message: Removing eager test for dataset from large tensors.
diff: @@ -29,7 +29,6 @@ from tensorflow.python.data.experimental.ops import scan_ops
  from tensorflow.python.data.experimental.ops import threadpool
  from tensorflow.python.data.kernel_tests import test_base
  from tensorflow.python.data.ops import dataset_ops
  -from tensorflow.python.eager import context
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  @@ -113,20 +112,14 @@ class OptimizeDatasetTest(test_base.DatasetTestBase, parameterized.TestCase):
  get_next = self.getNext(dataset)
  self.evaluate(get_next())
  - def testOptimizationLargeInputFromTensor(self):
  - def dataset_fn(input_t):
  + # TODO(b/123300735): Add eager coverage for the following tests.
  + def testSkipEagerOptimizationLargeInputFromTensor(self):
  + input_t = array_ops.placeholder(dtypes.int32, (None, None, None))
  dataset = dataset_ops.Dataset.from_tensors(input_t)
  options = dataset_ops.Options()
  options.experimental_optimization.apply_default_optimizations = False
  - return dataset.with_options(options)
  -
  - if context.executing_eagerly():
  - input_t = np.ones([512, 1024, 1025], np.int32)
  - get_next = self.getNext(dataset_fn(input_t))
  - self.evaluate(get_next())
  - else:
  - input_t = array_ops.placeholder(dtypes.int32, (None, None, None))
  - iterator = dataset_ops.make_initializable_iterator(dataset_fn(input_t))
  + dataset = dataset.with_options(options)
  + iterator = dataset_ops.make_initializable_iterator(dataset)
  init_op = iterator.initializer
  get_next = iterator.get_next()
  @@ -134,20 +127,14 @@ class OptimizeDatasetTest(test_base.DatasetTestBase, parameterized.TestCase):
  sess.run(init_op, {input_t: np.ones([512, 1024, 1025], np.int32)})
  self.evaluate(get_next)
  - def testOptimizationLargeInputFromTensorSlices(self):
  - def dataset_fn(input_t):
  + # TODO(b/117581999): Add eager coverage for the following tests.
  + def testSkipEagerOptimizationLargeInputFromTensorSlices(self):
  + input_t = array_ops.placeholder(dtypes.int32, (None, None, None, None))
  dataset = dataset_ops.Dataset.from_tensor_slices(input_t)
  options = dataset_ops.Options()
  options.experimental_optimization.apply_default_optimizations = False
  - return dataset.with_options(options)
  -
  - if context.executing_eagerly():
  - input_t = np.ones([1, 512, 1024, 1025], np.int32)
  - get_next = self.getNext(dataset_fn(input_t))
  - self.evaluate(get_next())
  - else:
  - input_t = array_ops.placeholder(dtypes.int32, (None, None, None, None))
  - iterator = dataset_ops.make_initializable_iterator(dataset_fn(input_t))
  + dataset = dataset.with_options(options)
  + iterator = dataset_ops.make_initializable_iterator(dataset)
  init_op = iterator.initializer
  get_next = iterator.get_next()
  