class_docstr: Base class for implementing a dataset. Inherits from PyTorch's Dataset class
  but adds some custom functionality on top. Processors mentioned in the
  configuration are automatically initialized for the end user.
  
  Args:
      dataset_name (str): Name of your dataset to be used a representative
          in text strings
      dataset_type (str): Type of your dataset. Normally, train|val|test
      config (DictConfig): Configuration for the current dataset
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docstr: Basically, __getitem__ of a torch dataset.
  
  Args:
      idx (int): Index of the sample to be loaded.
docstr_tok: ["Basically",",","__getitem__","of","a","torch","dataset",".","Args",":","idx","(","int",")",":","Index","of","the","sample","to","be","loaded","."]
code: def __getitem__(self, idx):
      """
      Basically, __getitem__ of a torch dataset.
  
      Args:
          idx (int): Index of the sample to be loaded.
      """
  
      raise NotImplementedError
  
file_path: mmf/mmf/datasets/base_dataset.py
imports: from mmf.common.registry import registry
  from mmf.common.sample import SampleList
  from mmf.utils.general import get_current_device
  from torch.utils.data.dataset import Dataset
label_desc: Use image node_id 3 for calling the BaseDataset obj's underlying member method code with example usage: obj.__getitem__(idx) without return types