class_docstr: EmbeddingStrategyGenerator is a generic class to generate strategies for nn.Embedding or F.embedding.
  The operation data is defined as `output = input x other`.
class_docstr_tok: ["EmbeddingStrategyGenerator","is","a","generic","class","to","generate","strategies","for","nn.Embedding","or","F.embedding",".","The","operation","data","is","defined","as","`","output","=","input","x","other","`","."]
code: def non_split(self):
      name = f"RR = R x RR"
  
      dim_partition_dict_mapping = {
          "input": {},
          "other": {},
          "output": {},
      }
  
      sharding_spec_mapping = self.to_sharding_spec_mapping(
          dim_partition_dict_mapping
      )
  
      return self.get_sharding_strategy(
          name=name,
          sharding_spec_mapping=sharding_spec_mapping,
          communication_action_mapping={},
      )
  
file_path: colossalai/colossalai/auto_parallel/tensor_shard/node_handler/strategy/embedding_generator.py
imports: import copy
  import operator
  from functools import reduce
  from typing import List
  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 4 for calling the EmbeddingStrategyGenerator obj's underlying member method code with example usage: obj.non_split() and returns: self