code: def setUp(self):
      self.trainer = argparse.Namespace()
      self.config = load_yaml(os.path.join("configs", "defaults.yaml"))
      self.config = OmegaConf.merge(
          self.config,
          {
              "model": "simple",
              "model_config": {},
              "training": {
                  "lr_scheduler": True,
                  "lr_ratio": 0.1,
                  "lr_steps": [1, 2],
                  "use_warmup": False,
              },
          },
      )
      # Keep original copy for testing purposes
      self.trainer.config = deepcopy(self.config)
      registry.register("config", self.trainer.config)
  
      self.trainer.model = SimpleModule()
      self.trainer.val_loader = torch.utils.data.DataLoader(
          NumbersDataset(), batch_size=self.config.training.batch_size
      )
  
      self.trainer.optimizer = torch.optim.Adam(
          self.trainer.model.parameters(), lr=1e-01
      )
      self.trainer.lr_scheduler_callback = LRSchedulerCallback(
          self.config, self.trainer
      )
  
imports: import argparse
  import os
  import unittest
  from copy import deepcopy
  import torch
  from mmf.common.registry import registry
  from mmf.models.base_model import BaseModel
  from mmf.trainers.callbacks.lr_scheduler import LRSchedulerCallback
  from mmf.utils.configuration import load_yaml
  from omegaconf import OmegaConf
label_desc: Use image node_id 1 for calling the TestLogisticsCallback obj's underlying member method code with example usage: obj.setUp() without return types