class_docstr: Prepare labels for multi-output support. That is when a single example has
  more than one label. Currently all that this transform does is one-hot encodes
  the labels. This is because the Classy accuracy meters already expects the labels
  to be one-hot encoded when in a multi-output mode.
docstr: Args:
      num_classes (int): how many classes there are in total. Default 1000 for imagenet1k.
code: def __init__(self, num_classes: int = 1000):
      """
      Args:
          num_classes (int): how many classes there are in total. Default 1000 for imagenet1k.
      """
      super().__init__()
  
      self.num_classes = num_classes
  
file_path: vissl/vissl/data/ssl_transforms/one_hot_encode.py
imports: from typing import Dict, List, Union
  import torch
  from classy_vision.dataset.transforms import register_transform
  from classy_vision.dataset.transforms.classy_transform import ClassyTransform
label_desc: Use image node_id 1 to create a new OneHotEncode object from inherited base classes: ClassyTransform with example: obj = OneHotEncode(num_classes)