code: def export() -> None:
      node = onnx.helper.make_node(
          "MelWeightMatrix",
          inputs=[
              "num_mel_bins",
              "dft_length",
              "sample_rate",
              "lower_edge_hertz",
              "upper_edge_hertz",
          ],
          outputs=["output"],
      )
  
      num_mel_bins = np.int32(8)
      dft_length = np.int32(16)
      sample_rate = np.int32(8192)
      lower_edge_hertz = np.float32(0)
      upper_edge_hertz = np.float32(8192 / 2)
  
      num_spectrogram_bins = dft_length // 2 + 1
      frequency_bins = np.arange(0, num_mel_bins + 2)
  
      low_frequency_mel = 2595 * np.log10(1 + lower_edge_hertz / 700)
      high_frequency_mel = 2595 * np.log10(1 + upper_edge_hertz / 700)
      mel_step = (
          high_frequency_mel - low_frequency_mel
      ) / frequency_bins.shape[0]
  
      frequency_bins = frequency_bins * mel_step + low_frequency_mel
      frequency_bins = 700 * (np.power(10, (frequency_bins / 2595)) - 1)
      frequency_bins = (
          (dft_length + 1) * frequency_bins
      ) // sample_rate
      frequency_bins = frequency_bins.astype(int)
  
      output = np.zeros((num_spectrogram_bins, num_mel_bins))
      output.flags.writeable = True
  
      for i in range(num_mel_bins):
          lower_frequency_value = frequency_bins[i]  # left
          center_frequency_point = frequency_bins[i + 1]  # center
          higher_frequency_point = frequency_bins[i + 2]  # right
          low_to_center = center_frequency_point - lower_frequency_value
          if low_to_center == 0:
              output[center_frequency_point, i] = 1
          else:
              for j in range(
                  lower_frequency_value, center_frequency_point + 1
              ):
                  output[j, i] = float(
                      j - lower_frequency_value
                  ) / float(low_to_center)
          center_to_high = (
              higher_frequency_point - center_frequency_point
          )
          if center_to_high > 0:
              for j in range(
                  center_frequency_point, higher_frequency_point
              ):
                  output[j, i] = float(
                      higher_frequency_point - j
                  ) / float(center_to_high)
  
      # Expected output
      # 1.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
      # 0.000000, 0.000000, 1.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000,
      # 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000, 0.000000,
      # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000,
      # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000,
      # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000,
      # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
      # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
      # 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
      output = output.astype(np.float32)
      expect(
          node,
          inputs=[
              num_mel_bins,
              dft_length,
              sample_rate,
              lower_edge_hertz,
              upper_edge_hertz,
          ],
          outputs=[output],
          name="test_melweightmatrix",
      )
  
imports: import numpy
  import onnx
  from onnx.backend.test.case.base import Base
  from onnx.backend.test.case.node import expect
label_desc: Use image node_id 1 for calling the MelWeightMatrix obj's underlying member method code with example usage: obj.export() without return types