text: # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Utilities for computing default gradients."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import resource_variable_ops
  
  
  def get_zeros_dtype(t):
    """Return the dtype for the default gradient for a Tensor."""
    if t.dtype == dtypes.resource:
      handle_data = resource_variable_ops.get_eager_safe_handle_data(t)
      if (handle_data is None or not handle_data.is_set or
          len(handle_data.shape_and_type) != 1):
        raise ValueError("Internal error: Tried to take gradients (or similar) "
                         "of a variable without handle data:\n%s" % str(t))
      return handle_data.shape_and_type[0].dtype
    return t.dtype
  
  
  def shape_and_dtype(t):
    """Return the shape and dtype for the default gradient for a Tensor."""
    if t.dtype == dtypes.resource:
      handle_data = resource_variable_ops.get_eager_safe_handle_data(t)
      if (handle_data is None or not handle_data.is_set or
          len(handle_data.shape_and_type) != 1):
        raise ValueError("Internal error: Tried to take gradients (or similar) "
                         "of a variable without handle data:\n%s" % str(t))
      shape_and_type = handle_data.shape_and_type[0]
      return (tensor_shape.TensorShape(shape_and_type.shape),
              dtypes.as_dtype(shape_and_type.dtype))
    return t.shape, t.dtype
  
  
  def zeros_like(t):
    """Like array_ops.zeros_like, but respects resource handles."""
    if t.dtype == dtypes.resource:
      return array_ops.zeros(*shape_and_dtype(t))
    else:
      return array_ops.zeros_like(t)
  
  
  def ones_like(t):
    """Like array_ops.ones_like, but respects resource handles."""
    if t.dtype == dtypes.resource:
      return array_ops.ones(*shape_and_dtype(t))
    else:
      return array_ops.ones_like(t)
  
  
  def supports_default_grad(t):
    """Whether tensor `t` supports creating a default gradient.
  
    This function assumes that `t` is of a trainable type.
  
    Args:
      t: Tensor
  
    Returns:
      Bool
    """
    if t.dtype == dtypes.resource:
      handle_data = resource_variable_ops.get_eager_safe_handle_data(t)
      if (handle_data is None or not handle_data.is_set or
          len(handle_data.shape_and_type) != 1):
        return False
    return True
  