tensorflow_probability/python/mcmc/slice_sampler_kernel.py
def _maybe_call_fn(fn,
                   fn_arg_list,
                   fn_result=None,
                   description='target_log_prob'):
  """Helper which computes `fn_result` if needed."""
  fn_arg_list = (list(fn_arg_list) if mcmc_util.is_list_like(fn_arg_list)
                 else [fn_arg_list])
  if fn_result is None:
    fn_result = fn(*fn_arg_list)
  if not fn_result.dtype.is_floating:
    raise TypeError('`{}` must be a `Tensor` with `float` `dtype`.'.format(
        description))
  return fn_result
def _maybe_call_fn(fn,
                   fn_arg_list,
                   fn_result=None,
                   description='target_log_prob'):
  fn_arg_list = (list(fn_arg_list) if mcmc_util.is_list_like(fn_arg_list)
                 else [fn_arg_list])
  if fn_result is None:
    fn_result = fn(*fn_arg_list)
  if not fn_result.dtype.is_floating:
    raise TypeError('`{}` must be a `Tensor` with `float` `dtype`.'.format(
        description))
  return fn_result
def _maybe_call_fn ( fn , fn_arg_list , fn_result = None , description = 'target_log_prob' ) : fn_arg_list = ( list ( fn_arg_list ) if mcmc_util . is_list_like ( fn_arg_list ) else [ fn_arg_list ] ) if fn_result is None : fn_result = fn ( * fn_arg_list ) if not fn_result . dtype . is_floating : raise TypeError ( '`{}` must be a `Tensor` with `float` `dtype`.' . format ( description ) ) return fn_result
Helper which computes `fn_result` if needed.
Helper which computes fn_result if needed .