code: def async_event_loop_interleave_simple():
      model = nn.Sequential(
          nn.Linear(10, 10),
          nn.ReLU(inplace=False),
          nn.Linear(10, 10),
          nn.ReLU(inplace=False),
      )
      pipe = AMPnetPipe(
          module=model,
          balance=[2, 2],
          worker_map=get_worker_map(),
          chunks=10,
          checkpoint="never",
      )
      fake_dataset = FakeDataset()
      fake_dataloader = DataLoader(
          fake_dataset, batch_size=4, shuffle=True, num_workers=0
      )
      loss = nn.MSELoss()
      opt = MySGD(model.parameters(), lr=0.01)
      transform_and_log = AMPnetDelegate()
      pipe.interleave(fake_dataloader, loss, opt, transform_and_log)
  
imports: import torch
  from torch import nn
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader, Dataset
  from fairscale.experimental.nn.ampnet_pipe.pipe import AMPnetPipe
  from fairscale.fair_dev.testing.testing import get_worker_map, torch_spawn
label_desc: Use image node_id 1 for calling a global function with example usage: async_event_loop_interleave_simple() without return types