code: def preprocess(self):
      if self.shard_config.enable_tensor_parallelism:
          # Resize embedding
          vocab_size = self.model.config.vocab_size
          world_size = self.shard_config.tensor_parallel_size
  
          if vocab_size % world_size != 0:
              new_vocab_size = (
                  vocab_size + world_size - vocab_size % world_size
              )
              self.model.resize_token_embeddings(new_vocab_size)
  
      return self.model
  
imports: import warnings
  from functools import partial
  from typing import Callable, Dict, List, Optional, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from torch.nn import Module
  from transformers.modeling_outputs import CausalLMOutputWithPast
  from transformers.utils import logging
  from colossalai.moe.manager import MOE_MANAGER
  from colossalai.pipeline.stage_manager import PipelineStageManager
  from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col
  from colossalai.shardformer.policies.base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
  from .modeling_openmoe import OpenMoeDecoderLayer, OpenMoeForCausalLM, OpenMoeModel
label_desc: Use image node_id 2 for calling the OpenMoePolicy obj's underlying member method code with example usage: obj.preprocess() and returns: self