code: def channel_convert(in_c, tar_type, img_list):
      # conversion among BGR, gray and y
      if in_c == 3 and tar_type == "gray":  # BGR to gray
          gray_list = [
              cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list
          ]
          return [np.expand_dims(img, axis=2) for img in gray_list]
      elif in_c == 3 and tar_type == "y":  # BGR to y
          y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
          return [np.expand_dims(img, axis=2) for img in y_list]
      elif in_c == 1 and tar_type == "RGB":  # gray/y to BGR
          return [
              cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list
          ]
      else:
          return img_list
  
imports: import math
  import os
  import random
  from datetime import datetime
  import cv2
  import numpy
  import torch
  from torchvision.utils import make_grid
label_desc: Use image node_id 42 for calling a global function with example usage: channel_convert(in_c, tar_type, img_list) and returns: unknown, unknown, unknown, img_list