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","."]
docstr: Dictionary of value -> count.
docstr_tok: ["Dictionary","of","value","-",">","count","."]
code: def count(self):
      """Dictionary of value -> count."""
      if self._count is None:
          self._count = {}
          for val in self.uniques:
              self._count[val] = 0
          for val in self.data:
              self._count[val] += 1
      return self._count
  
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 11 for calling the DataAttribute obj's underlying member method code with example usage: obj.count() and returns: self