code: def inceptionRA(x, f, scale=1.0, scope=None):
      """Inception-ResNet-A (Fig. 10 and 16 in the v4 paper)"""
      conv1 = conv(x, f[0], 1, scope="1x1")
  
      conv2 = conv(x, f[1], 1, scope="3x3/r")
      conv2 = conv(conv2, f[1], 3, scope="3x3/1")
  
      conv3 = conv(x, f[2][0], 1, scope="d3x3/r")
      conv3 = conv(conv3, f[2][1], 3, scope="d3x3/1")
      conv3 = conv(conv3, f[2][2], 3, scope="d3x3/2")
  
      convs = concat([conv1, conv2, conv3], axis=3, name="concat")
      convs = conv2d(
          convs,
          f[3],
          1,
          biases_initializer=tf.zeros_initializer(),
          scope="linear",
      )
  
      x = relu(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 19 for calling a global function with example usage: inceptionRA(x, f, scale, scope) and returns: x