class_docstr: A customized communication operation which forward is an all to all operation,
  backward is an all to all operation.
  
  Args:
      input_: input matrix.
      comm_spec: comm_spec will give information like process group, rank list, etc.
class_docstr_tok: ["A","customized","communication","operation","which","forward","is","an","all","to","all","operation",",","backward","is","an","all","to","all","operation",".","Args",":","input_",":","input","matrix",".","comm_spec",":","comm_spec","will","give","information","like","process","group",",","rank","list",",","etc","."]
code: def forward(ctx, input_, comm_spec):
      output = _all_to_all(input_, comm_spec)
      comm_spec_for_backward = CommSpec(
          comm_pattern=comm_spec.comm_pattern,
          sharding_spec=comm_spec.sharding_spec,
          gather_dim=comm_spec.shard_dim,
          shard_dim=comm_spec.gather_dim,
          logical_process_axis=comm_spec.logical_process_axis,
      )
      ctx.comm_spec = comm_spec_for_backward
      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 2 for calling the _AllToAll obj's underlying member method code with example usage: obj.forward(ctx, input_, comm_spec) and returns: output