old_contents: # Copyright 2018 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 generating Tensor-valued random seeds."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import random_seed
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  
  
  def get_seed(seed):
    """Returns the local seeds an operation should use given an op-specific seed.
  
    See `tf.compat.v1.get_seed` for more details. This wrapper adds support for
    the case
    where `seed` may be a tensor.
  
    Args:
      seed: An integer or a `tf.int64` scalar tensor.
  
    Returns:
      A tuple of two `tf.int64` scalar tensors that should be used for the local
      seed of the calling dataset.
    """
    seed, seed2 = random_seed.get_seed(seed)
    if seed is None:
      seed = constant_op.constant(0, dtype=dtypes.int64, name="seed")
    else:
      seed = ops.convert_to_tensor(seed, dtype=dtypes.int64, name="seed")
    if seed2 is None:
      seed2 = constant_op.constant(0, dtype=dtypes.int64, name="seed2")
    else:
      with ops.name_scope("seed2") as scope:
        seed2 = ops.convert_to_tensor(seed2, dtype=dtypes.int64)
        seed2 = array_ops.where(
            math_ops.logical_and(
                math_ops.equal(seed, 0), math_ops.equal(seed2, 0)),
            constant_op.constant(2**31 - 1, dtype=dtypes.int64),
            seed2,
            name=scope)
    return seed, seed2
  
new_contents: # Copyright 2018 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 generating Tensor-valued random seeds."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import random_seed
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  
  
  def get_seed(seed):
    """Returns the local seeds an operation should use given an op-specific seed.
  
    See `tf.compat.v1.get_seed` for more details. This wrapper adds support for
    the case
    where `seed` may be a tensor.
  
    Args:
      seed: An integer or a `tf.int64` scalar tensor.
  
    Returns:
      A tuple of two `tf.int64` scalar tensors that should be used for the local
      seed of the calling dataset.
    """
    seed, seed2 = random_seed.get_seed(seed)
    if seed is None:
      seed = constant_op.constant(0, dtype=dtypes.int64, name="seed")
    else:
      seed = ops.convert_to_tensor(seed, dtype=dtypes.int64, name="seed")
    if seed2 is None:
      seed2 = constant_op.constant(0, dtype=dtypes.int64, name="seed2")
    else:
      with ops.name_scope("seed2") as scope:
        seed2 = ops.convert_to_tensor(seed2, dtype=dtypes.int64)
        seed2 = array_ops.where_v2(
            math_ops.logical_and(
                math_ops.equal(seed, 0), math_ops.equal(seed2, 0)),
            constant_op.constant(2**31 - 1, dtype=dtypes.int64),
            seed2,
            name=scope)
    return seed, seed2
  
subject: Use where_v2 to silence deprecation warning