code: def resnet_bottleneck_stack(
      input, num_stack_layers, out_num_filters, inter_out_num_filters
  ):
      assert num_stack_layers >= 0
      l = input
      for _ in range(num_stack_layers):
          l = resnet_bottleneck(
              l, out_num_filters, inter_out_num_filters
          )
      return l
  
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 8 for calling a global function with example usage: resnet_bottleneck_stack(input, num_stack_layers, out_num_filters, inter_out_num_filters) and returns: l