code: def yuv_to_rgb(image: torch.Tensor) -> torch.Tensor:
      r"""Convert an YUV image to RGB.
  
      The image data is assumed to be in the range of (0, 1) for luma and (-0.5, 0.5) for chroma.
  
      Args:
          image: YUV Image to be converted to RGB with shape :math:`(*, 3, H, W)`.
  
      Returns:
          RGB version of the image with shape :math:`(*, 3, H, W)`.
  
      Example:
          >>> input = torch.rand(2, 3, 4, 5)
          >>> output = yuv_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}"
          )
  
      y: torch.Tensor = image[..., 0, :, :]
      u: torch.Tensor = image[..., 1, :, :]
      v: torch.Tensor = image[..., 2, :, :]
  
      r: torch.Tensor = y + 1.14 * v  # coefficient for g is 0
      g: torch.Tensor = y + -0.396 * u - 0.581 * v
      b: torch.Tensor = y + 2.029 * u  # coefficient for b is 0
  
      out: torch.Tensor = torch.stack([r, g, b], -3)
  
      return out
  
imports: from typing import Tuple
  import torch
  from torch import nn
label_desc: Use image node_id 4 for calling a global function with example usage: yuv_to_rgb(image) and returns: out