code: def ConvReluBN(incoming, num_filters, filter_size, name, is_training):
      """
      Convolution -> Batch normalization -> Relu
      :param incoming:
      :param num_filters:
      :param filter_size:
      :param name:
      :param is_training:
      :return:
      """
      num_filters_from = incoming.get_shape().as_list()[3]
      with tf.compat.v1.variable_scope(name):
          conv_W = var_random(
              "W",
              tuple(filter_size) + (num_filters_from, num_filters),
              regularizable=True,
          )
  
          after_conv = tf.nn.conv2d(
              incoming,
              filters=conv_W,
              strides=(1, 1, 1, 1),
              padding="SAME",
          )
  
          after_bn = batch_norm(after_conv, is_training)
  
          return tf.nn.relu(after_bn)
  
file_path: attention-ocr/aocr/model/cnn.py
imports: from __future__ import absolute_import
  import numpy
  import tensorflow
label_desc: Use image node_id 6 for calling a global function with example usage: ConvReluBN(incoming, num_filters, filter_size, name, is_training) and returns: tf