code: def reorg_python(a_np, stride):
      """Reorg operator
  
      Parameters
      ----------
      a_np : numpy.ndarray
          4-D with shape [batch, in_channel, in_height, in_width]
  
      stride : int
          Stride size
  
      Returns
      -------
      b_np : np.ndarray
          4-D with shape [batch, out_channel, out_height, out_width]
      """
  
      batch, in_channel, in_height, in_width = a_np.shape
      a_np = np.reshape(a_np, batch * in_channel * in_height * in_width)
      out_c = int(in_channel / (stride * stride))
      out_channel = in_channel * stride * stride
      out_height = int(in_height / stride)
      out_width = int(in_width / stride)
      b_np = np.zeros(batch * out_channel * out_height * out_width)
      cnt = 0
      for b in range(batch):
          for k in range(in_channel):
              for j in range(in_height):
                  for i in range(in_width):
                      c2 = k % out_c
                      offset = int(k / out_c)
                      w2 = int(i * stride + offset % stride)
                      h2 = int(j * stride + offset / stride)
                      out_index = int(
                          w2
                          + in_width
                          * stride
                          * (h2 + in_height * stride * (c2 + out_c * b))
                      )
                      b_np[cnt] = a_np[int(out_index)]
                      cnt = cnt + 1
      b_np = np.reshape(
          b_np, (batch, out_channel, out_height, out_width)
      )
      return b_np
  
label_desc: Use image node_id 1 for calling a global function with example usage: reorg_python(a_np, stride) and returns: b_np