code: def classifier_2p5d(
      A: Tensor,
      B: Tensor,
      bias,
      tesseract_dim: int,
      out_shape: Tuple[int, ...],
      row_rank: int,
      col_rank: int,
      row_parallel_mode: ParallelMode,
      col_parallel_mode: ParallelMode,
      data_parallel_rank: int,
      pipeline_parallel_rank: int,
      pipeline_parallel_size: int,
      tensor_parallel_size: int,
  ) -> Tensor:
      r"""Classifier.
  
      Args:
          A (:class:`torch.tensor`): matrix :math:`A`.
          B (:class:`torch.tensor`): matrix :math:`B`.
          bias (:class:`torch.tensor`): matrix of bias.
          tesseract_dim (int): dimension of TESSERACT fo 2.5D parallelism.
          out_shape (:class:`torch.size`): shape of output tensor.
          row_rank (int): the rank of row.
          col_rank (int): the rank of column.
          row_parallel_mode (:class:`colossalai.legacy.context.ParallelMode`): row parallel mode.
          col_parallel_mode (:class:`colossalai.legacy.context.ParallelMode`): column parallel mode.
          data_parallel_rank (int): data parallel rank.
          pipeline_parallel_rank (int): pipeline parallel rank
          pipeline_parallel_size (int): pipeline parallel size.
          tensor_parallel_size (int): tensor parallel size.
  
      Note:
          The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
          in `parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_
      """
      return _Classifier2p5D.apply(
          A,
          B,
          bias,
          tesseract_dim,
          out_shape,
          row_rank,
          col_rank,
          row_parallel_mode,
          col_parallel_mode,
          data_parallel_rank,
          pipeline_parallel_rank,
          pipeline_parallel_size,
          tensor_parallel_size,
      )
  
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 4 for calling a global function with example usage: classifier_2p5d(A, B, bias, tesseract_dim, out_shape, row_rank, col_rank, row_parallel_mode, col_parallel_mode, data_parallel_rank, pipeline_parallel_rank, pipeline_parallel_size, tensor_parallel_size) and returns: _Classifier2p5D