code: def dimension_values(self, dim, expanded=True, flat=True):
          
          dim_idx = self.get_dimension_index(dim)
          if not expanded and dim_idx == 0:
              return np.array(range(self.data.shape[1]))
          elif not expanded and dim_idx == 1:
              return np.array(range(self.data.shape[0]))
          elif dim_idx in [0, 1]:
              values = np.mgrid[0:self.data.shape[1], 0:self.data.shape[0]][dim_idx]
              return values.flatten() if flat else values
          elif dim_idx == 2:
              arr = self.data.T
              return arr.flatten() if flat else arr
          else:
              return super(Raster, self).dimension_values(dim)
docs: The set of samples available along a particular dimension.