code: def mlp(self, enable_training=False):
      mlp1_up = self.client_module.mlp.up_proj.weight
      mlp1_gate = self.client_module.mlp.gate_proj.weight
      mlp2 = self.client_module.mlp.down_proj.weight
  
      mlp1 = Parameter(
          torch.cat((mlp1_up, mlp1_gate), dim=0),
          requires_grad=enable_training,
      )
  
      return mlp1, None, mlp2, None
  
imports: from .base import *
  from .features import HybridSplitQKVContainer, HybridGatedMLPContainer, MetaTensorContainer
  from deepspeed.utils.types import ActivationFuncType, NormType
  from deepspeed.model_implementations.transformers.ds_gpt import DeepSpeedGPTInference
  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 4 for calling the LLAMALayerPolicy obj's underlying member method code with example usage: obj.mlp(enable_training) and returns: mlp1,  None,  mlp2,  None