code: def test_auto_tpu_strategy(self):
      self.setup_py()
      worker_config = machine_config.COMMON_MACHINE_CONFIGS["TPU"]
      script_lines = self.get_preprocessed_entry_point(
          worker_config=worker_config, worker_count=1
      )
      expected_lines = [
          "import os\n",
          "import tensorflow as tf\n",
          'os.environ["TF_KERAS_RUNNING_REMOTELY"]="1"\n',
          "import sys\n",
          "for flag in sys.argv[1:]:\n",
          '  if flag.startswith("TUNER_ID"):\n',
          '    os.environ["KERASTUNER_TUNER_ID"]=flag\n',
          "import json\n",
          "import logging\n",
          "import time\n",
          "logger = logging.getLogger(__name__)\n",
          "logging.basicConfig(level=logging.INFO)\n",
          "def wait_for_tpu_cluster_resolver_ready():\n",
          "  tpu_config_env = os.environ.get('TPU_CONFIG')\n",
          "  if not tpu_config_env:\n",
          (
              "    logging.info('Missing TPU_CONFIG, "
              "use CPU/GPU for training.')\n"
          ),
          "    return None\n",
          "  tpu_node = json.loads(tpu_config_env)\n",
          "  logging.info('Waiting for TPU to be ready: %s.', tpu_node)\n",
          "  num_retries = 40\n",
          "  for i in range(num_retries):\n",
          "    try:\n",
          "      tpu_cluster_resolver = (\n",
          "          tf.distribute.cluster_resolver.TPUClusterResolver(\n",
          "              tpu=[tpu_node['tpu_node_name']],\n",
          "              zone=tpu_node['zone'],\n",
          "              project=tpu_node['project'],\n",
          "              job_name='worker'))\n",
          "      tpu_cluster_resolver_dict = "
          "tpu_cluster_resolver.cluster_spec().as_dict()\n",
          "      if 'worker' in tpu_cluster_resolver_dict:\n",
          (
              "        logging.info('Found TPU worker: %s', "
              "tpu_cluster_resolver_dict)\n"
          ),
          "        return tpu_cluster_resolver\n",
          "    except Exception as e:\n",
          "      if i < num_retries - 1:\n",
          "        logging.info('Still waiting for provisioning of TPU VM"
          " instance.')\n",
          "      else:\n",
          "        # Preserves the traceback.\n",
          (
              "        raise RuntimeError('Failed to schedule TPU: "
              "{}'.format(e))\n"
          ),
          "    time.sleep(10)\n",
          "  raise RuntimeError('Failed to schedule TPU.')\n",
          "resolver = wait_for_tpu_cluster_resolver_ready()\n",
          "tf.config.experimental_connect_to_cluster(resolver)\n",
          "tf.tpu.experimental.initialize_tpu_system(resolver)\n",
          "strategy = tf.distribute.experimental.TPUStrategy(resolver)\n",
          "tf.distribute.experimental_set_strategy(strategy)\n",
          'exec(open("{}").read())\n'.format(self.entry_point_name),
      ]
      self.assert_and_cleanup(expected_lines, script_lines)
  
file_path: cloud/src/python/tensorflow_cloud/core/tests/unit/preprocess_test.py
imports: import os
  from absl.testing import absltest
  import mock
  from tensorflow_cloud.core import machine_config
  from tensorflow_cloud.core import preprocess
label_desc: Use image node_id 8 for calling the TestPreprocess obj's underlying member method code with example usage: obj.test_auto_tpu_strategy() without return types