code: def inceptionRB(x, f, k=7, scale=1.0, activation_fn=relu, scope=None):
      """Inception-ResNet-B (Fig. 11 and 17 in the v4 paper) and
      Inception-ResNet-C (Fig. 13 and 19 in the v4 paper)
      """
      conv1 = conv(x, f[0], 1, scope="1x1")
  
      conv2 = conv(x, f[1][0], 1, scope="%dx%d/r" % (k, k))
      conv2 = conv(conv2, f[1][1], (1, k), scope="%dx%d/1" % (k, k))
      conv2 = conv(conv2, f[1][2], (k, 1), scope="%dx%d/2" % (k, k))
  
      convs = concat([conv1, conv2], axis=3, name="concat")
      convs = conv2d(
          convs,
          f[2],
          1,
          biases_initializer=tf.zeros_initializer(),
          scope="linear",
      )
  
      if activation_fn is not None:
          x = activation_fn(x + scale * convs, name="out")
      else:
          x = add(x, scale * convs, name="out")
      return x
  
imports: from __future__ import absolute_import
  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 convrelu0
  from .layers import convbnrelu
  from .ops import *
  from .utils import pad_info
  from .utils import set_args
  from .utils import var_scope
label_desc: Use image node_id 20 for calling a global function with example usage: inceptionRB(x, f, k, scale, activation_fn, scope) and returns: x