code: def attention(self, enable_training=False):
      qw = self.client_module.self_attn.q_proj.weight
      kw = self.client_module.self_attn.k_proj.weight
      vw = self.client_module.self_attn.v_proj.weight
      qb = self.client_module.self_attn.q_proj.bias
      kb = self.client_module.self_attn.k_proj.bias
      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.o_proj.weight,
          self.client_module.self_attn.o_proj.bias,
      )
  
imports: import importlib
  import torch
  from torch.nn.parameter import Parameter
  from deepspeed.model_implementations.transformers.ds_gpt import DeepSpeedGPTInference
  from deepspeed.utils.types import ActivationFuncType, NormType
  from ..policy import TransformerPolicy, maybe_copy, maybe_copy_geglu, maybe_copy_qkv, maybe_get_lora, transformer_param_names
  from .base import *
  from .features import HybridGatedMLPContainer, HybridSplitQKVContainer
label_desc: Use image node_id 4 for calling the InternLMLayerPolicy obj's underlying member method code with example usage: obj.attention(enable_training) and returns: qkvw,  qkvb,  self,  self