code: def audio_microfrontend(
      audio,
      sample_rate=16000,
      window_size=25,
      window_step=10,
      num_channels=32,
      upper_band_limit=7500.0,
      lower_band_limit=125.0,
      smoothing_bits=10,
      even_smoothing=0.025,
      odd_smoothing=0.06,
      min_signal_remaining=0.05,
      enable_pcan=True,
      pcan_strength=0.95,
      pcan_offset=80.0,
      gain_bits=21,
      enable_log=True,
      scale_shift=6,
      left_context=0,
      right_context=0,
      frame_stride=1,
      zero_padding=False,
      out_scale=1,
      out_type=dtypes.uint16,
  ):
      """Audio Microfrontend Op.
  
      This Op converts a sequence of audio data into one or more
      feature vectors containing filterbanks of the input. The
      conversion process uses a lightweight library to perform:
  
      1. A slicing window function
      2. Short-time FFTs
      3. Filterbank calculations
      4. Noise reduction
      5. PCAN Auto Gain Control
      6. Logarithmic scaling
  
      Args:
        audio: 1D Tensor, int16 audio data in temporal ordering.
        sample_rate: Integer, the sample rate of the audio in Hz.
        window_size: Integer, length of desired time frames in ms.
        window_step: Integer, length of step size for the next frame in ms.
        num_channels: Integer, the number of filterbank channels to use.
        upper_band_limit: Float, the highest frequency included in the filterbanks.
        lower_band_limit: Float, the lowest frequency included in the filterbanks.
        smoothing_bits: Int, scale up signal by 2^(smoothing_bits) before reduction.
        even_smoothing: Float, smoothing coefficient for even-numbered channels.
        odd_smoothing: Float, smoothing coefficient for odd-numbered channels.
        min_signal_remaining: Float, fraction of signal to preserve in smoothing.
        enable_pcan: Bool, enable PCAN auto gain control.
        pcan_strength: Float, gain normalization exponent.
        pcan_offset: Float, positive value added in the normalization denominator.
        gain_bits: Int, number of fractional bits in the gain.
        enable_log: Bool, enable logarithmic scaling of filterbanks.
        scale_shift: Integer, scale filterbanks by 2^(scale_shift).
        left_context: Integer, number of preceding frames to attach to each frame.
        right_context: Integer, number of preceding frames to attach to each frame.
        frame_stride: Integer, M frames to skip over, where output[n] = frame[n*M].
        zero_padding: Bool, if left/right context is out-of-bounds, attach frame of
          zeroes. Otherwise, frame[0] or frame[size-1] will be copied.
        out_scale: Integer, divide all filterbanks by this number.
        out_type: DType, type of the output Tensor, defaults to UINT16.
  
      Returns:
        filterbanks: 2D Tensor, each row is a time frame, each column is a channel.
  
      Raises:
        ValueError: If the audio tensor is not explicitly a vector.
      """
      audio_shape = audio.shape
      if audio_shape.ndims is None:
          raise ValueError(
              "Input to `AudioMicrofrontend` should have known rank."
          )
      if len(audio_shape) > 1:
          audio = array_ops.reshape(audio, [-1])
  
      return gen_audio_microfrontend_op.audio_microfrontend(
          audio,
          sample_rate,
          window_size,
          window_step,
          num_channels,
          upper_band_limit,
          lower_band_limit,
          smoothing_bits,
          even_smoothing,
          odd_smoothing,
          min_signal_remaining,
          enable_pcan,
          pcan_strength,
          pcan_offset,
          gain_bits,
          enable_log,
          scale_shift,
          left_context,
          right_context,
          frame_stride,
          zero_padding,
          out_scale,
          out_type,
      )
  
imports: from tensorflow.lite.experimental.microfrontend.ops import gen_audio_microfrontend_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import load_library
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.platform import resource_loader
label_desc: Use image node_id 1 for calling a global function with example usage: audio_microfrontend(audio, sample_rate, window_size, window_step, num_channels, upper_band_limit, lower_band_limit, smoothing_bits, even_smoothing, odd_smoothing, min_signal_remaining, enable_pcan, pcan_strength, pcan_offset, gain_bits, enable_log, scale_shift, left_context, right_context, frame_stride, zero_padding, out_scale, out_type) and returns: gen_audio_microfrontend_op