code: def forward(
      ctx: Any,
      input: Tensor,
      bias: Tensor,
      output_size_per_partition: int,
      tesseract_dim: int,
      row_rank: int,
      col_rank: int,
      dep_rank: int,
      col_parallel_mode: ParallelMode,
      skip_bias_add: bool,
      data_parallel_rank: int,
      pipeline_parallel_rank: int,
      pipeline_parallel_size: int,
      tensor_parallel_size: int,
  ) -> Tensor:
      if row_rank == 0:
          bias_temp = bias.clone()
      else:
          bias_temp = torch.zeros(
              output_size_per_partition,
              dtype=bias.dtype,
              device=get_current_device(),
          )
      src_rank = (
          col_rank
          + dep_rank * tesseract_dim**2
          + data_parallel_rank
          * pipeline_parallel_size
          * tensor_parallel_size
          + pipeline_parallel_rank * tensor_parallel_size
      )
      dist.broadcast(
          bias_temp,
          src=src_rank,
          group=get_parallel_group(col_parallel_mode),
      )
  
      ctx.row_rank = row_rank
      ctx.col_rank = col_rank
      ctx.dep_rank = dep_rank
      ctx.tesseract_dim = tesseract_dim
      ctx.col_parallel_mode = col_parallel_mode
      ctx.bias = skip_bias_add
      ctx.data_parallel_rank = data_parallel_rank
      ctx.pipeline_parallel_rank = pipeline_parallel_rank
      ctx.pipeline_parallel_size = pipeline_parallel_size
      ctx.tensor_parallel_size = tensor_parallel_size
  
      if skip_bias_add:
          return bias_temp
      else:
          output = input + bias_temp
          return output
  
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 1 for calling the _Add_Bias_2p5D obj's underlying member method code with example usage: obj.forward(ctx, input, bias, output_size_per_partition, tesseract_dim, row_rank, col_rank, dep_rank, col_parallel_mode, skip_bias_add, data_parallel_rank, pipeline_parallel_rank, pipeline_parallel_size, tensor_parallel_size) and returns: bias_temp, output