code: def _train(model, optimizer, use_fp16):
      inputs = torch.ones(32, 2).cuda()
      labels = torch.ones(32, 2)
      loss_fn = torch.nn.MSELoss(reduction="sum")
      model.train()
      with _get_fp16_context(use_fp16):
          pred = model(inputs)
          loss = loss_fn(pred, labels.to(pred.device))
          loss.backward()
      optimizer.step()
      return model, optimizer, loss
  
file_path: fairscale/tests/nn/pipe/test_parity.py
imports: import contextlib
  import copy
  import numpy
  import pytest
  import torch
  from fairscale.fair_dev.testing.testing import skip_if_single_gpu
  from fairscale.nn import Pipe
label_desc: Use image node_id 4 for calling a global function with example usage: _train(model, optimizer, use_fp16) and returns: model,  optimizer,  loss