code: def attention(self, enable_training=False):
      qw = self.client_module.self_attn.q_proj.weight
      qb = self.client_module.self_attn.q_proj.bias
  
      kw = self.client_module.self_attn.k_proj.weight
      kb = self.client_module.self_attn.k_proj.bias
  
      vw = self.client_module.self_attn.v_proj.weight
      vb = self.client_module.self_attn.v_proj.bias
  
      qkvw = Parameter(
          torch.cat((qw, kw, vw), dim=0), requires_grad=enable_training
      )
      qkvb = Parameter(
          torch.cat((qb, kb, vb), dim=0), requires_grad=enable_training
      )
      return (
          qkvw,
          qkvb,
          self.client_module.self_attn.out_proj.weight,
          self.client_module.self_attn.out_proj.bias,
      )
  
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 3 for calling the HFOPTLayerPolicy obj's underlying member method code with example usage: obj.attention(enable_training) and returns: qkvw,  qkvb,  self,  self