code: def _InceptionModule(
      model,
      input_blob,
      input_depth,
      output_name,
      conv1_depth,
      conv3_depths,
      conv5_depths,
      pool_depth,
  ):
      # path 1: 1x1 conv
      conv1 = brew.conv(
          model,
          input_blob,
          output_name + ":conv1",
          input_depth,
          conv1_depth,
          1,
          ("XavierFill", {}),
          ("ConstantFill", {}),
      )
      conv1 = brew.relu(model, conv1, conv1)
      # path 2: 1x1 conv + 3x3 conv
      conv3_reduce = brew.conv(
          model,
          input_blob,
          output_name + ":conv3_reduce",
          input_depth,
          conv3_depths[0],
          1,
          ("XavierFill", {}),
          ("ConstantFill", {}),
      )
      conv3_reduce = brew.relu(model, conv3_reduce, conv3_reduce)
      conv3 = brew.conv(
          model,
          conv3_reduce,
          output_name + ":conv3",
          conv3_depths[0],
          conv3_depths[1],
          3,
          ("XavierFill", {}),
          ("ConstantFill", {}),
          pad=1,
      )
      conv3 = brew.relu(model, conv3, conv3)
      # path 3: 1x1 conv + 5x5 conv
      conv5_reduce = brew.conv(
          model,
          input_blob,
          output_name + ":conv5_reduce",
          input_depth,
          conv5_depths[0],
          1,
          ("XavierFill", {}),
          ("ConstantFill", {}),
      )
      conv5_reduce = brew.relu(model, conv5_reduce, conv5_reduce)
      conv5 = brew.conv(
          model,
          conv5_reduce,
          output_name + ":conv5",
          conv5_depths[0],
          conv5_depths[1],
          5,
          ("XavierFill", {}),
          ("ConstantFill", {}),
          pad=2,
      )
      conv5 = brew.relu(model, conv5, conv5)
      # path 4: pool + 1x1 conv
      pool = brew.max_pool(
          model,
          input_blob,
          output_name + ":pool",
          kernel=3,
          stride=1,
          pad=1,
      )
      pool_proj = brew.conv(
          model,
          pool,
          output_name + ":pool_proj",
          input_depth,
          pool_depth,
          1,
          ("XavierFill", {}),
          ("ConstantFill", {}),
      )
      pool_proj = brew.relu(model, pool_proj, pool_proj)
      output = brew.concat(
          model, [conv1, conv3, conv5, pool_proj], output_name
      )
      return output
  
file_path: pytorch/caffe2/python/convnet_benchmarks.py
imports: import argparse
  from caffe2.python import workspace, brew, model_helper
label_desc: Use image node_id 5 for calling a global function with example usage: _InceptionModule(model, input_blob, input_depth, output_name, conv1_depth, conv3_depths, conv5_depths, pool_depth) and returns: output