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.
code: def _one_hot_encode(self, labels):
      # Create tensor of dim (num_classes).
      one_hot_encoded = torch.zeros(
          (self.num_classes), dtype=torch.long, device=labels.device
      )
  
      # Change 0 to 1 for all labels.
      one_hot_encoded = one_hot_encoded.scatter_(0, labels, 1)
  
      return one_hot_encoded
  
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 3 for calling the OneHotEncode obj's underlying member method code with example usage: obj._one_hot_encode(labels) and returns: one_hot_encoded