code: def _train_offload_model(
      model,
      device,
      offload_device,
      use_fp16=False,
      checkpoint_activation=False,
      num_microbatches=1,
  ):
      omodel = copy.deepcopy(model)
      offload_model = OffloadModel(
          model=omodel,
          device=device,
          offload_device=offload_device,
          num_slices=2,
          checkpoint_activation=checkpoint_activation,
          num_microbatches=num_microbatches,
      )
      offload_optimizer = torch.optim.SGD(
          offload_model.parameters(), lr=0.001
      )
      return _train(offload_model, offload_optimizer, use_fp16, device)
  
file_path: fairscale/tests/experimental/nn/test_offload.py
imports: import contextlib
  import copy
  import numpy
  import pytest
  import torch
  from fairscale.experimental.nn.offload import OffloadModel
  from fairscale.fair_dev.testing.testing import skip_if_no_cuda
  from fairscale.internal import torch_version
label_desc: Use image node_id 8 for calling a global function with example usage: _train_offload_model(model, device, offload_device, use_fp16, checkpoint_activation, num_microbatches) and returns: _train