code: def gaussian(img, pt, sigma):
      # Draw a 2D gaussian
      assert sigma >= 0
      if sigma == 0:
          img[pt[1], pt[0]] = 1.0
          return True
      # assert pt[0]<=img.shape[1]
      # assert pt[1]<=img.shape[0]
  
      # Check that any part of the gaussian is in-bounds
      ul = [int(pt[0] - 3 * sigma), int(pt[1] - 3 * sigma)]
      br = [int(pt[0] + 3 * sigma + 1), int(pt[1] + 3 * sigma + 1)]
      if (
          ul[0] > img.shape[1]
          or ul[1] >= img.shape[0]
          or br[0] < 0
          or br[1] < 0
      ):
          # If not, just return the image as is
          # print('gaussian error')
          return False
          # return img
  
      # Generate gaussian
      size = 6 * sigma + 1
      x = np.arange(0, size, 1, float)
      y = x[:, np.newaxis]
      x0 = y0 = size // 2
      # The gaussian is not normalized, we want the center value to equal 1
      g = np.exp(-((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma**2))
  
      # Usable gaussian range
      g_x = max(0, -ul[0]), min(br[0], img.shape[1]) - ul[0]
      g_y = max(0, -ul[1]), min(br[1], img.shape[0]) - ul[1]
      # Image range
      img_x = max(0, ul[0]), min(br[0], img.shape[1])
      img_y = max(0, ul[1]), min(br[1], img.shape[0])
  
      img[img_y[0] : img_y[1], img_x[0] : img_x[1]] = g[
          g_y[0] : g_y[1], g_x[0] : g_x[1]
      ]
      return True
  
file_path: insightface/alignment/heatmap/img_helper.py
imports: import numpy
  import math
  import cv2
  from skimage import transform
label_desc: Use image node_id 3 for calling a global function with example usage: gaussian(img, pt, sigma) and returns: True, True, False