class_docstr: An instance of PyTorch VisionData the represents the COCO128-segments dataset.
  
  Parameters
  ----------
  root : str
      Path to the root directory of the dataset.
  name : str
      Name of the dataset.
  train : bool
      if `True` train dataset, otherwise test dataset
  transforms : Callable, optional
      A function/transform that takes in an PIL image and returns a transformed version.
      E.g, transforms.RandomCrop
class_docstr_tok: ["An","instance","of","PyTorch","VisionData","the","represents","the","COCO128-segments","dataset",".","Parameters","--","--","--","--","--","root",":","str","Path","to","the","root","directory","of","the","dataset",".","name",":","str","Name","of","the","dataset",".","train",":","bool","if","`","True","`","train","dataset",",","otherwise","test","dataset","transforms",":","Callable",",","optional","A","function\/transform","that","takes","in","an","PIL","image","and","returns","a","transformed","version",".","E.g",",","transforms.RandomCrop"]
code: def __len__(self):
      return len(self.images)
  
imports: import contextlib
  import os
  import typing
  from pathlib import Path
  import albumentations
  import matplotlib.pyplot
  import numpy
  import torch
  import torchvision.transforms.functional
  from albumentations.pytorch.transforms import ToTensorV2
  from PIL import Image, ImageDraw
  from torch.utils.data import DataLoader
  from torchvision.datasets import VisionDataset
  from torchvision.datasets.utils import download_and_extract_archive
  from torchvision.utils import draw_segmentation_masks
  from deepchecks.vision import VisionData, BatchOutputFormat
  from deepchecks.vision.checks import ImagePropertyDrift
  from deepchecks.vision.checks import LabelDrift
label_desc: Use image node_id 3 for calling the CocoInstanceSegmentationDataset obj's underlying member method code with example usage: obj.__len__() and returns: len