code: def test_optim_config_parse_bypass(self):
      basic_optim_config = {"weight_decay": 0.001, "betas": [0.8, 0.5]}
      parsed_params = optim.parse_optimizer_args(
          "novograd", basic_optim_config
      )
      assert (
          parsed_params["weight_decay"]
          == basic_optim_config["weight_decay"]
      )
      assert parsed_params["betas"][0] == basic_optim_config["betas"][0]
      assert parsed_params["betas"][1] == basic_optim_config["betas"][1]
  
      dict_config = omegaconf.OmegaConf.create(basic_optim_config)
      parsed_params = optim.parse_optimizer_args(
          "novograd", dict_config
      )
      assert (
          parsed_params["weight_decay"] == dict_config["weight_decay"]
      )
      assert parsed_params["betas"][0] == dict_config["betas"][0]
      assert parsed_params["betas"][1] == dict_config["betas"][1]
  
imports: import math
  import random
  import omegaconf
  import pytest
  import pytorch_lightning
  import torch
  import torch.optim
  from pytorch_lightning.utilities import rank_zero_only
  from nemo.core import config, optim
  from nemo.core.optim.lr_scheduler import AVAILABLE_SCHEDULERS
  from nemo.core.optim.optimizers import AVAILABLE_OPTIMIZERS
  from nemo.utils import logging
label_desc: Use image node_id 3 for calling the TestOptimizersSchedulers obj's underlying member method code with example usage: obj.test_optim_config_parse_bypass() without return types