code: def resnet_basic_inc(input, num_filters, strides=(2, 2)):
      c1 = conv_bn_relu(input, (3, 3), num_filters, strides)
      c2 = conv_bn(c1, (3, 3), num_filters, bn_init_scale=1)
      s = conv_bn(input, (1, 1), num_filters, strides)  # Shortcut
      p = c2 + s
      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 4 for calling a global function with example usage: resnet_basic_inc(input, num_filters, strides) and returns: relu