code: def _benchmark_eager_train(
      self,
      label,
      make_iterator,
      device_and_format,
      defun=False,
      execution_mode=None,
  ):
      with context.execution_mode(execution_mode):
          device, data_format = device_and_format
          for batch_size in self._train_batch_sizes():
              (images, labels) = resnet50_test_util.random_batch(
                  batch_size, data_format
              )
              model = resnet50.ResNet50(data_format)
              # TODO(b/161911585): tf_to_corert MLIR lowering pipeline should handle
              # case when momentum is not set.
              optimizer = tf.keras.optimizers.SGD(0.1, 0.1)
              apply_grads = apply_gradients
              if defun:
                  model.call = tf.function(model.call)
                  apply_grads = tf.function(apply_gradients)
  
              num_burn = 3
              num_iters = 10
              with tf.device(device):
                  iterator = make_iterator((images, labels))
                  for _ in range(num_burn):
                      (images, labels) = iterator.next()
                      apply_grads(
                          model,
                          optimizer,
                          compute_gradients(model, images, labels),
                      )
                  if execution_mode:
                      context.async_wait()
                  self._force_device_sync()
                  gc.collect()
  
                  start = time.time()
                  for _ in range(num_iters):
                      (images, labels) = iterator.next()
                      apply_grads(
                          model,
                          optimizer,
                          compute_gradients(model, images, labels),
                      )
                  if execution_mode:
                      context.async_wait()
                  self._force_device_sync()
                  self._report(
                      label,
                      start,
                      num_iters,
                      device,
                      batch_size,
                      data_format,
                  )
  
imports: import gc
  import os
  import tempfile
  import time
  import tensorflow
  from tensorflow.python.client import device_lib
  from tensorflow.python.eager import context
  from tensorflow.python.eager import record
  from tensorflow.python.eager.benchmarks.resnet50 import resnet50
  from tensorflow.python.eager.benchmarks.resnet50 import resnet50_test_util
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 8 for calling the ResNet50Benchmarks obj's underlying member method code with example usage: obj._benchmark_eager_train(label, make_iterator, device_and_format, defun, execution_mode) without return types