code: def backward(ctx: Any, output_grad: Tensor) -> Tuple[Tensor, ...]:
      row_rank = ctx.row_rank
      col_rank = ctx.col_rank
      dep_rank = ctx.dep_rank
      tesseract_dim = ctx.tesseract_dim
      col_parallel_mode = ctx.col_parallel_mode
      data_parallel_rank = ctx.data_parallel_rank
      pipeline_parallel_rank = ctx.pipeline_parallel_rank
      pipeline_parallel_size = ctx.pipeline_parallel_size
      tensor_parallel_size = ctx.tensor_parallel_size
  
      if ctx.bias:
          dst_rank = (
              col_rank
              + dep_rank * (tesseract_dim**2)
              + data_parallel_rank
              * pipeline_parallel_size
              * tensor_parallel_size
              + pipeline_parallel_rank * tensor_parallel_size
          )
          dist.reduce(
              output_grad,
              dst=dst_rank,
              group=get_parallel_group(col_parallel_mode),
          )
          if row_rank == 0:
              return (
                  None,
                  output_grad,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
              )
          else:
              grad_tmp = torch.zeros_like(output_grad)
              return (
                  None,
                  grad_tmp,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
              )
      else:
          reduce_dim = tuple(range(output_grad.ndim - 1))
          reduce = torch.sum(output_grad, dim=reduce_dim)
          dst_rank = (
              col_rank
              + dep_rank * (tesseract_dim**2)
              + data_parallel_rank
              * pipeline_parallel_size
              * tensor_parallel_size
              + pipeline_parallel_rank * tensor_parallel_size
          )
          dist.reduce(
              reduce,
              dst=dst_rank,
              group=get_parallel_group(col_parallel_mode),
          )
          if row_rank == 0:
              return (
                  output_grad,
                  reduce,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
              )
          else:
              reduce_tmp = torch.zeros_like(reduce)
              return (
                  output_grad,
                  reduce_tmp,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
                  None,
              )
  
imports: from typing import Any, Tuple
  import torch
  import torch.distributed
  from torch import Tensor
  from torch.cuda.amp import custom_bwd, custom_fwd
  from colossalai.legacy.communication.collective import all_gather, all_reduce, reduce_scatter
  from colossalai.legacy.context.parallel_mode import ParallelMode
  from colossalai.legacy.core import global_context
  from colossalai.utils import get_current_device
label_desc: Use image node_id 2 for calling the _Add_Bias_2p5D obj's underlying member method code with example usage: obj.backward(ctx, output_grad) and returns: None,  output_grad,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None, None,  grad_tmp,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None, output_grad,  reduce,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None, output_grad,  reduce_tmp,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None,  None