code: def create_multiple_workers(devices):
      model = [
          RemoteModuleParams(nn.Linear, (4, 4), {}),
          RemoteModuleParams(nn.ReLU, (), {}),
      ]
      pipe = create_sequence_pipeline(
          model, balance=[1, 1], chunks=1, devices=devices[:2]
      )
  
imports: import functools
  import tempfile
  from typing import Any, Dict, List, NamedTuple, Tuple
  import pytest
  import torch
  import torch.distributed.autograd
  from torch.distributed.nn import RemoteModule
  from torch.distributed.optim import DistributedOptimizer
  import torch.distributed.rpc
  import torch.multiprocessing
  import torch.nn
  from fairscale.experimental.nn.distributed_pipeline import DistributedLoss, DistributedPipeline, PipelineModulesGraph
  from fairscale.fair_dev.testing.testing import skip_due_to_flakyness, skip_if_single_gpu
  from fairscale.internal import torch_version
label_desc: Use image node_id 6 for calling a global function with example usage: create_multiple_workers(devices) without return types