code: def mlp(self, enable_training=False):
      if self.pre_attn_norm:
          intermediate_ff = self.client_module.intermediate.dense_act
      else:
          intermediate_ff = self.client_module.intermediate.dense
  
      return (
          intermediate_ff.weight,
          intermediate_ff.bias,
          self.client_module.output.dense.weight,
          self.client_module.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 4 for calling the HFBertLayerPolicy obj's underlying member method code with example usage: obj.mlp(enable_training) and returns: intermediate_ff,  intermediate_ff,  self,  self