class_docstr: Convert an image from HSV to RGB.
  
  H channel values are assumed to be in the range 0..2pi. S and V are in the range 0..1.
  
  Returns:
      RGB version of the image.
  
  Shape:
      - image: :math:`(*, 3, H, W)`
      - output: :math:`(*, 3, H, W)`
  
  Example:
      >>> input = torch.rand(2, 3, 4, 5)
      >>> rgb = HsvToRgb()
      >>> output = rgb(input)  # 2x3x4x5
class_docstr_tok: ["Convert","an","image","from","HSV","to","RGB",".","H","channel","values","are","assumed","to","be","in","the","range","0","..","2pi",".","S","and","V","are","in","the","range","0","..","1",".","Returns",":","RGB","version","of","the","image",".","Shape",":","-","image",":",":","math",":","`","(","*",",","3",",","H",",","W",")","`","-","output",":",":","math",":","`","(","*",",","3",",","H",",","W",")","`","Example",":",">",">",">","input","=","torch.rand","(","2",",","3",",","4",",","5",")",">",">",">","rgb","=","HsvToRgb","(",")",">",">",">","output","=","rgb","(","input",")","#","2x3x4x5"]
code: def forward(self, image: torch.Tensor) -> torch.Tensor:
      return hsv_to_rgb(image)
  
file_path: kornia/kornia/color/hsv.py
imports: import math
  import torch
  from torch import nn
label_desc: Use image node_id 1 for calling the HsvToRgb obj's underlying member method code with example usage: obj.forward(image) and returns: hsv_to_rgb