code: def convert_LinearInterpolate(
      func, opset_version, input_names, output_names, context
  ):
      typ = (
          func.inputs[0].dtype
          if isinstance(func.inputs[0].dtype, np.dtype)
          else np.dtype(func.inputs[0].dtype)
      )
  
      one_name = context.add_const(np.array(1, dtype=typ), "one")
  
      kwargs = {"consumed_inputs": [1, 1]} if opset_version == 1 else {}
      kwargs2 = {} if opset_version >= 7 else {"broadcast": 1}
  
      gb = onnx_helper.GraphBuilder()
      p, x, y = input_names
      n1 = gb.op("Sub", [one_name, p], **kwargs, **kwargs2)
      n2 = gb.op("Mul", [p, x], **kwargs)
      n3 = gb.op("Mul", [n1, y], **kwargs)
      gb.op_output_named("Add", [n2, n3], output_names, **kwargs)
  
      return gb.nodes()
  
imports: import numpy
  from onnx.mapping import NP_TYPE_TO_TENSOR_TYPE
  from onnx_chainer.functions.opset_version import support
  from onnx_chainer import onnx_helper
label_desc: Use image node_id 37 for calling a global function with example usage: convert_LinearInterpolate(func, opset_version, input_names, output_names, context) and returns: gb