code: def test_onnx_compatibility(self):
      tmp_onnx_file_path = os.path.join(
          tempfile.gettempdir(), str(uuid.uuid4()) + ".onnx"
      )
      try:
          resampler = ResampleFrac(old_sr=32_000, new_sr=16_000)
          example_input1 = th.rand(1, 100, dtype=th.float32)
          example_input2 = th.rand(1, 124, dtype=th.float32)
  
          th.onnx.export(
              resampler,
              example_input1,
              tmp_onnx_file_path,
              export_params=True,
              opset_version=11,
              do_constant_folding=True,
              input_names=["input"],
              output_names=["output"],
              dynamic_axes={
                  "input": {0: "num_channels", 1: "num_samples"},
                  "output": {0: "num_channels", 1: "num_samples"},
              },
          )
          onnx_model = onnxruntime.InferenceSession(tmp_onnx_file_path)
          onnxruntime_output = onnx_model.run(
              ["output"], {"input": example_input2.numpy()}
          )[0]
          self.assertEqual(onnxruntime_output.shape[-1], 62)
  
          torch_output = resampler(example_input2)
          self.assertEqual(torch_output.shape[-1], 62)
  
          self.assertSimilar(
              th.from_numpy(onnxruntime_output),
              torch_output,
              example_input2,
          )
      finally:
          if os.path.isfile(tmp_onnx_file_path):
              os.remove(tmp_onnx_file_path)
  
imports: import os
  import tempfile
  import uuid
  from pathlib import Path
  import math
  import random
  import unittest
  import resampy
  import torch
  from julius import resample, ResampleFrac
label_desc: Use image node_id 10 for calling the TestResampleFrac obj's underlying member method code with example usage: obj.test_onnx_compatibility() without return types