code: def __init__(self, files, metadata=None, loader=default_loader):
      """
      metadata (List[Dict[Type]] or List[Type], Optional):
          metadata to be added to each sample.
          The Type can be anything that pytorch default_collate can handle.
          If Type is tensor, make sure that the tensors are of same dimension.
      """
      if metadata is not None:
          assert isinstance(metadata, list), "metadata should be a list"
          assert len(files) == len(metadata)
          assert len(files) > 0, "Empty ListDataset is not allowed"
          if not isinstance(metadata[0], dict):
              metadata = [{"target": target} for target in metadata]
      self.files = files
      self.metadata = metadata
      self.loader = loader
  
imports: from torchvision.datasets.folder import default_loader
label_desc: Use image node_id 1 to create a new ListDataset object  with example: obj = ListDataset(files, metadata, loader)