tensorflow_probability/python/distributions/joint_distribution.py
def maybe_check_wont_broadcast(flat_xs, validate_args):
  """Verifies that `parts` don't broadcast."""
  flat_xs = tuple(flat_xs)  # So we can receive generators.
  if not validate_args:
    # Note: we don't try static validation because it is theoretically
    # possible that a user wants to take advantage of broadcasting.
    # Only when `validate_args` is `True` do we enforce the validation.
    return flat_xs
  msg = 'Broadcasting probably indicates an error in model specification.'
  s = tuple(x.shape for x in flat_xs)
  if all(tensorshape_util.is_fully_defined(s_) for s_ in s):
    if not all(a == b for a, b in zip(s[1:], s[:-1])):
      raise ValueError(msg)
    return flat_xs
  assertions = [assert_util.assert_equal(a, b, message=msg)
                for a, b in zip(s[1:], s[:-1])]
  with tf.control_dependencies(assertions):
    return tuple(tf.identity(x) for x in flat_xs)
def maybe_check_wont_broadcast(flat_xs, validate_args):
  flat_xs = tuple(flat_xs)  # So we can receive generators.
  if not validate_args:
    # Note: we don't try static validation because it is theoretically
    # possible that a user wants to take advantage of broadcasting.
    # Only when `validate_args` is `True` do we enforce the validation.
    return flat_xs
  msg = 'Broadcasting probably indicates an error in model specification.'
  s = tuple(x.shape for x in flat_xs)
  if all(tensorshape_util.is_fully_defined(s_) for s_ in s):
    if not all(a == b for a, b in zip(s[1:], s[:-1])):
      raise ValueError(msg)
    return flat_xs
  assertions = [assert_util.assert_equal(a, b, message=msg)
                for a, b in zip(s[1:], s[:-1])]
  with tf.control_dependencies(assertions):
    return tuple(tf.identity(x) for x in flat_xs)
def maybe_check_wont_broadcast ( flat_xs , validate_args ) : flat_xs = tuple ( flat_xs ) # So we can receive generators. if not validate_args : # Note: we don't try static validation because it is theoretically # possible that a user wants to take advantage of broadcasting. # Only when `validate_args` is `True` do we enforce the validation. return flat_xs msg = 'Broadcasting probably indicates an error in model specification.' s = tuple ( x . shape for x in flat_xs ) if all ( tensorshape_util . is_fully_defined ( s_ ) for s_ in s ) : if not all ( a == b for a , b in zip ( s [ 1 : ] , s [ : - 1 ] ) ) : raise ValueError ( msg ) return flat_xs assertions = [ assert_util . assert_equal ( a , b , message = msg ) for a , b in zip ( s [ 1 : ] , s [ : - 1 ] ) ] with tf . control_dependencies ( assertions ) : return tuple ( tf . identity ( x ) for x in flat_xs )
Verifies that `parts` don't broadcast.
Verifies that parts don t broadcast .