code: def test(self, topo_config):
      config_dict = {
          "train_batch_size": 16,
          "train_micro_batch_size_per_gpu": 4,
          "steps_per_print": 20,
          "optimizer": {
              "type": "OneBitLamb",
              "params": {
                  "lr": 0.00001,
                  "betas": [0.9, 0.999],
                  "eps": 1e-8,
                  "weight_decay": 3e-7,
                  "freeze_step": 200,
                  "cuda_aware": False,
                  "comm_backend_name": "nccl",
              },
          },
          "gradient_clipping": 1.0,
          "zero_optimization": {"stage": 0},
          "fp16": {
              "enabled": True,
              "loss_scale": 0,
              "initial_scale_power": 16,
          },
          "pipeline": {
              "seed_layers": True,
              "activation_checkpoint_interval": 1,
          },
      }
  
      topo = PipeTopo(**topo_config)
      steps = 500  # Must be >=100
  
      # Allocate model for consistent initial weights.
      init_net = AlexNetPipe()
  
      test_net = copy.deepcopy(init_net)
      test_model = PipelineModule(
          layers=test_net.to_layers(),
          topology=topo,
          loss_fn=nn.CrossEntropyLoss(),
      )
  
      test_losses = train_cifar(
          test_model,
          config=config_dict,
          num_steps=steps,
          fp16=config_dict["fp16"]["enabled"],
      )
  
imports: import torch
  import torch.nn
  import deepspeed.comm
  import deepspeed
  import pytest
  import copy
  import os
  import numpy
  from deepspeed.runtime.pipe.topology import PipeDataParallelTopology
  from deepspeed.ops.op_builder import OpBuilder
  from deepspeed.runtime.pipe.module import PipelineModule
  from unit.common import DistributedTest
  from unit.simple_model import SimpleModel, random_dataloader
  from unit.alexnet_model import AlexNetPipe, train_cifar
label_desc: Use image node_id 1 for calling the TestOneBitLambFP16Pipeline obj's underlying member method code with example usage: obj.test(topo_config) without return types