code: def attention(self, enable_training=False):
      qw = self.client_module.attention.self.query.weight
      qb = self.client_module.attention.self.query.bias
      kw = self.client_module.attention.self.key.weight
      kb = self.client_module.attention.self.key.bias
      vw = self.client_module.attention.self.value.weight
      vb = self.client_module.attention.self.value.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.attention.output.dense.weight,
          self.client_module.attention.output.dense.bias,
      )
  
file_path: deepspeed/deepspeed/module_inject/containers/bert.py
imports: from .base import *
  from deepspeed.model_implementations.transformers.ds_bert import DeepSpeedBERTInference
  import torch
  from torch.nn.parameter import Parameter
  from ..policy import TransformerPolicy
label_desc: Use image node_id 3 for calling the HFBertLayerPolicy obj's underlying member method code with example usage: obj.attention(enable_training) and returns: qkvw,  qkvb,  self,  self