class MaxPool2:
  # ...

  def iterate_regions(self, image):
    '''
    Generates non-overlapping 2x2 image regions to pool over.
    - image is a 2d numpy array
    '''
    h, w, _ = image.shape
    new_h = h // 2
    new_w = w // 2

    for i in range(new_h):
      for j in range(new_w):
        im_region = image[(i * 2):(i * 2 + 2), (j * 2):(j * 2 + 2)]
        yield im_region, i, j

  def backprop(self, d_L_d_out):
    '''
    Performs a backward pass of the maxpool layer.
    Returns the loss gradient for this layer's inputs.
    - d_L_d_out is the loss gradient for this layer's outputs.
    '''
    d_L_d_input = np.zeros(self.last_input.shape)

    for im_region, i, j in self.iterate_regions(self.last_input):
      h, w, f = im_region.shape
      amax = np.amax(im_region, axis=(0, 1))

      for i2 in range(h):
        for j2 in range(w):
          for f2 in range(f):
            # If this pixel was the max value, copy the gradient to it.
            if im_region[i2, j2, f2] == amax[f2]:
              d_L_d_input[i * 2 + i2, j * 2 + j2, f2] = d_L_d_out[i, j, f2]

    return d_L_d_input