code: def create_cifar10_model(input, num_stack_layers, num_classes):
      c_map = [16, 32, 64]
  
      conv = conv_bn_relu(input, (3, 3), c_map[0])
      r1 = resnet_basic_stack(conv, num_stack_layers, c_map[0])
  
      r2_1 = resnet_basic_inc(r1, c_map[1])
      r2_2 = resnet_basic_stack(r2_1, num_stack_layers - 1, c_map[1])
  
      r3_1 = resnet_basic_inc(r2_2, c_map[2])
      r3_2 = resnet_basic_stack(r3_1, num_stack_layers - 1, c_map[2])
  
      # Global average pooling and output
      pool = AveragePooling(
          filter_shape=(8, 8), name="final_avg_pooling"
      )(r3_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 9 for calling a global function with example usage: create_cifar10_model(input, num_stack_layers, num_classes) and returns: z