code: def _tf_padding(x, ksize, stride, tf_padding):
      pad_2 = _get_pad(x.shape[2], ksize[0], stride[0], tf_padding)
      pad_3 = _get_pad(x.shape[3], ksize[1], stride[1], tf_padding)
      if pad_2 or pad_3:
          return pad(
              x,
              (
                  (0, 0),
                  (0, 0),
                  (pad_2 // 2, int(np.ceil(float(pad_2) / 2))),
                  (pad_3 // 2, int(np.ceil(float(pad_3) / 2))),
              ),
              mode="constant",
          )
      else:
          return x
  
file_path: chainercv/chainercv/links/model/mobilenet/tf_convolution_2d.py
imports: import numpy
  import chainer
  from chainer.functions import pad
  from chainer.links import Convolution2D
  from chainer.utils import conv
label_desc: Use image node_id 3 for calling a global function with example usage: _tf_padding(x, ksize, stride, tf_padding) and returns: pad, x