docstr: S0S1 -> RR
  S1S0 -> RR
  S01R -> RR
  RS01 -> RR
docstr_tok: ["S0S1","-",">","RR","S1S0","-",">","RR","S01R","-",">","RR","RS01","-",">","RR"]
code: def get_all_mix_gather_spec(
      self, source_spec: ShardingSpec, orig_cost_dict: Dict[str, float]
  ) -> Dict[ShardingSpec, float]:
      """
      S0S1 -> RR
      S1S0 -> RR
      S01R -> RR
      RS01 -> RR
      """
      valid_spec_dict = {}
      comm_pattern = CollectiveCommPattern.MIXGATHER_FWD_SPLIT_BWD
      tensor_dims = len(source_spec.entire_shape)
      for f_index in range(tensor_dims - 1):
          for b_index in range(f_index + 1, tensor_dims):
              if (f_index not in source_spec.dim_partition_dict) and (
                  b_index not in source_spec.dim_partition_dict
              ):
                  continue
              else:
                  if f_index in source_spec.dim_partition_dict:
                      # skip (S10, R) -> (R, R)
                      if (
                          len(f_target_pair[1]) == 2
                          and f_target_pair[1][0] >= f_target_pair[1][1]
                      ):
                          continue
                      f_target_pair = (
                          f_index,
                          deepcopy(
                              source_spec.dim_partition_dict[f_index]
                          ),
                      )
                  else:
                      f_target_pair = (f_index, [])
                  if b_index in source_spec.dim_partition_dict:
                      # skip (R, S10) -> (R, R)
                      if (
                          len(b_target_pair[1]) == 2
                          and b_target_pair[1][0] >= b_target_pair[1][1]
                      ):
                          continue
                      b_target_pair = (
                          b_index,
                          deepcopy(
                              source_spec.dim_partition_dict[b_index]
                          ),
                      )
                  else:
                      b_target_pair = (b_index, [])
  
              gather_dim, logical_process_axes = mix_gather_simulator(
                  f_target_pair, b_target_pair
              )
              comm_spec = CommSpec(
                  comm_pattern,
                  sharding_spec=source_spec,
                  gather_dim=gather_dim,
                  logical_process_axis=logical_process_axes,
                  forward_only=self.forward_only,
                  mix_gather=True,
              )
              cost_dict = comm_spec.get_comm_cost()
              new_dim_partition_dict = {}
              # generate new sharding spec
              try:
                  new_sharding_spec = ShardingSpec(
                      source_spec.device_mesh,
                      source_spec.entire_shape,
                      dim_partition_dict=new_dim_partition_dict,
                  )
                  for phase, cost in cost_dict.items():
                      cost_dict[phase] = cost + orig_cost_dict[phase]
                  valid_spec_dict[new_sharding_spec] = (
                      comm_spec,
                      cost_dict,
                  )
              except ShardingSpecException:
                  pass
  
      return valid_spec_dict
  
imports: import math
  from copy import deepcopy
  from dataclasses import dataclass
  from typing import Dict, List, Tuple
  import numpy
  import torch
  from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, TrainCycleItem
  from colossalai.context.singleton_meta import SingletonMeta
  from colossalai.tensor.sharding_spec import ShardingSpec, ShardingSpecException
  from colossalai.tensor.utils import all_gather_simulator, all_to_all_simulator, mix_gather_simulator, shard_simulator
  from .comm_spec import *
label_desc: Use image node_id 9 for calling the ShapeConsistencyManager obj's underlying member method code with example usage: obj.get_all_mix_gather_spec(source_spec, orig_cost_dict) and returns: valid_spec_dict