message: Remove explicit tape watching in backprop tests.
diff: @@ -23,7 +23,6 @@ import numpy as np
  from tensorflow.python import pywrap_tensorflow
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager import context
  -from tensorflow.python.eager import tape
  from tensorflow.python.eager import test
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  @@ -87,7 +86,6 @@ class BackpropTest(test.TestCase):
  initial_value=constant_op.constant(1.0), name='x')
  def fn():
  - tape.watch_variable(x)
  b = constant_op.constant(2.0)
  c = math_ops.add(x.value(), b)
  return math_ops.add(c, constant_op.constant(3.0))
  @@ -194,7 +192,6 @@ class BackpropTest(test.TestCase):
  initial_value=random_init, dtype=dtypes.float32, name='embedding')
  def f():
  - tape.watch_variable(embedding)
  embedded_x = embedding_ops.embedding_lookup(embedding, x)
  return constant_op.constant(1.0, dtypes.float32) - embedded_x
  @@ -404,7 +401,6 @@ class BackpropTest(test.TestCase):
  def f():
  with context.device('gpu:0'):
  - tape.watch_variable(v)
  return v.read_value()
  self.assertEqual(
  @@ -784,7 +780,6 @@ class BackpropTest(test.TestCase):
  initial_value=array_ops.constant([1.0]), name='x')
  def fn():
  - tape.watch_variable(x)
  a = math_ops.add(x.value(), 1.0)
  # Make sure convert_to_tensor works correctly with list of TensorNodes.
  b = array_ops.stack([a, a], axis=0)
  @@ -931,12 +926,12 @@ class BackpropTest(test.TestCase):
  with ops.Graph().as_default(), self.test_session():
  t = constant_op.constant(1, dtype=dtypes.float32, shape=(10, 4))
  x = constant_op.constant(2, dtype=dtypes.float32, shape=(10, 4))
  - with backprop.GradientTape() as gt:
  + with backprop.GradientTape() as tape:
  tape.watch(x)
  x1, _ = array_ops.split(x, num_or_size_splits=2, axis=1)
  y1 = x1**2
  y = array_ops.concat([y1, t], axis=1)
  - return self.evaluate(gt.gradient(y, x))
  + return self.evaluate(tape.gradient(y, x))
  grad1 = get_grad()
  grad2 = get_grad()
  