code: def _run_pass(
      self, max_lr=1.0, total_steps=1000, end_zero=False, **args
  ):
      if "max_train_steps" not in args:
          args["max_train_steps"] = total_steps
      # for checks of correctness, hardcode warmup_rate to be 0
      args["warmup_rate"] = 0
      p = torch.nn.Parameter(torch.randn(4, 4))
      optimizer = torch.optim.SGD([p], lr=max_lr)
      scheduler = lr_scheduler.ParlAILRScheduler.lr_scheduler_factory(
          args, optimizer, {}, True
      )
      output = []
      for step in range(total_steps):
          scheduler.step(step)
          output.append(scheduler.get_last_lr())
      for value in output:
          assert value >= 0
      for step, o in enumerate(output):  # noqa: B007
          assert o <= max_lr
          assert o > 0 or step == total_steps - 1
      warmup_updates = args.get("warmup_updates", 0)
      assert warmup_updates >= 0
      if warmup_updates > 0:
          assert abs(max_lr - output[warmup_updates - 1]) < 0.04
          # LR is always linear
          for step in range(warmup_updates - 2):
              self.assertAlmostEqual(
                  output[step + 1] - output[step], 1 / warmup_updates
              )
      if end_zero:
          self.assertAlmostEquals(output[-1], 0)
      else:
          self.assertNotAlmostEqual(output[-1], 0)
      return output
  
imports: import os
  import unittest
  import torch
  import parlai.nn.lr_scheduler
  import parlai.utils.testing
label_desc: Use image node_id 1 for calling the TestLRSchedulers obj's underlying member method code with example usage: obj._run_pass(max_lr, total_steps, end_zero) and returns: output