code: def mlp(self, moe_type="standard", enable_training=False):
      # for now, all of this is tightly coupled to megatron-deepspeed moe implementation
      # todo: think and refactor this to be more general
  
      # from deepspeed.moe.utils import has_moe_layers
      # moe, _ = has_moe_layers(self.client_module)
  
      moe_experts = (
          self.client_module.mlp.deepspeed_moe.experts.deepspeed_experts
          if moe_type == "standard"
          else self.client_module.mlp.moe.deepspeed_moe.experts.deepspeed_experts
      )
      num_experts = len(moe_experts)
      self.num_experts = num_experts
  
      if moe_type == "standard":
          return (
              [
                  moe_experts[i].dense_h_to_4h.weight
                  for i in range(num_experts)
              ],
              [
                  moe_experts[i].dense_h_to_4h.bias
                  for i in range(num_experts)
              ],
              [
                  moe_experts[i].dense_4h_to_h.weight
                  for i in range(num_experts)
              ],
              [
                  moe_experts[i].dense_4h_to_h.bias
                  for i in range(num_experts)
              ],
          )
      else:
          return (
              [
                  moe_experts[i].dense_h_to_4h.weight
                  for i in range(num_experts)
              ],
              [
                  moe_experts[i].dense_h_to_4h.bias
                  for i in range(num_experts)
              ],
              [
                  moe_experts[i].dense_4h_to_h.weight
                  for i in range(num_experts)
              ],
              [
                  moe_experts[i].dense_4h_to_h.bias
                  for i in range(num_experts)
              ],
              self.client_module.mlp.mlp.dense_h_to_4h.weight,
              self.client_module.mlp.mlp.dense_h_to_4h.bias,
              self.client_module.mlp.mlp.dense_4h_to_h.weight,
              self.client_module.mlp.mlp.dense_4h_to_h.bias,
              self.client_module.mlp.coefficient.weight,
          )
  
file_path: DeepSpeed/deepspeed/module_inject/containers/megatron_gpt_moe.py
imports: from .base import *
  from .base_moe import *
  from .features.megatron import MegatronContainer
  from deepspeed.model_implementations.transformers.ds_megatron_gpt import DeepSpeedMegatronGPTInference
  import torch
  from .megatron_gpt import MegatronLayerPolicy
  from packaging import version
label_desc: Use image node_id 3 for calling the MegatronMoELayerPolicy obj's underlying member method code with example usage: obj.mlp(moe_type, enable_training) and returns: unknown,  unknown,  unknown,  unknown, unknown,  unknown,  unknown,  unknown,  self,  self,  self,  self,  self