code: def __init__(self):
      super().__init__()
      self.model = keras.Sequential(
          [
              layers.Input(shape=(28, 28, 1)),
              layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
              layers.MaxPooling2D(pool_size=(2, 2)),
              layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
              layers.MaxPooling2D(pool_size=(2, 2)),
              layers.Flatten(),
              layers.Dropout(0.5),
              layers.Dense(num_classes),
          ]
      )
  
file_path: keras/examples/demo_custom_torch_workflow.py
imports: import os
  import torch
  import torch.nn
  import torch.optim
  from keras import layers
  import keras
  import numpy
label_desc: Use image node_id 1 to create a new MyModel object from inherited base classes: nn with example: obj = MyModel()