class_docstr: Pascal VOC Semantic Segmentation Dataset.
  
  Parameters
  ----------
  root : string
      Path to VOCdevkit folder. Default is '$(HOME)/mxnet/datasets/voc'
  split: string
      'train', 'val' or 'test'
  transform : callable, optional
      A function that transforms the image
  
  Examples
  --------
  >>> from mxnet.gluon.data.vision import transforms
  >>> # Transforms for Normalization
  >>> input_transform = transforms.Compose([
  >>>     transforms.ToTensor(),
  >>>     transforms.Normalize([.485, .456, .406], [.229, .224, .225]),
  >>> ])
  >>> # Create Dataset
  >>> trainset = gluoncv.data.VOCSegmentation(split='train', transform=input_transform)
  >>> # Create Training Loader
  >>> train_data = gluon.data.DataLoader(
  >>>     trainset, 4, shuffle=True, last_batch='rollover',
  >>>     num_workers=4)
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code: def __len__(self):
      return len(self.images)
  
file_path: gluon-cv/gluoncv/data/pascal_voc/segmentation.py
imports: import os
  import numpy
  from PIL import Image
  from mxnet import cpu
  import mxnet.ndarray
  from ..segbase import SegmentationDataset
label_desc: Use image node_id 3 for calling the VOCSegmentation obj's underlying member method code with example usage: obj.__len__() and returns: len