class_docstr: Data attributes manage sample-wise information such as task or
  class labels.
  
  It provides access to unique values (`self.uniques`) and their indices
  (`self.val_to_idx`). Both fields are initialized lazily.
  
  Data attributes can be efficiently concatenated and subsampled.
class_docstr_tok: ["Data","attributes","manage","sample-wise","information","such","as","task","or","class","labels",".","It","provides","access","to","unique","values","(","`","self.uniques","`",")","and","their","indices","(","`","self.val_to_idx","`",")",".","Both","fields","are","initialized","lazily",".","Data","attributes","can","be","efficiently","concatenated","and","subsampled","."]
code: def __iter__(self):
      for i in range(len(self)):
          yield self[i]
  
imports: from typing import Dict, List, Optional, Sequence, Set, TypeVar, Union, overload
  import torch
  from .dataset_definitions import IDataset
  from .flat_data import ConstantSequence, FlatData
label_desc: Use image node_id 2 for calling the DataAttribute obj's underlying member method code with example usage: obj.__iter__() without return types