code: def _mix_split(tensor, comm_spec):
      """
      Implement mix split operation. Mix split is only called for the backward of mix gather (Use ctx to keep consistent)
      Mix split shards the tensor on device mesh based on information provided by comm_spec. It is different from split
      because _mix_split shards the tensor in two dimensions of device mesh, while _split only shards in one dimension.
      Assume index of f and b target pairs are 'f' and 'b'
      S0S1 => [b, f], (1, 0)
      S1S0 => [b, f], (0, 1)
      S01R => [f], (0, 0)
      RS01 => [b], (0, 0)
      Example:
      mesh_shape = (2,4)
              # [[0, 1, 2, 3],
              #  [4, 5, 6, 7]]
              # return {0: [0, 4, 1, 5, 2, 6, 3, 7], 1: [0, 1, 2, 3, 4, 5, 6, 7]}
      """
      mesh_shape = comm_spec.device_meshes.shape
      dim = comm_spec.gather_dim
      total_slices = comm_spec.device_mesh.shape[0]
  
      # Get global rank
      rank = dist.get_rank()
  
      leading_group_dim = comm_spec.logical_process_axes[0]
      process_number_list = comm_spec.device_meshes.process_number_dict[
          leading_group_dim
      ]
      rank = process_number_list.index(rank)
  
      if (
          comm_spec.logical_process_axes[0]
          == comm_spec.logical_process_axes[1]
      ):
          length = tensor.shape[dim[0]] // total_slices
          start = length * rank
          output = torch.narrow(
              tensor, dim[0], start, length
          ).contiguous()
      else:
          tensor_shape = [tensor.shape[dim[0]], tensor.shape[dim[1]]]
          rank_slice = [
              mesh_shape[comm_spec.logical_process_axes[0]],
              mesh_shape[comm_spec.logical_process_axes[1]],
          ]
          length = [
              tensor_shape[0] // rank_slice[0],
              tensor_shape[1] // rank_slice[1],
          ]
          start = [
              (rank % rank_slice[0]) * length[0],
              (rank // rank_slice[0]) * length[1],
          ]
          tmp_output = torch.narrow(
              tensor, dim[0], start[0], length[0]
          ).contiguous()
          output = torch.narrow(
              tmp_output, dim[1], start[1], length[1]
          ).contiguous()
  
      return output
  
imports: import operator
  from enum import Enum
  from functools import reduce
  import torch
  import torch.distributed
  from torch.distributed import ReduceOp
label_desc: Use image node_id 6 for calling a global function with example usage: _mix_split(tensor, comm_spec) and returns: output