code: def test_model_training(self, model_num_channel):
      # Define a model.
      input_shape = (100, 221, model_num_channel)
      model = keras_modeling.inceptionv3(
          input_shape=input_shape, init_backbone_with_imagenet=False
      )
  
      # Generate random input and target data.
      x_train = np.random.rand(32, *input_shape)
      y_train = np.random.randint(dv_constants.NUM_CLASSES, size=(32,))
  
      # Compile the model.
      model.compile(
          optimizer="adam", loss="sparse_categorical_crossentropy"
      )
  
      # Train the model for a few steps.
      model.train_on_batch(x_train, y_train)
      model.train_on_batch(x_train, y_train)
  
      # Check that the loss has decreased after training.
      loss_before = model.evaluate(x_train, y_train, verbose=0)
      model.train_on_batch(x_train, y_train)
      loss_after = model.evaluate(x_train, y_train, verbose=0)
      self.assertNotEqual(
          loss_after, loss_before, "Training is expected to change loss"
      )
  
imports: from typing import Tuple
  from absl.testing import absltest
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from deepvariant import dv_config
  from deepvariant import dv_constants
  from deepvariant import keras_modeling
  from third_party.nucleus.testing import test_utils
label_desc: Use image node_id 3 for calling the KerasModelingTest obj's underlying member method code with example usage: obj.test_model_training(model_num_channel) without return types