python-sdk/tests/data/tensorflow_mnist/mnist_v2.py
imports: import argparse
  import numpy
  import os
  import json
  import tensorflow
  from tensorflow.keras.layers import Conv2D, BatchNormalization, Dense, Flatten
label_desc: Use image node_id 1 to create a new LeNet object from inherited base classes: tf.keras with example: obj = LeNet()