code: def resnet_bottleneck_inc(
      input,
      out_num_filters,
      inter_out_num_filters,
      stride1x1,
      stride3x3,
  ):
      c1 = conv_bn_relu(
          input, (1, 1), inter_out_num_filters, strides=stride1x1
      )
      c2 = conv_bn_relu(
          c1, (3, 3), inter_out_num_filters, strides=stride3x3
      )
      c3 = conv_bn(c2, (1, 1), out_num_filters, bn_init_scale=0)
      stride = np.multiply(stride1x1, stride3x3)
      s = conv_bn(
          input, (1, 1), out_num_filters, strides=stride
      )  # Shortcut
      p = c3 + 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 7 for calling a global function with example usage: resnet_bottleneck_inc(input, out_num_filters, inter_out_num_filters, stride1x1, stride3x3) and returns: relu