code: def _calculate_meta_reordering_scatter_offsets(
      m, meta_ncols, meta_dtype, device
  ):
      dst_rows = torch.arange(0, m, device=device)[:, None].repeat(
          1, meta_ncols
      )
      dst_cols = torch.arange(0, meta_ncols, device=device).repeat(m, 1)
  
      # Reorder the rows, then swizzle the 2x2 blocks.
      group = 32 if meta_dtype.itemsize == 2 else 16
      interweave = 4 if meta_dtype.itemsize == 2 else 2
      dst_rows = (
          dst_rows // group * group
          + (dst_rows % 8) * interweave
          + (dst_rows % group) // 8
      )
  
      topright = ((dst_rows % 2 == 0) & (dst_cols % 2 == 1)).to(
          torch.int8
      )
      bottomleft = ((dst_rows % 2 == 1) & (dst_cols % 2 == 0)).to(
          torch.int8
      )
      dst_rows += topright - bottomleft
      dst_cols -= topright - bottomleft
  
      # Assumed that meta tensor is to be stored in CUTLASS
      # InterleavedColumnMajor layout, and reverse engineered
      # corresponding code to store values into this tensor.
      interleave = 2
      cols_maj = dst_cols // interleave
      cols_min = dst_cols % interleave
      return (
          cols_maj * m * interleave + dst_rows * interleave + cols_min
      ).view(-1)
  
label_desc: Use image node_id 1 for calling a global function with example usage: _calculate_meta_reordering_scatter_offsets(m, meta_ncols, meta_dtype, device) and returns: unknown