<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def as_array( self, include_missing=False, weighted=True, include_transforms_for_dims=None, prune=False, ):
 """Return `ndarray` representing cube values. Returns the tabular representation of the crunch cube. The returned array has the same number of dimensions as the cube. E.g. for a cross-tab representation of a categorical and numerical variable, the resulting cube will have two dimensions. *include_missing* (bool):
 Include rows/cols for missing values. Example 1 (Categorical x Categorical):
: np.array([ [5, 2], [5, 3], ]) Example 2 (Categorical x Categorical, include missing values):
: np.array([ [5, 3, 2, 0], [5, 2, 3, 0], [0, 0, 0, 0], ]) """

        array = self._as_array(
            include_missing=include_missing,
            weighted=weighted,
            include_transforms_for_dims=include_transforms_for_dims,
        )

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

        return self._drop_mr_cat_dims(array)