code: def get_seed(seed):
      """Returns the local seeds an operation should use given an op-specific seed.
  
      See `random_seed.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
  
imports: 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
label_desc: Use image node_id 1 for calling a global function with example usage: get_seed(seed) and returns: seed,  seed2