code: def step(self, closure=None):
      loss = None
      if closure is not None:
          with torch.enable_grad():
              loss = closure()
      for group in self.param_groups:
          for p in group["params"]:
              if self._step == 0:
                  p.data = 0.1 * torch.ones(p.shape)
              elif self._step == 1:
                  p.data = 0.0 * torch.ones(p.shape)
              else:
                  p.data = 0.01 * torch.ones(p.shape)
      self._step += 1
      return loss
  
imports: import math
  import os
  import re
  from pathlib import Path
  from typing import Any
  import pytest
  import pytorch_lightning
  import torch
  from omegaconf import OmegaConf
  from omegaconf.errors import OmegaConfBaseException
  from pytorch_lightning import Callback
  from pytorch_lightning.loops import _TrainingEpochLoop
  from nemo.constants import NEMO_ENV_VARNAME_VERSION
  from nemo.core.classes import ModelPT
  from nemo.utils.exp_manager import CheckpointMisconfigurationError, LoggerMisconfigurationError, NotFoundError, exp_manager
label_desc: Use image node_id 2 for calling the MyTestOptimizer obj's underlying member method code with example usage: obj.step(closure) and returns: loss