code: def _apply_fc_weight_for_sum_match(
      model,
      input,
      dim_in,
      dim_out,
      scope,
      name,
  ):
      output = brew.fc(
          model,
          input,
          s(scope, name),
          dim_in=dim_in,
          dim_out=dim_out,
          axis=2,
      )
      output = model.net.Squeeze(
          output,
          output,
          dims=[0],
      )
      return output
  
file_path: pytorch/caffe2/python/attention.py
imports: from caffe2.python import brew
label_desc: Use image node_id 5 for calling a global function with example usage: _apply_fc_weight_for_sum_match(model, input, dim_in, dim_out, scope, name) and returns: output