code: def benchmark_train(
      self, num_samples, batch_size, data=None, label=None
  ):
      if data is None:
          # Generate default data if not provided.
          if isinstance(self.input_shape[0], (tuple, list)):
              # The layer has multiple inputs.
              data = []
              for data_shape in self.input_shape:
                  data_shape = [num_samples] + list(data_shape)
                  data.append(np.random.normal(size=data_shape))
          else:
              data_shape = [num_samples] + list(self.input_shape)
              data = [np.random.normal(size=data_shape)]
  
      if label is None:
          # Generate default label if not provided.
          if self.flat_call_inputs:
              # Scale by a small factor to avoid zero gradients.
              label = (
                  keras.backend.convert_to_numpy(
                      self._keras_layer(*data)
                  )
                  * 1.001
              )
          else:
              label = (
                  keras.backend.convert_to_numpy(
                      self._keras_layer(data)
                  )
                  * 1.001
              )
  
      num_iterations = num_samples // batch_size - 1
      callback = KerasCoreBenchmarkMetricsCallback(
          stop_batch=num_iterations
      )
      tf_keras_callback = TFKerasBenchmarkMetricsCallback(
          stop_batch=num_iterations
      )
  
      self._keras_model.fit(
          data,
          label,
          batch_size=batch_size,
          callbacks=[callback],
      )
      self._tf_keras_model.fit(
          data,
          label,
          batch_size=batch_size,
          callbacks=[tf_keras_callback],
      )
  
      keras_throughput = (
          callback._callback.state["throughput"] * batch_size
      )
      tf_keras_throughput = (
          tf_keras_callback._callback.state["throughput"] * batch_size
      )
      print(
          f"Keras 3 throughput of forward & backward pass of "
          f"{self.layer_name}: {keras_throughput:.2f} samples/sec."
      )
      print(
          f"TF Keras  throughput of forward & backward pass of "
          f"{self.layer_name}: {tf_keras_throughput:.2f} samples/sec."
      )
  
imports: import time
  import numpy
  import tensorflow
  from absl import flags
  import keras
label_desc: Use image node_id 5 for calling the LayerBenchmark obj's underlying member method code with example usage: obj.benchmark_train(num_samples, batch_size, data, label) without return types