code: def test_swa_auto(self):
      # Tests SWA in Auto mode: values of x and y after opt.finalize()
      # should be equal to the manually computed averages
      x, y, loss_fun, opt = self._define_vars_loss_opt()
      swa_start = 5
      swa_freq = 2
      opt = StochasticWeightAveraging(
          opt, swa_start=swa_start, swa_freq=swa_freq, swa_lr=0.001
      )
  
      x_sum = torch.zeros_like(x)
      y_sum = torch.zeros_like(y)
      n_avg = 0
      for i in range(1, 11):
          opt.zero_grad()
          loss = loss_fun(x, y)
          loss.backward()
          opt.step()
          n_avg, x_sum, y_sum = self._update_test_vars(
              i,
              swa_freq,
              swa_start,
              n_avg,
              x_sum,
              y_sum,
              x,
              y,
              upd_fun=lambda: None,
          )
  
      opt.finalize()
      x_avg = x_sum / n_avg
      y_avg = y_sum / n_avg
      assert equal(x_avg, x)
      assert equal(y_avg, y)
  
imports: import contextlib
  import functools
  import re
  import warnings
  from unittest import TestCase
  import torch
  from pytext.optimizer import Lamb, StochasticWeightAveraging
  from torch import nn, optim, sparse
  from torch.autograd import Variable
  from torch.utils import data
label_desc: Use image node_id 10 for calling the TestSWA obj's underlying member method code with example usage: obj.test_swa_auto() without return types