How to use the pygeoprocessing.get_cell_size_from_uri function in pygeoprocessing

Returns:
            ``numpy.ndarray``, in 2 dimensions.  This will contain the ``m``
            parameter from the fitted line.
        """
        stacked_array = numpy.dstack(blocks)
        new_shape = (stacked_array.shape[0]*stacked_array.shape[1],
                     len(timesteps))
        reshaped = numpy.swapaxes(numpy.reshape(stacked_array, new_shape), 0, 1)
        regression = numpy.polyfit(timesteps,
                                   reshaped, deg=deg, w=weights)[0]
        out_block = regression.reshape(blocks[0].shape)
        # Mask out any pixel stacks where there's a nodata value in the stack.
        # Out block is multiplied by 365.25 to convert m/day to m/year trend.
        return numpy.where(numpy.min(stacked_array, axis=2) == 0, 0, out_block*365.25)

    raster_cell_sizes = [pygeoprocessing.get_cell_size_from_uri(r)
                         for r in rasters]
    min_cell_size = min(raster_cell_sizes)
    if not len(set(raster_cell_sizes)) == 1:
        warnings.warn(('Cell sizes of input rasters do not all match. '
                       'Using min pixelsize of %s. Mismatched values: %s') % (
                      min_cell_size, set(raster_cell_sizes)))

    pygeoprocessing.vectorize_datasets(
        dataset_uri_list=rasters,
        dataset_pixel_op=_regression,
        dataset_out_uri=out_filename,
        datatype_out=gdal.GDT_Float32,
        nodata_out=0,
        pixel_size_out=min_cell_size,
        bounding_box_mode='intersection',
        vectorize_op=False,