code: def adjust(p, x, filters, scope=None):
      if p is None:
          p = x
      elif int(p.shape[1]) != int(x.shape[1]):
          p = relu(p, name="relu")
          p1 = avg_pool2d(
              p, 1, stride=2, padding="VALID", scope="pool1"
          )
          p1 = conv2d(p1, int(filters / 2), 1, scope="conv1")
          p2 = pad(p, [[0, 0], [0, 1], [0, 1], [0, 0]])[:, 1:, 1:, :]
          p2 = avg_pool2d(
              p2, 1, stride=2, padding="VALID", scope="pool2"
          )
          p2 = conv2d(p2, int(filters / 2), 1, scope="conv2")
          p = concat([p1, p2], axis=3, name="concat")
          p = batch_norm(p, scope="bn")
      elif int(p.shape[-1]) != filters:
          p = relu(p, name="relu")
          p = conv(p, filters, 1, scope="prev_1x1")
      return p
  
imports: from __future__ import absolute_import
  from __future__ import division
  import tensorflow
  from .layers import avg_pool2d
  from .layers import batch_norm
  from .layers import conv2d
  from .layers import dropout
  from .layers import fc
  from .layers import max_pool2d
  from .layers import sconv2d
  from .layers import convbn
  from .layers import sconvbn
  from .ops import *
  from .utils import pad_info
  from .utils import set_args
  from .utils import var_scope
label_desc: Use image node_id 8 for calling a global function with example usage: adjust(p, x, filters, scope) and returns: p