class_docstr: This dataset is useful for external open source dataset which
  usually have annotation files, images and features (which we generate).
  The dataset takes care of creating annotation db, features db and image db
  if the configuration follows a set format. Also, you can optionally enable
  image or features. The class has a resources method which can be overridden
  to download data. More details to come.
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code: def __init__(
      self,
      dataset_name,
      config,
      dataset_type="train",
      index=0,
      annotation_database=AnnotationDatabase,
      *args,
      **kwargs,
  ):
      super().__init__(
          dataset_name, config, dataset_type, *args, **kwargs
      )
      self._index = index
      self.annotation_database = annotation_database
      self.annotation_db = self.build_annotation_db()
      self._use_images = self.config.get("use_images", False)
      if self._use_images:
          self.image_db = self.build_image_db()
  
      self._use_features = self.config.get("use_features", False)
      if self._use_features:
          self.features_db = self.build_features_db()
  
file_path: mmf/mmf/datasets/mmf_dataset.py
imports: import collections
  import os
  from mmf.datasets.base_dataset import BaseDataset
  from mmf.datasets.databases.annotation_database import AnnotationDatabase
  from mmf.datasets.databases.features_database import FeaturesDatabase
  from mmf.datasets.databases.image_database import ImageDatabase
label_desc: Use image node_id 1 to create a new MMFDataset object from inherited base classes: BaseDataset with example: obj = MMFDataset(dataset_name, config, dataset_type, index, annotation_database)