code: def compute_pyramid_patch_weight_loss(
      width: int, height: int
  ) -> np.ndarray:
      """Compute a weight matrix that assigns bigger weight on pixels in center and
      less weight to pixels on image boundary.
      This weight matrix then used for merging individual tile predictions and helps dealing
      with prediction artifacts on tile boundaries.
  
      :param width: Tile width
      :param height: Tile height
      :return: Since-channel image [Width x Height]
      """
      xc = width * 0.5
      yc = height * 0.5
      xl = 0
      xr = width
      yb = 0
      yt = height
      Dc = np.zeros((width, height))
      De = np.zeros((width, height))
  
      Dcx = np.square(np.arange(width) - xc + 0.5)
      Dcy = np.square(np.arange(height) - yc + 0.5)
      Dc = np.sqrt(Dcx[np.newaxis].transpose() + Dcy)
  
      De_l = np.square(np.arange(width) - xl + 0.5) + np.square(0.5)
      De_r = np.square(np.arange(width) - xr + 0.5) + np.square(0.5)
      De_b = np.square(0.5) + np.square(np.arange(height) - yb + 0.5)
      De_t = np.square(0.5) + np.square(np.arange(height) - yt + 0.5)
  
      De_x = np.sqrt(np.minimum(De_l, De_r))
      De_y = np.sqrt(np.minimum(De_b, De_t))
      De = np.minimum(De_x[np.newaxis].transpose(), De_y)
  
      alpha = (width * height) / np.sum(np.divide(De, np.add(Dc, De)))
      W = alpha * np.divide(De, np.add(Dc, De))
      return W, Dc, De
  
imports: import dataclasses
  import math
  from typing import List, Iterable, Tuple, Union, Sequence
  import cv2
  import numpy
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: compute_pyramid_patch_weight_loss(width, height) and returns: W,  Dc,  De