code: def attention(self, enable_training=False):
      qw = self.client_module.attn.q_proj.weight
      kw = self.client_module.attn.k_proj.weight
      vw = self.client_module.attn.v_proj.weight
  
      qkvw = Parameter(
          torch.cat((qw, kw, vw), dim=0), requires_grad=enable_training
      )
  
      return (
          qkvw,
          None,
          self.client_module.attn.out_proj.weight,
          None,
      )
  
imports: from .base import *
  from .features.meta_tensor import MetaTensorContainer
  from .features.split_qkv import HybridSplitQKVContainer
  from deepspeed.model_implementations.transformers.ds_gpt import DeepSpeedGPTInference
  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
label_desc: Use image node_id 3 for calling the HFGPTJLayerPolicy obj's underlying member method code with example usage: obj.attention(enable_training) and returns: qkvw,  None,  self,  None