code: def as_array(
          self,
          include_missing=False,
          weighted=True,
          include_transforms_for_dims=None,
          prune=False,
      ):
          
          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)
docs: 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)::
  
              >>> cube = CrunchCube(response)
              >>> cube.as_array()
              np.array([
                  [5, 2],
                  [5, 3],
              ])
  
          Example 2 (Categorical x Categorical, include missing values)::
  
              >>> cube = CrunchCube(response)
              >>> cube.as_array(include_missing=True)
              np.array([
                  [5, 3, 2, 0],
                  [5, 2, 3, 0],
                  [0, 0, 0, 0],
              ])