code: def test(self, return_type):
      config_dict = {
          "train_batch_size": 4,
          "steps_per_print": 1,
          "optimizer": {"type": "Adam", "params": {"lr": 1e-4}},
          "fp16": {"enabled": True},
          "zero_optimization": {"stage": 3},
      }
      hidden_dim = 10
  
      class MyModel(torch.nn.Module):
          def __init__(self, hidden_dim):
              super(MyModel, self).__init__()
              self.l1 = torch.nn.Linear(hidden_dim, hidden_dim)
              self.cel = torch.nn.CrossEntropyLoss()
  
          def forward(self, x, y):
              x = self.l1(x)
              loss = self.cel(x, y)
              if return_type == dict:
                  val = {"a": x, "loss": loss, "b": 1, "c": None}
              elif return_type == list:
                  val = [x, loss]
              elif return_type == tuple:
                  val = (x, loss)
              else:
                  raise NotImplementedError
              return val
  
      with deepspeed.zero.Init():
          model = MyModel(hidden_dim)
  
      model, _, _, _ = deepspeed.initialize(
          model=model,
          model_parameters=model.parameters(),
          config=config_dict,
      )
      data_loader = random_dataloader(
          model=model,
          total_samples=50,
          hidden_dim=hidden_dim,
          device=model.device,
      )
      dist.barrier()
      for n, batch in enumerate(data_loader):
          loss = model(batch[0], batch[1])
          if return_type == dict:
              loss = loss["loss"]
          else:
              loss = loss[1]
          model.backward(loss)
          model.step()
  
imports: import math
  from typing import Dict, List, Set
  import pytest
  import deepspeed.comm
  import torch
  from torch import Tensor
  from torch.nn import Linear, Module
  from torch.nn.modules.container import ModuleList
  from torch.nn.modules.loss import L1Loss
  from torch.nn.parameter import Parameter
  from unit.common import DistributedTest
  from unit.simple_model import SimpleModel, random_dataloader
  import deepspeed
  from deepspeed.runtime.engine import DeepSpeedEngine
  from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
  from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
label_desc: Use image node_id 1 for calling the TestZero3DictFwd obj's underlying member method code with example usage: obj.test(return_type) and returns: val