code: def margin(
          self,
          axis=None,
          weighted=True,
          include_missing=False,
          include_transforms_for_dims=None,
          prune=False,
          include_mr_cat=False,
      ):
          
  
          axis = self._calculate_correct_axis_for_cube(axis)
          hs_dims = self._hs_dims_for_cube(include_transforms_for_dims)
  
          margin = self._cube.margin(
              axis=axis,
              weighted=weighted,
              include_missing=include_missing,
              include_transforms_for_dims=hs_dims,
              prune=prune,
              include_mr_cat=include_mr_cat,
          )
  
          return self._extract_slice_result_from_cube(margin)
docs: Return ndarray representing slice margin across selected axis.
  
          A margin (or basis) can be calculated for a contingency table, provided
          that the dimensions of the desired directions are marginable. The
          dimensions are marginable if they represent mutualy exclusive data,
          such as true categorical data. For array types the items dimensions are
          not marginable. Requesting a margin across these dimensions
          (e.g. slice.margin(axis=0) for a categorical array cube slice) will
          produce an error. For multiple response slices, the implicit convention
          is that the provided direction scales to the selections dimension of the
          slice. These cases produce meaningful data, but of a slightly different
          shape (e.g. slice.margin(0) for a MR x CAT slice will produce 2D ndarray
          (variable dimensions are never collapsed!)).
  
          :param axis: Axis across which to sum. Can be 0 (columns margin),
              1 (rows margin) and None (table margin). If requested across
              variables dimension (e.g. requesting 0 margin for CA array) it will
              produce an error.
          :param weighted: Weighted or unweighted counts.
          :param include_missing: Include missing categories or not.
          :param include_transforms_for_dims: Indices of dimensions for which to
                 include transformations
          :param prune: Perform pruning based on unweighted counts.
          :returns: (weighed or unweighted counts) summed across provided axis.
              For multiple response types, items dimensions are not collapsed.