class_docstr: Convert a PIL image to LAB tensor of shape C x H x W
  This transform was proposed in Colorization - https://arxiv.org/abs/1603.08511
  
  The input image is PIL Image. We first convert it to tensor
  HWC which has channel order RGB. We then convert the RGB to BGR
  and use OpenCV to convert the image to LAB. The LAB image is
  8-bit image in range > L [0, 255], A [0, 255], B [0, 255]. We
  rescale it to: L [0, 100], A [-128, 127], B [-128, 127]
  
  The output is image torch tensor.
code: def __call__(self, image):
      img_tensor = np.array(image)
      # PIL image tensor is RGB. Convert to BGR
      img_bgr = img_tensor[:, :, ::-1]
      img_lab = self._convertbgr2lab(img_bgr.astype(np.uint8))
      # convert HWC -> CHW. The image is LAB.
      img_lab = np.transpose(img_lab, (2, 0, 1))
      # torch tensor output
      img_lab_tensor = torch.from_numpy(img_lab).float()
      return img_lab_tensor
  
file_path: vissl/vissl/data/ssl_transforms/img_pil_to_lab_tensor.py
imports: from typing import Any, Dict
  import numpy
  import torch
  from classy_vision.dataset.transforms import register_transform
  from classy_vision.dataset.transforms.classy_transform import ClassyTransform
  from vissl.utils.misc import is_opencv_available
label_desc: Use image node_id 2 for calling the ImgPil2LabTensor obj's underlying member method code with example usage: obj.__call__(image) and returns: img_lab_tensor