old_contents: # Copyright 2020 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.
  # ==============================================================================
  """Core TensorFlow types."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from typing import Union
  import numpy as np
  
  from tensorflow.python.util.tf_export import tf_export
  
  # TODO(mdan): Consider adding ABC once the dependence on isinstance is reduced.
  # TODO(mdan): Add type annotations.
  
  
  class Tensor(object):
    """The base class of all dense Tensor objects.
  
    A dense tensor has a static data type (dtype), and may have a static rank and
    shape. Tensor objects are immutable. Mutable objects may be backed by a Tensor
    which holds the unique handle that identifies the mutable object.
    """
  
    @property
    def dtype(self):
      pass
  
    @property
    def shape(self):
      pass
  
  
  class Symbol(Tensor):
    """Symbolic "graph" Tensor.
  
    These objects represent the output of an op definition and do not carry a
    value.
    """
    pass
  
  
  class Value(Tensor):
    """Tensor that can be associated with a value (aka "eager tensor").
  
    These objects represent the (usually future) output of executing an op
    immediately.
    """
  
    def numpy(self):
      pass
  
  
  TensorLike = Union[Tensor, int, float, bool, str, complex, tuple, list, np.ndarray]
  tf_export("experimental.TensorLike").export_constant(__name__, "TensorLike")
  
new_contents: # Copyright 2020 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.
  # ==============================================================================
  """Core TensorFlow types."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from typing import Union
  import numpy as np
  
  from tensorflow.python.util.tf_export import tf_export
  
  # TODO(mdan): Consider adding ABC once the dependence on isinstance is reduced.
  # TODO(mdan): Add type annotations.
  
  
  class Tensor(object):
    """The base class of all dense Tensor objects.
  
    A dense tensor has a static data type (dtype), and may have a static rank and
    shape. Tensor objects are immutable. Mutable objects may be backed by a Tensor
    which holds the unique handle that identifies the mutable object.
    """
  
    @property
    def dtype(self):
      pass
  
    @property
    def shape(self):
      pass
  
  
  class Symbol(Tensor):
    """Symbolic "graph" Tensor.
  
    These objects represent the output of an op definition and do not carry a
    value.
    """
    pass
  
  
  class Value(Tensor):
    """Tensor that can be associated with a value (aka "eager tensor").
  
    These objects represent the (usually future) output of executing an op
    immediately.
    """
  
    def numpy(self):
      pass
  
  
  TensorLike = Union[Tensor, int, float, bool, str, complex, tuple, list,
                     np.ndarray]
  tf_export("experimental.TensorLike").export_constant(__name__, "TensorLike")
  
subject: fix pylint errors