code: def inference(self):
      dataset_type = []
      if "val" in self.run_type:
          dataset_type.append("val")
      if any(
          rt in self.run_type for rt in ["inference", "test", "predict"]
      ):
          dataset_type.append("test")
  
      for dataset in dataset_type:
          if self.config.evaluation.predict:
              self.on_prediction_start()
              self.prediction_loop(dataset)
              self.on_prediction_end()
          else:
              self.on_test_start()
              logger.info(f"Starting inference on {dataset} set")
              report, meter = self.evaluation_loop(
                  dataset, use_tqdm=True
              )
              self.on_test_end(report=report, meter=meter)
  
imports: import logging
  import warnings
  import omegaconf
  import torch
  from mmf.common.registry import registry
  from mmf.datasets.multi_datamodule import MultiDataModule
  from mmf.modules.metrics import Metrics
  from mmf.trainers.base_trainer import BaseTrainer
  from mmf.trainers.callbacks.checkpoint import CheckpointCallback
  from mmf.trainers.callbacks.early_stopping import EarlyStoppingCallback
  from mmf.trainers.callbacks.logistics import LogisticsCallback
  from mmf.trainers.callbacks.lr_scheduler import LRSchedulerCallback
  from mmf.trainers.core.callback_hook import TrainerCallbackHookMixin
  from mmf.trainers.core.device import TrainerDeviceMixin
  from mmf.trainers.core.evaluation_loop import TrainerEvaluationLoopMixin
  from mmf.trainers.core.profiling import TrainerProfilingMixin
  from mmf.trainers.core.training_loop import TrainerTrainingLoopMixin
  from mmf.utils.build import build_model, build_optimizer
  from mmf.utils.general import print_model_parameters
  from omegaconf import DictConfig, OmegaConf
  from packaging import version
label_desc: Use image node_id 10 for calling the MMFTrainer obj's underlying member method code with example usage: obj.inference() without return types