code: def scale_means(self, hs_dims=None, prune=False):
          
          scale_means = self._cube.scale_means(hs_dims, prune)
  
          if self.ca_as_0th:
              # If slice is used as 0th CA, then we need to observe the 1st dimension,
              # because the 0th dimension is CA items, which is only used for slicing
              # (and thus doesn't have numerical values, and also doesn't constitute any
              # dimension of the actual crosstabs that will be created in this case).
              scale_means = scale_means[0][-1]
              if scale_means is None:
                  return [None]
              return [scale_means[self._index]]
  
          return scale_means[self._index]
docs: Return list of column and row scaled means for this slice.
  
          If a row/col doesn't have numerical values, return None for the
          corresponding dimension. If a slice only has 1D, return only the column
          scaled mean (as numpy array). If both row and col scaled means are
          present, return them as two numpy arrays inside of a list.