code: def adjust_ghsps(*args):
      global transformedImage
  
      if not currentImage is None:
          # reset the error label's text
          if lblError["text"] == "Error":
              lblError["text"] = ""
  
          transformedImage = caer.to_tensor(currentImage, cspace="rgb")
  
          # apply all transformations to currently displayed image
  
          if image_resized:
              transformedImage = caer.resize(
                  transformedImage,
                  target_size=(int(image_size[0]), int(image_size[1])),
                  preserve_aspect_ratio=False,
              )
  
          if hue.get() != 0.0:
              transformedImage = caer.transforms.adjust_hue(
                  transformedImage, hue.get()
              )
  
          if saturation.get() != 1.0:
              transformedImage = caer.transforms.adjust_saturation(
                  transformedImage, saturation.get()
              )
  
          if imgGamma.get() != 1.05:
              transformedImage = caer.transforms.adjust_gamma(
                  transformedImage, imgGamma.get()
              )
  
          if sharpen.get() != 8.9:
              transformedImage = caer.core.cv.filter2D(
                  transformedImage, -1, sharpenKernel
              )
  
          gb = gaussian_blur.get()
  
          if gb > 1:
              transformedImage = caer.core.cv.GaussianBlur(
                  transformedImage,
                  (gb + 1, gb + 1),
                  caer.core.cv.BORDER_DEFAULT,
              )
  
          if posterize.get() < 6:
              transformedImage = caer.transforms.posterize(
                  transformedImage, posterize.get()
              )
  
          if solarize.get() < 255:
              transformedImage = caer.transforms.solarize(
                  transformedImage, solarize.get()
              )
  
          if sobel_threshold.get() > 0:
              transformedImage = caer.core.cv.cvtColor(
                  transformedImage, caer.core.cv.COLOR_RGB2GRAY
              )
              sobelKernel = (
                  sobel_threshold.get()
                  if sobel_threshold.get() % 2 != 0
                  else sobel_threshold.get() + 1
              )  # values 1, 3 and 5
              dx = dy = (
                  sobel_threshold.get() - 2
                  if sobel_threshold.get() > 2
                  else sobel_threshold.get()
              )
              sobelx = caer.core.cv.Sobel(
                  transformedImage,
                  caer.core.cv.IMREAD_GRAYSCALE,
                  dx,
                  0,
                  ksize=sobelKernel,
              )
              sobely = caer.core.cv.Sobel(
                  transformedImage,
                  caer.core.cv.IMREAD_GRAYSCALE,
                  0,
                  dy,
                  ksize=sobelKernel,
              )
              transformedImage = caer.core.cv.bitwise_or(sobelx, sobely)
              transformedImage = caer.core.cv.cvtColor(
                  transformedImage, caer.core.cv.COLOR_GRAY2RGB
              )
  
          if show_edges.get() == 1:
              transformedImage = caer.core.cv.cvtColor(
                  transformedImage, caer.core.cv.COLOR_RGB2GRAY
              )
              transformedImage = caer.core.cv.Canny(
                  transformedImage,
                  low_threshold.get(),
                  low_threshold.get() * 2,
              )
              transformedImage = caer.core.cv.cvtColor(
                  transformedImage, caer.core.cv.COLOR_GRAY2RGB
              )
  
          if show_emboss.get() == 1:
              transformedImage = (
                  caer.core.cv.filter2D(
                      transformedImage, -1, embossKernel
                  )
                  + emboss.get()
              )
  
          if flip_H:
              transformedImage = caer.transforms.hflip(transformedImage)
  
          if flip_V:
              transformedImage = caer.transforms.vflip(transformedImage)
  
          if rotationApplied:
              show_rotated_image(True)
          else:
              image_show(transformedImage)
  
imports: import matplotlib.pyplot
  from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk
  from matplotlib.figure import Figure
  from tkinter import *
  from tkinter import filedialog
  import platform
  import math
  import caer
label_desc: Use image node_id 12 for calling a global function with example usage: adjust_ghsps() without return types