code: def inception(
      self,
      name,
      input,
      channels,
      filter1,
      filter3R,
      filter3,
      filter5R,
      filter5,
      proj,
  ):
      conv1 = self.conv_layer(
          input=input,
          num_filters=filter1,
          filter_size=1,
          stride=1,
          act=None,
      )
      conv3r = self.conv_layer(
          input=input,
          num_filters=filter3R,
          filter_size=1,
          stride=1,
          act=None,
      )
      conv3 = self.conv_layer(
          input=conv3r,
          num_filters=filter3,
          filter_size=3,
          stride=1,
          act=None,
      )
      conv5r = self.conv_layer(
          input=input,
          num_filters=filter5R,
          filter_size=1,
          stride=1,
          act=None,
      )
      conv5 = self.conv_layer(
          input=conv5r,
          num_filters=filter5,
          filter_size=5,
          stride=1,
          act=None,
      )
      pool = fluid.layers.pool2d(
          input=input,
          pool_size=3,
          pool_stride=1,
          pool_padding=1,
          pool_type="max",
      )
      convprj = fluid.layers.conv2d(
          input=pool,
          filter_size=1,
          num_filters=proj,
          stride=1,
          padding=0,
      )
      cat = fluid.layers.concat(
          input=[conv1, conv3, conv5, convprj], axis=1
      )
      cat = fluid.layers.relu(cat)
      return cat
  
file_path: AdvBox/tutorials/image_classification/googlenet.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from builtins import object
  import paddle
  import paddle.fluid
label_desc: Use image node_id 4 for calling the GoogleNet obj's underlying member method code with example usage: obj.inception(name, input, channels, filter1, filter3R, filter3, filter5R, filter5, proj) and returns: cat