code: def block17(net, scale=1.0, activation="relu"):
      tower_conv = relu(
          batch_normalization(
              conv_2d(
                  net,
                  192,
                  1,
                  bias=False,
                  activation=None,
                  name="Conv2d_1x1",
              )
          )
      )
      tower_conv_1_0 = relu(
          batch_normalization(
              conv_2d(
                  net,
                  128,
                  1,
                  bias=False,
                  activation=None,
                  name="Conv2d_0a_1x1",
              )
          )
      )
      tower_conv_1_1 = relu(
          batch_normalization(
              conv_2d(
                  tower_conv_1_0,
                  160,
                  [1, 7],
                  bias=False,
                  activation=None,
                  name="Conv2d_0b_1x7",
              )
          )
      )
      tower_conv_1_2 = relu(
          batch_normalization(
              conv_2d(
                  tower_conv_1_1,
                  192,
                  [7, 1],
                  bias=False,
                  activation=None,
                  name="Conv2d_0c_7x1",
              )
          )
      )
      tower_mixed = merge(
          [tower_conv, tower_conv_1_2], mode="concat", axis=3
      )
      tower_out = relu(
          batch_normalization(
              conv_2d(
                  tower_mixed,
                  net.get_shape()[3],
                  1,
                  bias=False,
                  activation=None,
                  name="Conv2d_1x1",
              )
          )
      )
      net += scale * tower_out
      if activation:
          if isinstance(activation, str):
              net = activations.get(activation)(net)
          elif hasattr(activation, "__call__"):
              net = activation(net)
          else:
              raise ValueError("Invalid Activation.")
      return net
  
file_path: tflearn/examples/images/inception_resnet_v2.py
imports: from __future__ import absolute_import, division, print_function
  import tflearn
  import tflearn.activations
  import tflearn.datasets.oxflower17
  from tflearn.activations import relu
  from tflearn.data_utils import shuffle, to_categorical
  from tflearn.layers.conv import avg_pool_2d, conv_2d, max_pool_2d
  from tflearn.layers.core import dropout, flatten, fully_connected, input_data
  from tflearn.layers.merge_ops import merge
  from tflearn.layers.normalization import batch_normalization
  from tflearn.utils import repeat
label_desc: Use image node_id 2 for calling a global function with example usage: block17(net, scale, activation) and returns: net