class_docstr: A structure for storing masks and their related data in batched format.
  Implements basic filtering and concatenation.
class_docstr_tok: ["A","structure","for","storing","masks","and","their","related","data","in","batched","format",".","Implements","basic","filtering","and","concatenation","."]
code: def __init__(self, **kwargs) -> None:
      for v in kwargs.values():
          assert isinstance(
              v, (list, np.ndarray, torch.Tensor)
          ), "MaskData only supports list, numpy arrays, and torch tensors."
      self._stats = dict(**kwargs)
  
imports: import numpy
  import torch
  import math
  from copy import deepcopy
  from itertools import product
  from typing import Any, Dict, Generator, ItemsView, List, Tuple
label_desc: Use image node_id 1 to create a new MaskData object  with example: obj = MaskData()