code: def test_tpu_estimator_simple_lifecycle(
      self, use_tpu, subnetwork_generator, want_loss
  ):
      config = tf.compat.v1.estimator.tpu.RunConfig(
          master="", tf_random_seed=42
      )
      estimator = TPUEstimator(
          # TODO: Add test with estimator Head v2.
          head=make_regression_head(use_tpu),
          subnetwork_generator=subnetwork_generator,
          max_iteration_steps=10,
          model_dir=self.test_subdirectory,
          config=config,
          use_tpu=use_tpu,
          train_batch_size=64 if use_tpu else 0,
      )
      max_steps = 30
  
      xor_features = [[1.0, 0.0], [0.0, 0], [0.0, 1.0], [1.0, 1.0]]
      xor_labels = [[1.0], [0.0], [1.0], [0.0]]
      train_input_fn = tu.dummy_input_fn(xor_features, xor_labels)
  
      # Train.
      estimator.train(
          input_fn=train_input_fn,
          steps=None,
          max_steps=max_steps,
          hooks=None,
      )
  
      # Evaluate.
      eval_results = estimator.evaluate(
          input_fn=train_input_fn, steps=1, hooks=None
      )
  
      # Predict.
      predictions = estimator.predict(
          input_fn=tu.dataset_input_fn(
              features=[0.0, 0.0], return_dataset=True
          )
      )
      # We need to iterate over all the predictions before moving on, otherwise
      # the TPU will not be shut down.
      for prediction in predictions:
          self.assertIsNotNone(prediction["predictions"])
  
      # Export SavedModel.
      def serving_input_fn():
          """Input fn for serving export, starting from serialized example."""
          serialized_example = tf.compat.v1.placeholder(
              dtype=tf.string, shape=(None), name="serialized_example"
          )
          return tf.estimator.export.ServingInputReceiver(
              features={
                  "x": tf.constant([[0.0, 0.0]], name="serving_x")
              },
              receiver_tensors=serialized_example,
          )
  
      estimator.export_saved_model(
          export_dir_base=estimator.model_dir,
          serving_input_receiver_fn=serving_input_fn,
      )
  
      self.assertAlmostEqual(want_loss, eval_results["loss"], places=2)
      self.assertEqual(max_steps, eval_results["global_step"])
      self.assertEqual(2, eval_results["iteration"])
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import itertools
  import json
  import os
  from absl.testing import parameterized
  from adanet import tf_compat
  from adanet.core import testing_utils
  from adanet.core.tpu_estimator import TPUEstimator
  from adanet.subnetwork import Builder
  from adanet.subnetwork import Report
  from adanet.subnetwork import SimpleGenerator
  from adanet.subnetwork import Subnetwork
  import numpy
  import tensorflow.compat.v2
  from tensorflow_estimator.python.estimator.head import regression_head
label_desc: Use image node_id 3 for calling the TPUEstimatorTest obj's underlying member method code with example usage: obj.test_tpu_estimator_simple_lifecycle(use_tpu, subnetwork_generator, want_loss) and returns: tf