code: def _make_colorwheel() -> torch.Tensor:
      """
      Generates a color wheel for optical flow visualization as presented in:
      Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007)
      URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf.
  
      Returns:
          colorwheel (Tensor[55, 3]): Colorwheel Tensor.
      """
  
      RY = 15
      YG = 6
      GC = 4
      CB = 11
      BM = 13
      MR = 6
  
      ncols = RY + YG + GC + CB + BM + MR
      colorwheel = torch.zeros((ncols, 3))
      col = 0
  
      # RY
      colorwheel[0:RY, 0] = 255
      colorwheel[0:RY, 1] = torch.floor(255 * torch.arange(0, RY) / RY)
      col = col + RY
      # YG
      colorwheel[col : col + YG, 0] = 255 - torch.floor(
          255 * torch.arange(0, YG) / YG
      )
      colorwheel[col : col + YG, 1] = 255
      col = col + YG
      # GC
      colorwheel[col : col + GC, 1] = 255
      colorwheel[col : col + GC, 2] = torch.floor(
          255 * torch.arange(0, GC) / GC
      )
      col = col + GC
      # CB
      colorwheel[col : col + CB, 1] = 255 - torch.floor(
          255 * torch.arange(CB) / CB
      )
      colorwheel[col : col + CB, 2] = 255
      col = col + CB
      # BM
      colorwheel[col : col + BM, 2] = 255
      colorwheel[col : col + BM, 0] = torch.floor(
          255 * torch.arange(0, BM) / BM
      )
      col = col + BM
      # MR
      colorwheel[col : col + MR, 2] = 255 - torch.floor(
          255 * torch.arange(MR) / MR
      )
      colorwheel[col : col + MR, 0] = 255
      return colorwheel
  
imports: import collections
  import math
  import pathlib
  import warnings
  from itertools import repeat
  from types import FunctionType
  from typing import Any, BinaryIO, List, Optional, Tuple, Union
  import numpy
  import torch
  from PIL import Image, ImageColor, ImageDraw, ImageFont
label_desc: Use image node_id 8 for calling a global function with example usage: _make_colorwheel() and returns: colorwheel