code: def make_bilinear_weights(size, num_channels):
      factor = (size + 1) // 2
      if size % 2 == 1:
          center = factor - 1
      else:
          center = factor - 0.5
      og = np.ogrid[:size, :size]
      filt = (1 - abs(og[0] - center) / factor) * (
          1 - abs(og[1] - center) / factor
      )
      filt = torch.from_numpy(filt)
      w = torch.zeros(num_channels, num_channels, size, size)
      for i in range(num_channels):
          w[i, i] = filt
      return w
  
imports: from ..None import resnext101_64x4d
  from math import floor
  import numpy
  import torch
  import torch.nn
label_desc: Use image node_id 3 for calling the _DeconvModule obj's underlying member method code with example usage: obj.make_bilinear_weights(size, num_channels) and returns: w