code: def _run(  # type: ignore
      self,
      num_mel_bins,
      dft_length,
      sample_rate,
      lower_edge_hertz,
      upper_edge_hertz,
      output_datatype=None,
  ):
      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)
  
      if output_datatype is None:
          output = output.astype(np.float32)
      else:
          dtype = tensor_dtype_to_np_dtype(output_datatype)
          output = output.astype(dtype)
      return (output,)
  
imports: import numpy
  from onnx.helper import tensor_dtype_to_np_dtype
  from onnx.reference.op_run import OpRun
label_desc: Use image node_id 1 for calling the MelWeightMatrix obj's underlying member method code with example usage: obj._run(num_mel_bins, dft_length, sample_rate, lower_edge_hertz, upper_edge_hertz, output_datatype) and returns: output