code: def CaffeBilinearUpSample(x, shape):
      """
      Deterministic bilinearly-upsample the input images.
      It is implemented by deconvolution with "BilinearFiller" in Caffe.
      It is aimed to mimic caffe behavior.
  
      Args:
          x (tf.Tensor): a NCHW tensor
          shape (int): the upsample factor
  
      Returns:
          tf.Tensor: a NCHW tensor.
      """
      inp_shape = x.shape.as_list()
      ch = inp_shape[1]
      assert ch == 1, "This layer only works for channel=1"
      # for a version that supports >1 channels, see:
      # https://github.com/tensorpack/tensorpack/issues/1040#issuecomment-452798180
  
      shape = int(shape)
      filter_shape = 2 * shape
  
      def bilinear_conv_filler(s):
          """
          s: width, height of the conv filter
          https://github.com/BVLC/caffe/blob/99bd99795dcdf0b1d3086a8d67ab1782a8a08383/include/caffe/filler.hpp#L219-L268
          """
          f = np.ceil(float(s) / 2)
          c = float(2 * f - 1 - f % 2) / (2 * f)
          ret = np.zeros((s, s), dtype="float32")
          for x in range(s):
              for y in range(s):
                  ret[x, y] = (1 - abs(x / f - c)) * (
                      1 - abs(y / f - c)
                  )
          return ret
  
      w = bilinear_conv_filler(filter_shape)
      w = np.repeat(w, ch * ch).reshape(
          (filter_shape, filter_shape, ch, ch)
      )
  
      weight_var = tf.constant(
          w,
          tf.float32,
          shape=(filter_shape, filter_shape, ch, ch),
          name="bilinear_upsample_filter",
      )
      x = tf.pad(
          x,
          [
              [0, 0],
              [0, 0],
              [shape - 1, shape - 1],
              [shape - 1, shape - 1],
          ],
          mode="SYMMETRIC",
      )
      out_shape = tf.shape(x) * tf.constant(
          [1, 1, shape, shape], tf.int32
      )
      deconv = tf.nn.conv2d_transpose(
          x,
          weight_var,
          out_shape,
          [1, 1, shape, shape],
          "SAME",
          data_format="NCHW",
      )
      edge = shape * (shape - 1)
      deconv = deconv[:, :, edge:-edge, edge:-edge]
  
      if inp_shape[2]:
          inp_shape[2] *= shape
      if inp_shape[3]:
          inp_shape[3] *= shape
      deconv.set_shape(inp_shape)
      return deconv
  
imports: import argparse
  import numpy
  import os
  import cv2
  import tensorflow
  from tensorpack import *
  from tensorpack.dataflow import dataset
  from tensorpack.tfutils import gradproc, optimizer
  from tensorpack.tfutils.summary import add_moving_summary, add_param_summary
  from tensorpack.utils.gpu import get_num_gpu
  from tensorpack.utils import logger
label_desc: Use image node_id 2 for calling a global function with example usage: CaffeBilinearUpSample(x, shape) and returns: deconv, ret