tensorflow_probability/python/distributions/linear_gaussian_ssm.py
def _check_equal_shape(name,
                       static_shape,
                       dynamic_shape,
                       static_target_shape,
                       dynamic_target_shape=None):
  """Check that source and target shape match, statically if possible."""

  static_target_shape = tf.TensorShape(static_target_shape)
  if tensorshape_util.is_fully_defined(
      static_shape) and tensorshape_util.is_fully_defined(static_target_shape):
    if static_shape != static_target_shape:
      raise ValueError("{}: required shape {} but found {}".
                       format(name, static_target_shape, static_shape))
    return None
  else:
    if dynamic_target_shape is None:
      if tensorshape_util.is_fully_defined(static_target_shape):
        dynamic_target_shape = tensorshape_util.as_list(static_target_shape)
      else:
        raise ValueError("{}: cannot infer target shape: no dynamic shape "
                         "specified and static shape {} is not fully defined".
                         format(name, static_target_shape))
    return assert_util.assert_equal(
        dynamic_shape,
        dynamic_target_shape,
        message=("{}: required shape {}".format(name, static_target_shape)))
def _check_equal_shape(name,
                       static_shape,
                       dynamic_shape,
                       static_target_shape,
                       dynamic_target_shape=None):
  static_target_shape = tf.TensorShape(static_target_shape)
  if tensorshape_util.is_fully_defined(
      static_shape) and tensorshape_util.is_fully_defined(static_target_shape):
    if static_shape != static_target_shape:
      raise ValueError("{}: required shape {} but found {}".
                       format(name, static_target_shape, static_shape))
    return None
  else:
    if dynamic_target_shape is None:
      if tensorshape_util.is_fully_defined(static_target_shape):
        dynamic_target_shape = tensorshape_util.as_list(static_target_shape)
      else:
        raise ValueError("{}: cannot infer target shape: no dynamic shape "
                         "specified and static shape {} is not fully defined".
                         format(name, static_target_shape))
    return assert_util.assert_equal(
        dynamic_shape,
        dynamic_target_shape,
        message=("{}: required shape {}".format(name, static_target_shape)))
def _check_equal_shape ( name , static_shape , dynamic_shape , static_target_shape , dynamic_target_shape = None ) : static_target_shape = tf . TensorShape ( static_target_shape ) if tensorshape_util . is_fully_defined ( static_shape ) and tensorshape_util . is_fully_defined ( static_target_shape ) : if static_shape != static_target_shape : raise ValueError ( "{}: required shape {} but found {}" . format ( name , static_target_shape , static_shape ) ) return None else : if dynamic_target_shape is None : if tensorshape_util . is_fully_defined ( static_target_shape ) : dynamic_target_shape = tensorshape_util . as_list ( static_target_shape ) else : raise ValueError ( "{}: cannot infer target shape: no dynamic shape " "specified and static shape {} is not fully defined" . format ( name , static_target_shape ) ) return assert_util . assert_equal ( dynamic_shape , dynamic_target_shape , message = ( "{}: required shape {}" . format ( name , static_target_shape ) ) )
Check that source and target shape match, statically if possible.
Check that source and target shape match statically if possible .