code: def bottleneck_block(self, input, num_filters, stride, name):
      conv0 = self.conv_bn_layer(
          input=input,
          num_filters=num_filters,
          filter_size=1,
          act="relu",
          name=name + "_branch2a",
      )
      conv1 = self.conv_bn_layer(
          input=conv0,
          num_filters=num_filters,
          filter_size=3,
          stride=stride,
          act="relu",
          name=name + "_branch2b",
      )
      conv2 = self.conv_bn_layer(
          input=conv1,
          num_filters=num_filters * 4,
          filter_size=1,
          act=None,
          name=name + "_branch2c",
      )
  
      short = self.shortcut(
          input, num_filters * 4, stride, name=name + "_branch1"
      )
  
      return fluid.layers.elementwise_add(x=short, y=conv2, act="relu")
  
file_path: PaddleHub/modules/video/classification/videotag_tsn_lstm/resource/models/tsn/tsn_res_model.py
imports: import os
  import time
  import sys
  import paddle.fluid
  import math
label_desc: Use image node_id 4 for calling the TSN_ResNet obj's underlying member method code with example usage: obj.bottleneck_block(input, num_filters, stride, name) and returns: fluid