tensorflow_probability/python/distributions/vector_diffeomixture.py
def interpolate_scale(grid, scale):
  """Helper which interpolates between two scales."""
  if len(scale) != 2:
    raise NotImplementedError("Currently only bimixtures are supported; "
                              "len(scale)={} is not 2.".format(len(scale)))
  deg = tf.compat.dimension_value(
      tensorshape_util.with_rank_at_least(grid.shape, 1)[-1])
  if deg is None:
    raise ValueError("Num quadrature grid points must be known prior "
                     "to graph execution.")
  with tf.name_scope("interpolate_scale"):
    return [linop_add_lib.add_operators([
        linop_scale(grid[..., k, q], s)
        for k, s in enumerate(scale)
    ])[0] for q in range(deg)]
def interpolate_scale(grid, scale):
  """Helper which interpolates between two scales."""
  if len(scale) != 2:
    raise NotImplementedError("Currently only bimixtures are supported; "
                              "len(scale)={} is not 2.".format(len(scale)))
  deg = tf.compat.dimension_value(
      tensorshape_util.with_rank_at_least(grid.shape, 1)[-1])
  if deg is None:
    raise ValueError("Num quadrature grid points must be known prior "
                     "to graph execution.")
  with tf.name_scope("interpolate_scale"):
    return [linop_add_lib.add_operators([
        linop_scale(grid[..., k, q], s)
        for k, s in enumerate(scale)
    ])[0] for q in range(deg)]
Helper which interpolates between two scales.