code: def module_policy(self):
      from transformers.models.opt.modeling_opt import OPTForCausalLM
  
      policy = super().module_policy()
      if self.shard_config.enable_tensor_parallelism:
          self.append_or_create_submodule_replacement(
              description=SubModuleReplacementDescription(
                  suffix="lm_head",
                  target_module=Linear1D_Col,
                  kwargs=dict(gather_output=True),
              ),
              policy=policy,
              target_key=OPTForCausalLM,
          )
      if self.pipeline_stage_manager:
          self.set_pipeline_forward(
              model_cls=OPTForCausalLM,
              new_forward=OPTPipelineForwards.opt_for_causal_lm_forward,
              policy=policy,
          )
  
      return policy
  
imports: import warnings
  from functools import partial
  from typing import Callable, Dict, List
  import torch.nn
  from torch import Tensor, nn
  from colossalai.shardformer.layer import FusedLayerNorm, LayerNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D
  from .._utils import getattr_
  from ..modeling.jit import get_jit_fused_dropout_add_func
  from ..modeling.opt import OPTPipelineForwards, get_jit_fused_opt_decoder_layer_forward, get_opt_flash_attention_forward
  from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
label_desc: Use image node_id 1 for calling the OPTForCausalLMPolicy obj's underlying member method code with example usage: obj.module_policy() and returns: policy