code: def dilated_conv_one_pixel(
      center: (int, int),
      feature_map: np.ndarray,
      k: int = 3,
      r: int = 1,
      v: int = 1,
  ):
      """
      膨胀卷积核中心在指定坐标center处时，统计哪些像素被利用到，
      并在利用到的像素位置处加上增量v
      Args:
          center: 膨胀卷积核中心的坐标
          feature_map: 记录每个像素使用次数的特征图
          k: 膨胀卷积核的kernel大小
          r: 膨胀卷积的dilation rate
          v: 使用次数增量
      """
      assert divmod(3, 2)[1] == 1
  
      # left-top: (x, y)
      left_top = (
          center[0] - ((k - 1) // 2) * r,
          center[1] - ((k - 1) // 2) * r,
      )
      for i in range(k):
          for j in range(k):
              feature_map[left_top[1] + i * r][left_top[0] + j * r] += v
  
imports: import numpy
  import matplotlib.pyplot
  from matplotlib.colors import LinearSegmentedColormap
label_desc: Use image node_id 1 for calling a global function with example usage: dilated_conv_one_pixel(center, feature_map, k, r, v) without return types