docstr: Tests b/122795064.
docstr_tok: ["Tests","b\/122795064","."]
code: def test_extra_checkpoint_saver_hook(self):
      """Tests b/122795064."""
  
      features = {"input_1": [[1.0, 0.0]]}
      labels = [[1.0]]
  
      run_config = tf.estimator.RunConfig(tf_random_seed=42)
      head = tf.contrib.estimator.binary_classification_head(
          loss_reduction=tf.losses.Reduction.SUM_OVER_BATCH_SIZE
      )
  
      optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
      feature_columns = [
          tf.feature_column.numeric_column("input_1", shape=[2])
      ]
  
      estimator = AutoEnsembleEstimator(
          head=head,
          candidate_pool=[
              tf.estimator.LinearClassifier(
                  n_classes=2,
                  feature_columns=feature_columns,
                  optimizer=optimizer,
              ),
              tf.estimator.DNNClassifier(
                  n_classes=2,
                  feature_columns=feature_columns,
                  optimizer=optimizer,
                  hidden_units=[3],
              ),
          ],
          max_iteration_steps=3,
          force_grow=True,
          model_dir=self.test_subdirectory,
          config=run_config,
      )
  
      ckpt_dir = os.path.join(self.test_subdirectory)
      hooks = [tf.train.CheckpointSaverHook(ckpt_dir, save_steps=1)]
  
      def train_input_fn():
          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
  
      estimator.train(input_fn=train_input_fn, max_steps=6, hooks=hooks)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import contextlib
  import os
  import shutil
  import sys
  from absl import flags
  from absl import logging
  from absl.testing import parameterized
  from adanet import tf_compat
  from adanet.autoensemble.common import AutoEnsembleSubestimator
  from adanet.autoensemble.estimator import _GeneratorFromCandidatePool
  from adanet.autoensemble.estimator import AutoEnsembleEstimator
  import tensorflow.compat.v1
  from tensorflow.python.estimator.export import export
label_desc: Use image node_id 5 for calling the AutoEnsembleEstimatorTest obj's underlying member method code with example usage: obj.test_extra_checkpoint_saver_hook() and returns: input_features,  input_labels