message: Fixes ArgMax deprecation warnings in test
  The recent commit marks the `dimension` argument of argmax and
  argmin as deprecated, which are used in the corresponding tests and so
  this brings up warnings when the tests are run. The tests should be
  using the `axis` argument instead.
diff: @@ -29,12 +29,12 @@ class ArgMaxTest(test.TestCase):
  def _testArg(self,
  method,
  x,
  - dimension,
  + axis,
  expected_values,
  use_gpu=False,
  expected_err_re=None):
  with self.test_session(use_gpu=use_gpu):
  - ans = method(x, dimension=dimension)
  + ans = method(x, axis=axis)
  if expected_err_re is None:
  tf_ans = ans.eval()
  # Defaults to int64 output.
  @@ -48,27 +48,26 @@ class ArgMaxTest(test.TestCase):
  def _testBothArg(self,
  method,
  x,
  - dimension,
  + axis,
  expected_values,
  expected_err_re=None):
  - self._testArg(method, x, dimension, expected_values, True, expected_err_re)
  - self._testArg(method, x, dimension, expected_values, False, expected_err_re)
  + self._testArg(method, x, axis, expected_values, True, expected_err_re)
  + self._testArg(method, x, axis, expected_values, False, expected_err_re)
  def _testBasic(self, dtype):
  x = np.asarray(100 * np.random.randn(200), dtype=dtype)
  - # Check that argmin and argmax match numpy along the primary
  - # dimension
  + # Check that argmin and argmax match numpy along the primary axis
  self._testBothArg(math_ops.argmax, x, 0, x.argmax())
  self._testBothArg(math_ops.argmin, x, 0, x.argmin())
  def _testDim(self, dtype):
  x = np.asarray(100 * np.random.randn(3, 2, 4, 5, 6), dtype=dtype)
  - # Check that argmin and argmax match numpy along all dimensions
  - for dim in range(-5, 5):
  - self._testBothArg(math_ops.argmax, x, dim, x.argmax(dim))
  - self._testBothArg(math_ops.argmin, x, dim, x.argmin(dim))
  + # Check that argmin and argmax match numpy along all axes
  + for axis in range(-5, 5):
  + self._testBothArg(math_ops.argmax, x, axis, x.argmax(axis))
  + self._testBothArg(math_ops.argmin, x, axis, x.argmin(axis))
  def testFloat(self):
  self._testBasic(np.float32)
  @@ -78,7 +77,7 @@ class ArgMaxTest(test.TestCase):
  x = np.asarray(100 * np.random.randn(200), dtype=np.float32)
  expected_values = x.argmax()
  with self.test_session(use_gpu=True):
  - ans = math_ops.argmax(x, dimension=0, output_type=dtypes.int32)
  + ans = math_ops.argmax(x, axis=0, output_type=dtypes.int32)
  tf_ans = ans.eval()
  self.assertEqual(np.int32, tf_ans.dtype)
  # The values are equal when comparing int32 to int64 because
  @@ -86,7 +85,7 @@ class ArgMaxTest(test.TestCase):
  self.assertAllEqual(tf_ans, expected_values)
  expected_values = x.argmin()
  with self.test_session(use_gpu=True):
  - ans = math_ops.argmin(x, dimension=0, output_type=dtypes.int32)
  + ans = math_ops.argmin(x, axis=0, output_type=dtypes.int32)
  tf_ans = ans.eval()
  self.assertEqual(np.int32, tf_ans.dtype)
  self.assertAllEqual(tf_ans, expected_values)
  