code: def split_batch_2p5d(input_: Tensor, dim: int = 0) -> Tensor:
      """Splits 2P5D tensor in specified dimension across cols.
  
      Args:
          input_ (:class:`torch.tensor`): Input tensor.
          dim (int): Specified dimension in which to split.
  
      Returns:
          :class:`torch.tensor`: The tensor has been split.
      """
      dim_size = input_.size(dim)
      world_size = gpc.get_world_size(ParallelMode.PARALLEL_2P5D_COL)
  
      if world_size <= 1:
          return input_
  
      assert (
          dim_size % world_size == 0
      ), f"The batch size ({dim_size}) is not a multiple of 2.5D size * depth ({world_size})."
  
      return torch.chunk(
          input_,
          gpc.get_world_size(ParallelMode.PARALLEL_2P5D_COL),
          dim=dim,
      )[gpc.get_local_rank(ParallelMode.PARALLEL_2P5D_COL)].contiguous()
  
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 8 for calling a global function with example usage: split_batch_2p5d(input_, dim) and returns: torch, input_