code: def attention(self, enable_training=False):
      qw = self.client_module.attention.wq.weight
      kw = self.client_module.attention.wk.weight
      vw = self.client_module.attention.wv.weight
  
      qkvw = Parameter(
          torch.cat((qw, kw, vw), dim=0), requires_grad=enable_training
      )
  
      return qkvw, None, self.client_module.attention.wo.weight, None
  
imports: from .base import *
  from .features import HybridSplitQKVContainer, HybridGatedMLPContainer, MetaTensorContainer
  from deepspeed.utils.types import ActivationFuncType, NormType
  from deepspeed.model_implementations.transformers.ds_llama2 import DeepSpeedLlama2Inference
  import torch
  from torch.nn.parameter import Parameter
  from ..policy import TransformerPolicy, transformer_param_names, maybe_copy, maybe_copy_qkv, maybe_copy_geglu, maybe_get_lora
label_desc: Use image node_id 3 for calling the LLAMA2LayerPolicy obj's underlying member method code with example usage: obj.attention(enable_training) and returns: qkvw,  None,  self,  None