tensorflow_probability/python/distributions/deterministic.py
def _get_tol(tol, dtype, validate_args):
  """Gets a Tensor of type `dtype`, 0 if `tol` is None, validation optional."""
  if tol is None:
    return tf.convert_to_tensor(value=0, dtype=dtype)

  tol = tf.convert_to_tensor(value=tol, dtype=dtype)
  if validate_args:
    tol = distribution_util.with_dependencies([
        assert_util.assert_non_negative(
            tol, message="Argument 'tol' must be non-negative")
    ], tol)
  return tol
def _get_tol(tol, dtype, validate_args):
  """Gets a Tensor of type `dtype`, 0 if `tol` is None, validation optional."""
  if tol is None:
    return tf.convert_to_tensor(value=0, dtype=dtype)

  tol = tf.convert_to_tensor(value=tol, dtype=dtype)
  if validate_args:
    tol = distribution_util.with_dependencies([
        assert_util.assert_non_negative(
            tol, message="Argument 'tol' must be non-negative")
    ], tol)
  return tol
def _get_tol ( tol , dtype , validate_args ) : if tol is None : return tf . convert_to_tensor ( value = 0 , dtype = dtype ) tol = tf . convert_to_tensor ( value = tol , dtype = dtype ) if validate_args : tol = distribution_util . with_dependencies ( [ assert_util . assert_non_negative ( tol , message = "Argument 'tol' must be non-negative" ) ] , tol ) return tol
Gets a Tensor of type `dtype`, 0 if `tol` is None, validation optional.
Gets a Tensor of type dtype 0 if tol is None validation optional .