code: def _test_train(self, execution_mode=None):
      device, data_format = resnet50_test_util.device_and_data_format()
      model = resnet50.ResNet50(data_format)
      tf.compat.v2.summary.experimental.set_step(
          tf.compat.v1.train.get_or_create_global_step()
      )
      logdir = tempfile.mkdtemp()
      with tf.compat.v2.summary.create_file_writer(
          logdir, max_queue=0, name="t0"
      ).as_default(), tf.compat.v2.summary.record_if(True):
          with tf.device(device), context.execution_mode(
              execution_mode
          ):
              optimizer = tf.compat.v1.train.GradientDescentOptimizer(
                  0.1
              )
              images, labels = resnet50_test_util.random_batch(
                  2, data_format
              )
              apply_gradients(
                  model,
                  optimizer,
                  compute_gradients(model, images, labels),
              )
              self.assertEqual(320, len(model.variables))
              context.async_wait()
      events = events_from_logdir(logdir)
      self.assertEqual(len(events), 2)
      self.assertEqual(events[1].summary.value[0].tag, "loss")
  
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 11 for calling the ResNet50Test obj's underlying member method code with example usage: obj._test_train(execution_mode) without return types