code: def __init__(self, num_train_data=100, config=None):
      self.training_config = config.training
      self.config = config
      registry.register("config", self.config)
  
      if torch.cuda.is_available():
          self.device = "cuda"
      else:
          self.device = "cpu"
  
      self.distributed = False
  
      self.on_batch_start = MagicMock(return_value=None)
      self.on_update_start = MagicMock(return_value=None)
      self.logistics_callback = MagicMock(return_value=None)
      self.logistics_callback.log_interval = MagicMock(
          return_value=None
      )
      self.on_batch_end = MagicMock(return_value=None)
      self.on_update_end = MagicMock(return_value=None)
      self.on_validation_start = MagicMock(return_value=None)
      self.scaler = torch.cuda.amp.GradScaler(enabled=False)
      self.early_stop_callback = MagicMock(return_value=None)
      self.on_validation_end = MagicMock(return_value=None)
      self.metrics = MagicMock(return_value={})
  
      self.num_data = num_train_data
      self.run_type = self.config.get("run_type", "train")
  
imports: import functools
  from unittest.mock import MagicMock
  import torch
  from mmf.common.registry import registry
  from mmf.common.sample import SampleList
  from mmf.datasets.lightning_multi_datamodule import LightningMultiDataModule
  from mmf.datasets.mmf_dataset_builder import MMFDatasetBuilder
  from mmf.datasets.multi_datamodule import MultiDataModule
  from mmf.trainers.mmf_trainer import MMFTrainer
  from omegaconf import OmegaConf
  from tests.test_utils import NumbersDataset
label_desc: Use image node_id 1 to create a new TrainerTrainingLoopMock object from inherited base classes: MMFTrainer with example: obj = TrainerTrainingLoopMock(num_train_data, config)