code: def create_imagenet_model_bottleneck(
      input, num_stack_layers, num_classes, stride1x1, stride3x3
  ):
      c_map = [64, 128, 256, 512, 1024, 2048]
  
      # conv1 and max pooling
      conv1 = conv_bn_relu(input, (7, 7), c_map[0], strides=(2, 2))
      pool1 = MaxPooling((3, 3), strides=(2, 2), pad=True)(conv1)
  
      # conv2_x
      r2_1 = resnet_bottleneck_inc(
          pool1, c_map[2], c_map[0], (1, 1), (1, 1)
      )
      r2_2 = resnet_bottleneck_stack(
          r2_1, num_stack_layers[0], c_map[2], c_map[0]
      )
  
      # conv3_x
      r3_1 = resnet_bottleneck_inc(
          r2_2, c_map[3], c_map[1], stride1x1, stride3x3
      )
      r3_2 = resnet_bottleneck_stack(
          r3_1, num_stack_layers[1], c_map[3], c_map[1]
      )
  
      # conv4_x
      r4_1 = resnet_bottleneck_inc(
          r3_2, c_map[4], c_map[2], stride1x1, stride3x3
      )
      r4_2 = resnet_bottleneck_stack(
          r4_1, num_stack_layers[2], c_map[4], c_map[2]
      )
  
      # conv5_x
      r5_1 = resnet_bottleneck_inc(
          r4_2, c_map[5], c_map[3], stride1x1, stride3x3
      )
      r5_2 = resnet_bottleneck_stack(
          r5_1, num_stack_layers[3], c_map[5], c_map[3]
      )
  
      # Global average pooling and output
      pool = AveragePooling(
          filter_shape=(7, 7), name="final_avg_pooling"
      )(r5_2)
      z = Dense(num_classes, init=normal(0.01))(pool)
      return z
  
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 11 for calling a global function with example usage: create_imagenet_model_bottleneck(input, num_stack_layers, num_classes, stride1x1, stride3x3) and returns: z