code: def __init__(
      self, client_module, inference=True, use_load_prefix=True
  ):
      super().__init__(
          inference,
          linear_layer=True,
          pre_attn_norm=True,
          use_load_prefix=use_load_prefix,
      )
      self.client_module = client_module
      try:
          import transformers
  
          HFOPTLayerPolicy._orig_layer_class = (
              transformers.models.opt.modeling_opt.OPTDecoderLayer
          )
      except:
          HFOPTLayerPolicy._orig_layer_class = None
  
      if hasattr(TransformerPolicy, "hf_model_config") and hasattr(
          TransformerPolicy.hf_model_config, "activation_function"
      ):
          if (
              TransformerPolicy.hf_model_config.activation_function
              == "relu"
          ):
              self.mlp_act_func_type = ActivationFuncType.ReLU
          elif (
              TransformerPolicy.hf_model_config.activation_function
              in ["gelu", "gelu_new"]
          ):
              self.mlp_act_func_type = ActivationFuncType.GELU
          else:
              raise ValueError(
                  "Unsupported activation function: {}".format(
                      TransformerPolicy.hf_model_config.activation_function
                  )
              )
      else:
          self.mlp_act_func_type = ActivationFuncType.ReLU
  
imports: from .base import *
  from .features import MetaTensorContainer, HybridSplitQKVContainer
  from deepspeed.model_implementations.transformers.ds_opt import DeepSpeedOPTInference
  import torch
  from torch.nn.parameter import Parameter
  from ..policy import TransformerPolicy
  from ..policy import transformer_param_names
  from ..policy import maybe_copy
  from ..policy import maybe_copy_qkv
  from ..policy import maybe_get_lora
  from deepspeed.utils.types import ActivationFuncType
label_desc: Use image node_id 1 to create a new HFOPTLayerPolicy object from inherited base classes: TransformerPolicy with example: obj = HFOPTLayerPolicy(client_module, inference, use_load_prefix)