code: def test_no_garbage(self):
      device, data_format = resnet50_test_util.device_and_data_format()
      model = resnet50.ResNet50(data_format)
      optimizer = tf.compat.v1.train.GradientDescentOptimizer(0.1)
      with tf.device(device):
          images, labels = resnet50_test_util.random_batch(
              2, data_format
          )
          gc.disable()
          # Warm up. Note that this first run does create significant amounts of
          # garbage to be collected. The hope is that this is a build-only effect,
          # and a subsequent training loop will create nothing which needs to be
          # collected.
          apply_gradients(
              model, optimizer, compute_gradients(model, images, labels)
          )
          gc.collect()
          previous_gc_debug_flags = gc.get_debug()
          gc.set_debug(gc.DEBUG_SAVEALL)
          for _ in range(2):
              # Run twice to ensure that garbage that is created on the first
              # iteration is no longer accessible.
              apply_gradients(
                  model,
                  optimizer,
                  compute_gradients(model, images, labels),
              )
          gc.collect()
          # There should be no garbage requiring collection.
          self.assertEqual(0, len(gc.garbage))
          gc.set_debug(previous_gc_debug_flags)
          gc.enable()
  
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 14 for calling the ResNet50Test obj's underlying member method code with example usage: obj.test_no_garbage() without return types