code: def inverse_spectrogram(
      spectrogram, nfft, window, stride, iterations=30
  ):
      """
      Generate audio waveform from spectrogram using Griffin-Lim algorithm.
      This is an adaptation of the method introduced in
      D. Griffin and Jae Lim, "Signal estimation from modified
      short-time Fourier transform,"
  
      Args:
        spectrogram: A spectrogram with shape (None, nfft // 2 + 1).
            The first dimension depends on window and stride.
            The second contains half of the absolute values
            of the Fourier transforms because the FFT of a real
            signal is symmetrical.
        nfft: Size of FFT.
        window: Size of window.
        stride: Size of hops between windows.
        iterations: number of Griffin-Lim iterations.
            Change this to higher values if the reconstructed phase
            is not appropriate (i.e., the audio is coming out
            echoed or otherwise `weird`)
  
      Returns:
        An 1-D waveform audio tensor.
      """
      # TODO: Support multiple inv spectrograms
  
      S = tf.expand_dims(spectrogram, 0)
      S_complex = tf.identity(tf.cast(S, dtype=tf.complex64))
      y = tf.signal.inverse_stft(S_complex, window, stride, nfft)
      for i in range(iterations):
          estimated_spectrogram = tf.signal.stft(
              y,
              frame_length=window,
              frame_step=stride,
              fft_length=nfft,
              window_fn=tf.signal.hann_window,
              pad_end=False,
          )
  
          # 1e-8 is for numerical stability and works rather well
          angles = estimated_spectrogram / tf.cast(
              tf.maximum(1e-8, tf.abs(estimated_spectrogram)),
              tf.complex64,
          )
          y = tf.signal.inverse_stft(
              S_complex * angles, window, stride, nfft
          )
      # correct the magnitude of y to match the input spectrogram
      if iterations > 0:
          amplitude_correction = tf.reduce_max(
              tf.abs(S)
          ) / tf.reduce_max(tf.abs(estimated_spectrogram))
          y = y * amplitude_correction
      return tf.squeeze(y, 0)
  
imports: import sys
  import tensorflow
  from tensorflow_io.python.ops import core_ops
label_desc: Use image node_id 2 for calling a global function with example usage: inverse_spectrogram(spectrogram, nfft, window, stride, iterations) and returns: tf