code: def convert_LinearFunction(
      func, opset_version, input_names, output_names, context
  ):
      # When the func has bias
      if len(func.inputs) == 2:
          bias_dim = func.inputs[1].shape[0]
          bias = np.zeros((bias_dim,), dtype=func.inputs[0].dtype)
          bias_name = context.add_param(bias, "bias")
          input_names.append(bias_name)
  
      if opset_version == 1 or opset_version == 6:
          return (
              onnx_helper.make_node(
                  "Gemm",
                  input_names,
                  output_names,
                  alpha=1.0,
                  beta=1.0,
                  broadcast=1,
                  transA=0,
                  transB=1,
              ),
          )
      elif opset_version == 7:
          return (
              onnx_helper.make_node(
                  "Gemm",
                  input_names,
                  output_names,
                  alpha=1.0,
                  beta=1.0,
                  transA=0,
                  transB=1,
              ),
          )
  
file_path: chainer/onnx_chainer/functions/connection.py
imports: import numpy
  from onnx_chainer.functions.opset_version import support
  from onnx_chainer import onnx_helper
label_desc: Use image node_id 6 for calling a global function with example usage: convert_LinearFunction(func, opset_version, input_names, output_names, context) and returns: onnx_helper, onnx_helper