code: def add_lm_prediction_policy(self, base_policy):
      from transformers.models.bert.modeling_bert import (
          BertLMPredictionHead,
      )
  
      method_replacement = {
          "_save_to_state_dict": col_nn.ParallelModule._save_to_state_dict,
          "_load_from_state_dict": col_nn.ParallelModule._load_from_state_dict,
      }
      self.append_or_create_method_replacement(
          description=method_replacement,
          policy=base_policy,
          target_key=BertLMPredictionHead,
      )
      return base_policy
  
imports: from functools import partial
  from typing import Callable, Dict, List
  import torch.nn
  from torch import Tensor
  from torch.nn import Module
  import colossalai.shardformer.layer
  from ..modeling.bert import BertPipelineForwards, bert_sequence_parallel_forward_fn, get_bert_flash_attention_forward, get_jit_fused_bert_output_forward, get_jit_fused_bert_self_output_forward
  from ..modeling.jit import get_jit_fused_dropout_add_func
  from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
label_desc: Use image node_id 5 for calling the BertPolicy obj's underlying member method code with example usage: obj.add_lm_prediction_policy(base_policy) and returns: base_policy