code: def lab_to_rgb(
      image: torch.Tensor, clip: bool = True
  ) -> torch.Tensor:
      r"""Convert a Lab image to RGB.
  
      The L channel is assumed to be in the range of :math:`[0, 100]`.
      a and b channels are in the range of :math:`[-128, 127]`.
  
      Args:
          image: Lab image to be converted to RGB with shape :math:`(*, 3, H, W)`.
          clip: Whether to apply clipping to insure output RGB values in range :math:`[0, 1]`.
  
      Returns:
          Lab version of the image with shape :math:`(*, 3, H, W)`.
          The output RGB image are in the range of :math:`[0, 1]`.
  
      Example:
          >>> input = torch.rand(2, 3, 4, 5)
          >>> output = lab_to_rgb(input)  # 2x3x4x5
      """
      if not isinstance(image, torch.Tensor):
          raise TypeError(
              f"Input type is not a torch.Tensor. Got {type(image)}"
          )
  
      if len(image.shape) < 3 or image.shape[-3] != 3:
          raise ValueError(
              f"Input size must have a shape of (*, 3, H, W). Got {image.shape}"
          )
  
      L: torch.Tensor = image[..., 0, :, :]
      a: torch.Tensor = image[..., 1, :, :]
      _b: torch.Tensor = image[..., 2, :, :]
  
      fy = (L + 16.0) / 116.0
      fx = (a / 500.0) + fy
      fz = fy - (_b / 200.0)
  
      # if color data out of range: Z < 0
      fz = fz.clamp(min=0.0)
  
      fxyz = torch.stack([fx, fy, fz], dim=-3)
  
      # Convert from Lab to XYZ
      power = torch.pow(fxyz, 3.0)
      scale = (fxyz - 4.0 / 29.0) / 7.787
      xyz = torch.where(fxyz > 0.2068966, power, scale)
  
      # For D65 white point
      xyz_ref_white = torch.tensor(
          [0.95047, 1.0, 1.08883], device=xyz.device, dtype=xyz.dtype
      )[..., :, None, None]
      xyz_im = xyz * xyz_ref_white
  
      rgbs_im: torch.Tensor = xyz_to_rgb(xyz_im)
  
      # https://github.com/richzhang/colorization-pytorch/blob/66a1cb2e5258f7c8f374f582acc8b1ef99c13c27/util/util.py#L107
      #     rgbs_im = torch.where(rgbs_im < 0, torch.zeros_like(rgbs_im), rgbs_im)
  
      # Convert from RGB Linear to sRGB
      rgb_im = linear_rgb_to_rgb(rgbs_im)
  
      # Clip to 0,1 https://www.w3.org/Graphics/Color/srgb
      if clip:
          rgb_im = torch.clamp(rgb_im, min=0.0, max=1.0)
  
      return rgb_im
  
file_path: kornia/kornia/color/lab.py
imports: import torch
  from torch import nn
  from .rgb import linear_rgb_to_rgb, rgb_to_linear_rgb
  from .xyz import rgb_to_xyz, xyz_to_rgb
label_desc: Use image node_id 2 for calling a global function with example usage: lab_to_rgb(image, clip) and returns: rgb_im