class_docstr: Convert an image from YUV to RGB.
  
  The image data is assumed to be in the range of (0, 1) for luma and (-0.5, 0.5) for chroma.
  Width and Height evenly divisible by 2.
  
  Returns:
      RGB version of the image.
  
  Shape:
      - imagey: :math:`(*, 1, H, W)`
      - imageuv: :math:`(*, 2, H/2, W/2)`
      - output: :math:`(*, 3, H, W)`
  
  Examples:
      >>> inputy = torch.rand(2, 1, 4, 6)
      >>> inputuv = torch.rand(2, 2, 2, 3)
      >>> rgb = Yuv420ToRgb()
      >>> output = rgb(inputy, inputuv)  # 2x3x4x6
class_docstr_tok: ["Convert","an","image","from","YUV","to","RGB",".","The","image","data","is","assumed","to","be","in","the","range","of","(","0",",","1",")","for","luma","and","(","-0.5",",","0.5",")","for","chroma",".","Width","and","Height","evenly","divisible","by","2",".","Returns",":","RGB","version","of","the","image",".","Shape",":","-","imagey",":",":","math",":","`","(","*",",","1",",","H",",","W",")","`","-","imageuv",":",":","math",":","`","(","*",",","2",",","H\/2",",","W\/2",")","`","-","output",":",":","math",":","`","(","*",",","3",",","H",",","W",")","`","Examples",":",">",">",">","inputy","=","torch.rand","(","2",",","1",",","4",",","6",")",">",">",">","inputuv","=","torch.rand","(","2",",","2",",","2",",","3",")",">",">",">","rgb","=","Yuv420ToRgb","(",")",">",">",">","output","=","rgb","(","inputy",",","inputuv",")","#","2x3x4x6"]
code: def forward(
      self, inputy: torch.Tensor, inputuv: torch.Tensor
  ) -> torch.Tensor:  # skipcq: PYL-R0201
      return yuv420_to_rgb(inputy, inputuv)
  
imports: from typing import Tuple
  import torch
  from torch import nn
label_desc: Use image node_id 1 for calling the Yuv420ToRgb obj's underlying member method code with example usage: obj.forward(inputy, inputuv) and returns: yuv420_to_rgb