code: def bottleneck_block(self, input, num_filters, stride):
      conv0 = self.conv_bn_layer(
          input=input,
          num_filters=num_filters,
          filter_size=1,
          act="relu",
      )
      conv1 = self.conv_bn_layer(
          input=conv0,
          num_filters=num_filters,
          filter_size=3,
          stride=stride,
          act="relu",
      )
      conv2 = self.conv_bn_layer(
          input=conv1,
          num_filters=num_filters * 4,
          filter_size=1,
          act=None,
      )
  
      short = self.shortcut(input, num_filters * 4, stride)
  
      return fluid.layers.elementwise_add(x=short, y=conv2, act="relu")
  
imports: from __future__ import division
  from builtins import range
  from builtins import object
  from past.utils import old_div
  import paddle.fluid
  import math
label_desc: Use image node_id 5 for calling the ResNet obj's underlying member method code with example usage: obj.bottleneck_block(input, num_filters, stride) and returns: fluid