code: def preprocess(self):
      # Resize embedding
      if self.shard_config.enable_tensor_parallelism:
          vocab_size = self.model.config.padded_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)
  
      if self.pipeline_stage_manager is not None:
          # the batch_size_dim is bounded to Model
          bsz_dim = 1
          setattr(self.model, "batch_size_dim", bsz_dim)
  
      return self.model
  
imports: from functools import partial
  from typing import Callable, Dict, List, Union
  import torch.nn
  from torch import Tensor
  import colossalai.shardformer.layer
  from colossalai.shardformer.modeling.chatglm2 import ChatGLMPipelineForwards
  from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import ChatGLMForConditionalGeneration, ChatGLMModel
  from ..modeling.chatglm2 import get_chatglm_sequence_parallel_forward_fn, get_flash_core_attention_forward, get_jit_fused_glm_block_forward
  from ..modeling.jit import get_jit_fused_dropout_add_func
  from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
label_desc: Use image node_id 2 for calling the ChatGLMPolicy obj's underlying member method code with example usage: obj.preprocess() and returns: self