code: def non_split(self):
      name = f"RR = RR x RR"
  
      # get sharding spec
      dim_partition_dict_mapping = {
          "input": {},
          "other": {},
          "bias": {},
          "output": {},
      }
  
      # We don't have to do anything special for bias here, because
      # the bias is already the same sharding spec as the output.
      sharding_spec_mapping = self.to_sharding_spec_mapping(
          dim_partition_dict_mapping
      )
  
      # get communication action
      communication_action_mapping = {}
  
      return self.get_sharding_strategy(
          name=name,
          sharding_spec_mapping=sharding_spec_mapping,
          communication_action_mapping=communication_action_mapping,
      )
  
imports: import operator
  from functools import reduce
  from typing import List
  from colossalai.auto_parallel.tensor_shard.options import SolverPerference
  from colossalai.auto_parallel.tensor_shard.sharding_strategy import CommType, MemoryCost, ShardingStrategy, TrainCycleItem
  from colossalai.auto_parallel.tensor_shard.utils import ignore_sharding_exception
  from colossalai.tensor.shape_consistency import CollectiveCommPattern
  from .strategy_generator import StrategyGenerator
label_desc: Use image node_id 15 for calling the LinearProjectionStrategyGenerator obj's underlying member method code with example usage: obj.non_split() and returns: self