message: Remove the staled test case from checkpointing_test.
diff: @@ -17,86 +17,24 @@ from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  -import functools
  import os
  from absl.testing import parameterized
  from tensorflow.python.distribute import combinations
  from tensorflow.python.distribute import strategy_combinations
  -from tensorflow.python.distribute import tpu_strategy
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager import def_function
  from tensorflow.python.eager import test
  -from tensorflow.python.framework import constant_op
  -from tensorflow.python.keras.engine import training
  -from tensorflow.python.keras.layers import core
  from tensorflow.python.keras.optimizer_v2 import adam
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import variables as variables_lib
  -from tensorflow.python.training import adam as adam_v1
  -from tensorflow.python.training import checkpoint_management
  -from tensorflow.python.training import training_util
  -from tensorflow.python.training.tracking import tracking
  from tensorflow.python.training.tracking import util as trackable_utils
  -class NonLayerTrackable(tracking.AutoTrackable):
  -
  - def __init__(self):
  - super(NonLayerTrackable, self).__init__()
  - self.a_variable = trackable_utils.add_variable(
  - self, name="a_variable", shape=[])
  -
  -
  -class Subclassed(training.Model):
  - """A concrete Model for testing."""
  -
  - def __init__(self):
  - super(Subclassed, self).__init__()
  - self._named_dense = core.Dense(1, use_bias=True)
  - self._second = core.Dense(1, use_bias=False)
  - # We can still track Trackables which aren't Layers.
  - self._non_layer = NonLayerTrackable()
  -
  - def call(self, values):
  - ret = self._second(self._named_dense(values))
  - return ret
  -
  -
  class TrainingCheckpointTests(test.TestCase, parameterized.TestCase):
  - def testEagerTPUDistributionStrategy(self):
  - self.skipTest("b/121387144")
  - num_training_steps = 10
  - checkpoint_directory = self.get_temp_dir()
  - checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
  -
  - def _train_fn(optimizer, model):
  - input_value = constant_op.constant([[3.]])
  - optimizer.minimize(
  - functools.partial(model, input_value),
  - global_step=root.optimizer_step)
  -
  - for training_continuation in range(3):
  - strategy = tpu_strategy.TPUStrategy()
  - with strategy.scope():
  - model = Subclassed()
  - optimizer = adam_v1.AdamOptimizer(0.001)
  - root = trackable_utils.Checkpoint(
  - optimizer=optimizer, model=model,
  - optimizer_step=training_util.get_or_create_global_step())
  - root.restore(checkpoint_management.latest_checkpoint(
  - checkpoint_directory))
  -
  - for _ in range(num_training_steps):
  - strategy.extended.call_for_each_replica(
  - functools.partial(_train_fn, optimizer, model))
  - root.save(file_prefix=checkpoint_prefix)
  - self.assertEqual((training_continuation + 1) * num_training_steps,
  - root.optimizer_step.numpy())
  -
  @combinations.generate(
  combinations.combine(
  distribution=[
  