tensorflow_probability/python/internal/nest_util.py
def broadcast_structure(to_structure, from_structure):
  """Broadcasts `from_structure` to `to_structure`.

  This is useful for downstream usage of `zip` or `tf.nest.map_structure`.

  If `from_structure` is a singleton, it is tiled to match the structure of
  `to_structure`. Note that the elements in `from_structure` are not copied if
  this tiling occurs.

  Args:
    to_structure: A structure.
    from_structure: A structure.

  Returns:
    new_from_structure: Same structure as `to_structure`.

  #### Example:

  ```python
  a_structure = ['a', 'b', 'c']
  b_structure = broadcast_structure(a_structure, 'd')
  # -> ['d', 'd', 'd']
  c_structure = tf.nest.map_structure(
      lambda a, b: a + b, a_structure, b_structure)
  # -> ['ad', 'bd', 'cd']
  ```
  """
  from_parts = tf.nest.flatten(from_structure)
  if len(from_parts) == 1:
    from_structure = tf.nest.map_structure(lambda _: from_parts[0],
                                           to_structure)
  return from_structure
def broadcast_structure(to_structure, from_structure):
  from_parts = tf.nest.flatten(from_structure)
  if len(from_parts) == 1:
    from_structure = tf.nest.map_structure(lambda _: from_parts[0],
                                           to_structure)
  return from_structure
def broadcast_structure ( to_structure , from_structure ) : from_parts = tf . nest . flatten ( from_structure ) if len ( from_parts ) == 1 : from_structure = tf . nest . map_structure ( lambda _ : from_parts [ 0 ] , to_structure ) return from_structure
Broadcasts `from_structure` to `to_structure`.

  This is useful for downstream usage of `zip` or `tf.nest.map_structure`.

  If `from_structure` is a singleton, it is tiled to match the structure of
  `to_structure`. Note that the elements in `from_structure` are not copied if
  this tiling occurs.

  Args:
    to_structure: A structure.
    from_structure: A structure.

  Returns:
    new_from_structure: Same structure as `to_structure`.

  #### Example:

  ```python
  a_structure = ['a', 'b', 'c']
  b_structure = broadcast_structure(a_structure, 'd')
  # -> ['d', 'd', 'd']
  c_structure = tf.nest.map_structure(
      lambda a, b: a + b, a_structure, b_structure)
  # -> ['ad', 'bd', 'cd']
  ```
Broadcasts from_structure to to_structure .