code: def setUp(self):
      super().setUp()
      # Clear all cached tensors
      context._reset_context()
      # Run garbage collection to free any tensors from previous
      # runs.
      gc.collect()
  
      # Run a small program and copy the result to CPU.
      # This causes deferred deallocations to be flushed and new memory to be
      # allocated in a less fragmented way.
      # Turning deferred deallocations off no longer seems to work.
      assert (
          tf.reduce_sum(
              tf.random.uniform((1024, 128), dtype=tf.float32)
          ).numpy()
          > 1.0
      )
  
      self.resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
          tpu="", project=None, zone=None
      )
  
      tf.config.experimental_connect_to_cluster(self.resolver)
      tf.tpu.experimental.initialize_tpu_system(self.resolver)
  
file_path: tensorflow-upstream/tensorflow/python/distribute/integration_test/tpu_memory_test.py
imports: import gc
  import tensorflow
  from tensorflow.python.eager import context
  from tensorflow.python.platform import flags
label_desc: Use image node_id 1 for calling the TpuMemoryTest obj's underlying member method code with example usage: obj.setUp() without return types