docs: def pad_length(x, d):
      """Return a vector appropriate to a dimensional space, using an input vector
      as a prompt depending on its type:
  
          - If the input is a vector, return that vector.
          - If the input is a scalar, return a vector filled with that value.
  
      Useful when a function expects an array specifying values along each axis,
      but wants to also accept a scalar value in case the length is the same in
      all directions.
  
      Parameters
      ----------
      x: float or array-like
          The input parameter that may need padding.
      d: int
          The dimensional space to make `x` appropriate for.
  
      Returns
      -------
      x_pad: array-like, shape (d,)
          The padded parameter.
      """
      try:
          x[0]
      except TypeError:
          x = d * [x]
      return np.array(x)