code: def melscale(input, rate, mels, fmin, fmax, name=None):
      """
      Turn spectrogram into mel scale spectrogram
  
      Args:
        input: A spectrogram Tensor with shape [frames, nfft+1].
        rate: Sample rate of the audio.
        mels: Number of mel filterbanks.
        fmin: Minimum frequency.
        fmax: Maximum frequency.
        name: A name for the operation (optional).
  
      Returns:
        A tensor of mel spectrogram with shape [frames, mels].
      """
  
      # TODO: Support audio with channel > 1.
      nbins = tf.shape(input)[-1]
      matrix = tf.signal.linear_to_mel_weight_matrix(
          num_mel_bins=mels,
          num_spectrogram_bins=nbins,
          sample_rate=rate,
          lower_edge_hertz=fmin,
          upper_edge_hertz=fmax,
      )
  
      return tf.tensordot(input, matrix, 1)
  
imports: import sys
  import tensorflow
  from tensorflow_io.python.ops import core_ops
label_desc: Use image node_id 3 for calling a global function with example usage: melscale(input, rate, mels, fmin, fmax, name) and returns: tf