code: def step_test(optimizer, weight, bias, input):
      # to check if the optimizer can be printed as a string
      optimizer.__repr__()
  
      def fn():
          optimizer.zero_grad()
          y = weight.mv(input)
          if (
              y.is_cuda
              and bias.is_cuda
              and y.get_device() != bias.get_device()
          ):
              y = y.cuda(bias.get_device())
          loss = (y + bias).pow(2).sum()
          loss.backward()
          return loss
  
      initial_value = fn().item()
      for _i in range(5):
          optimizer.step(fn)
      assert fn().item() < initial_value
  
imports: from copy import deepcopy
  import functools
  import pytest
  import torch
label_desc: Use image node_id 4 for calling a global function with example usage: step_test(optimizer, weight, bias, input) and returns: loss