code: def preprocess_sample_info(self, sample_info):
      path = self._get_path_based_on_index(
          self.config, "annotations", self._index
      )
      # NOTE, TODO: Code duplication w.r.t to STVQA, revisit
      # during dataset refactor to support variable dataset classes
      if "stvqa" in path:
          feature_path = sample_info["feature_path"]
          append = "train"
  
          if self.dataset_type == "test":
              append = "test_task3"
  
          if not feature_path.startswith(append):
              feature_path = append + "/" + feature_path
  
          sample_info["feature_path"] = feature_path
          return sample_info
      # COCO Annotation DBs have corrext feature_path
      elif "COCO" not in sample_info["feature_path"]:
          sample_info["feature_path"] = sample_info[
              "image_path"
          ].replace(".jpg", ".npy")
      return sample_info
  
file_path: mmf/mmf/datasets/builders/textvqa/dataset.py
imports: import numpy
  import torch
  from mmf.common.sample import Sample
  from mmf.datasets.mmf_dataset import MMFDataset
  from mmf.utils.distributed import byte_tensor_to_object, object_to_byte_tensor
  from mmf.utils.text import word_tokenize
label_desc: Use image node_id 2 for calling the TextVQADataset obj's underlying member method code with example usage: obj.preprocess_sample_info(sample_info) and returns: sample_info, sample_info