code: def __init__(
      self, hidden_dim, num_experts=4, ep_size=1, use_residual=False
  ):
      super(SimpleMoEModel, self).__init__()
      self.linear1 = torch.nn.Linear(hidden_dim, hidden_dim)
      expert = torch.nn.Sequential(
          torch.nn.Linear(hidden_dim, hidden_dim),
          torch.nn.Linear(hidden_dim, hidden_dim),
      )
      # using two MoE layers to check implications of sharing a single storage
      self.moe_1 = MoE(
          hidden_size=hidden_dim,
          expert=expert,
          ep_size=ep_size,
          use_residual=use_residual,
          num_experts=num_experts,
          k=1,
      )
      # interleaving MoE modules with dense to create an opportunity
      # for gradients to be merged in ZeRO stage 2 average_tensor reduce bucket
      self.linear2 = torch.nn.Linear(hidden_dim, hidden_dim)
      self.moe_2 = MoE(
          hidden_size=hidden_dim,
          expert=expert,
          ep_size=ep_size,
          use_residual=use_residual,
          num_experts=num_experts,
          k=1,
      )
      self.linear3 = torch.nn.Linear(hidden_dim, hidden_dim)
      self.cross_entropy_loss = torch.nn.CrossEntropyLoss()
  
imports: import os
  import json
  import argparse
  import torch
  from collections import OrderedDict
  from deepspeed.pipe import PipelineModule, LayerSpec
  from deepspeed.moe.layer import MoE
  from deepspeed.accelerator import get_accelerator
  import deepspeed.comm
label_desc: Use image node_id 1 to create a new SimpleMoEModel object from inherited base classes: torch.nn with example: obj = SimpleMoEModel(hidden_dim, num_experts, ep_size, use_residual)