code: def resnet_bottleneck(input, out_num_filters, inter_out_num_filters):
      c1 = conv_bn_relu(input, (1, 1), inter_out_num_filters)
      c2 = conv_bn_relu(c1, (3, 3), inter_out_num_filters)
      c3 = conv_bn(c2, (1, 1), out_num_filters, bn_init_scale=0)
      p = c3 + input
      return relu(p)
  
file_path: CNTK/Examples/Image/Classification/ResNet/Python/resnet_models.py
imports: import numpy
  from cntk.initializer import he_normal, normal
  from cntk.layers import AveragePooling, MaxPooling, BatchNormalization, Convolution, Dense
  from cntk.ops import element_times, relu
label_desc: Use image node_id 6 for calling a global function with example usage: resnet_bottleneck(input, out_num_filters, inter_out_num_filters) and returns: relu