code: def get_dataloader(config):
      meta_processor_cls = getattr(
          processors, config.dataset.meta_processor
      )
      video_processor_cls = getattr(
          processors, config.dataset.video_processor
      )
      text_processor_cls = getattr(
          processors, config.dataset.text_processor
      )
      aligner_cls = getattr(processors, config.dataset.aligner)
  
      meta_processor = meta_processor_cls(config.dataset)
      video_processor = video_processor_cls(config.dataset)
      text_processor = text_processor_cls(config.dataset)
      aligner = aligner_cls(config.dataset)
  
      test_data = MMDataset(
          meta_processor,
          video_processor,
          text_processor,
          aligner,
      )
      print("test_len", len(test_data))
      output = test_data[0]
      test_data.print_example(output)
  
      test_dataloader = DataLoader(
          test_data,
          batch_size=config.fairseq.dataset.batch_size,
          shuffle=False,
          num_workers=6,
          collate_fn=test_data.collater,
      )
      return test_dataloader
  
file_path: fairseq/examples/MMPT/mmpt_cli/predict.py
imports: import os
  import glob
  import argparse
  import pprint
  import omegaconf
  from omegaconf import OmegaConf
  from torch.utils.data import DataLoader
  from mmpt.utils import load_config, set_seed
  from mmpt.evaluators import Evaluator
  from mmpt.evaluators import predictor
  from mmpt.tasks import Task
  from mmpt import processors
  from mmpt.datasets import MMDataset
label_desc: Use image node_id 1 for calling a global function with example usage: get_dataloader(config) and returns: test_dataloader