code: def test_auto_ensemble_estimator_lifecycle(self, use_tpu):
      head = head_lib.regression_head()
      feature_columns = [
          tf.feature_column.numeric_column("xor", shape=2)
      ]
  
      def optimizer_fn():
          optimizer = tf.compat.v1.train.GradientDescentOptimizer(
              learning_rate=0.01
          )
          if use_tpu:
              optimizer = tf.compat.v1.tpu.CrossShardOptimizer(
                  optimizer
              )
          return optimizer
  
      candidate_pool = {
          "tpu_estimator_dnn": _DNNTPUEstimator(
              head=head,
              feature_columns=feature_columns,
              optimizer=optimizer_fn,
              hidden_units=[3],
              use_tpu=True,
          ),
          "tpu_estimator_wider_dnn": _DNNTPUEstimator(
              head=head,
              feature_columns=feature_columns,
              optimizer=optimizer_fn,
              hidden_units=[6],
              use_tpu=True,
          ),
          "estimator_dnn": tf.compat.v1.estimator.DNNEstimator(
              head=head,
              feature_columns=feature_columns,
              optimizer=optimizer_fn,
              hidden_units=[3],
          ),
          "estimator_linear": tf.compat.v1.estimator.LinearEstimator(
              head=head,
              feature_columns=feature_columns,
              optimizer=optimizer_fn,
          ),
      }
  
      run_config = tf.compat.v1.estimator.tpu.RunConfig(
          master="", tf_random_seed=42
      )
      estimator = AutoEnsembleTPUEstimator(
          head=head,
          candidate_pool=candidate_pool,
          max_iteration_steps=10,
          model_dir=self.test_subdirectory,
          config=run_config,
          use_tpu=use_tpu,
          train_batch_size=4,
          force_grow=True,
      )
  
      features = {
          "xor": [[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]
      }
      labels = [[0.0], [1.0], [1.0], [0.0]]
  
      def train_input_fn(params):
          del params  # Unused.
  
          input_features = {}
          for key, feature in features.items():
              input_features[key] = tf.constant(feature, name=key)
          input_labels = tf.constant(labels, name="labels")
          return input_features, input_labels
  
      def test_input_fn(params):
          del params  # Unused.
          return tf.compat.v1.data.Dataset.from_tensor_slices(
              [features["xor"]]
          ).map(lambda f: {"xor": f})
  
      # Train for three iterations.
      estimator.train(input_fn=train_input_fn, max_steps=30)
  
      # Evaluate.
      eval_results = estimator.evaluate(
          input_fn=train_input_fn, steps=1
      )
      self.assertAllClose(30, eval_results["global_step"])
      self.assertAllClose(0.315863, eval_results["loss"], atol=0.3)
  
      # Predict.
      predictions = estimator.predict(input_fn=test_input_fn)
      # 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"
          )
          for key in features:
              features[key] = tf.constant([[0.0, 0.0], [0.0, 0.0]])
          return tf.estimator.export.ServingInputReceiver(
              features=features, receiver_tensors=serialized_example
          )
  
      export_dir_base = os.path.join(self.test_subdirectory, "export")
      export_saved_model_fn = getattr(
          estimator, "export_saved_model", None
      )
      if not callable(export_saved_model_fn):
          export_saved_model_fn = estimator.export_savedmodel
      export_saved_model_fn(
          export_dir_base=export_dir_base,
          serving_input_receiver_fn=serving_input_fn,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import itertools
  import os
  import shutil
  import sys
  from absl import flags
  from absl.testing import parameterized
  from adanet import tf_compat
  from adanet.autoensemble.estimator import AutoEnsembleTPUEstimator
  import numpy
  import tensorflow.compat.v2
  from tensorflow_estimator.python.estimator.canned import dnn
label_desc: Use image node_id 3 for calling the AutoEnsembleTPUEstimatorTest obj's underlying member method code with example usage: obj.test_auto_ensemble_estimator_lifecycle(use_tpu) and returns: optimizer, input_features,  input_labels, tf, tf