code: def train_step(input, target, model, loss_fn, optimizer, **unused):
      """Do forward, backward and parameter update."""
      model.train()
      output = model(input)
      loss = loss_fn(output, target)
      optimizer.backward(loss)
      optimizer.step()
  
imports: import argparse
  import functools
  import random
  import unittest
  from multiprocessing import Manager
  import torch
  import torch.nn
  from omegaconf import OmegaConf
  from fairseq import optim
  from fairseq.distributed import utils
label_desc: Use image node_id 2 for calling a global function with example usage: train_step(input, target, model, loss_fn, optimizer) without return types