<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def margin( self, axis=None, weighted=True, include_missing=False, include_transforms_for_dims=None, prune=False, include_mr_cat=False, ):
 """Get margin for the selected axis. the selected axis. For MR variables, this is the sum of the selected and non-selected slices. Args axis (int):
 Axis across the margin is calculated. If no axis is provided the margin is calculated across all axis. For Categoricals, Num, Datetime, and Text, this translates to sumation of all elements. Returns Calculated margin for the selected axis Example 1: np.array([ [5, 2], [5, 3], ]) np.array([10, 5]) Example 2: np.array([ [[1, 1], [0, 0], [0, 0], [0, 0]], [[2, 1], [1, 1], [0, 0], [0, 0]], [[0, 0], [2, 3], [0, 0], [0, 0]], [[0, 0], [0, 0], [3, 2], [0, 0]], [[0, 0], [0, 0], [1, 1], [0, 1]] ]) np.array([ [3, 2], [3, 4], [4, 3], [0, 1], ]) """


        table = self._counts(weighted).raw_cube_array
        new_axis = self._adjust_axis(axis)
        index = tuple(
            None if i in new_axis else slice(None) for i, _ in enumerate(table.shape)
        )

        # Calculate denominator. Only include those H&S dimensions, across
        # which we DON'T sum. These H&S are needed because of the shape, when
        # dividing. Those across dims which are summed across MUST NOT be
        # included, because they would change the result.
        hs_dims = self._hs_dims_for_den(include_transforms_for_dims, axis)
        den = self._apply_subtotals(
            self._apply_missings(table, include_missing=include_missing), hs_dims
        )

        # Apply correct mask (based on the as_array shape)
        arr = self._as_array(
            include_transforms_for_dims=hs_dims, include_missing=include_missing
        )

        # ---prune array if pruning was requested---
        if prune:
            arr = self._prune_body(arr, transforms=hs_dims)

        arr = self._drop_mr_cat_dims(arr, fix_valids=include_missing)

        if isinstance(arr, np.ma.core.MaskedArray):
            # Inflate the reduced version of the array, to match the
            # non-reduced version, for the purposes of creating the correct
            # mask. Create additional dimension (with no elements) where MR_CAT
            # dimensions should be. Don't inflate 0th dimension if it has only
            # a single element, because it's not being reduced
            # in self._drop_mr_cat_dims
            inflate_ind = tuple(
                (
                    None
                    if (
                        d.dimension_type == DT.MR_CAT
                        or i != 0
                        and (n <= 1 or len(d.valid_elements) <= 1)
                    )
                    else slice(None)
                )
                for i, (d, n) in enumerate(zip(self._all_dimensions, table.shape))
            )
            mask = np.logical_or(np.zeros(den.shape, dtype=bool), arr.mask[inflate_ind])
            den = np.ma.masked_array(den, mask)

        if (
            self.ndim != 1
            or axis is None
            or axis == 0
            and len(self._all_dimensions) == 1
        ):
            # Special case for 1D cube wigh MR, for "Table" direction
            den = np.sum(den, axis=new_axis)[index]

        den = self._drop_mr_cat_dims(
            den, fix_valids=(include_missing or include_mr_cat)
        )
        if den.shape[0] == 1 and len(den.shape) > 1 and self.ndim < 3:
            den = den.reshape(den.shape[1:])
        return den