code: def amp_pooled_outliers(
      controls, frame_width=80, num_devs=2, pad_mode="center"
  ):
      """Finds amps that are n std devs below the mean of their neighbors."""
      log_amps = np.log(
          np.squeeze(controls["harmonic"]["controls"]["amplitudes"])
      )
      padded_amps = pad_for_frame(
          log_amps, mode=pad_mode, frame_width=frame_width, axis=0
      )
      frames = tf.signal.frame(padded_amps, frame_width, 1)
      low_pooled = np.mean(frames, axis=-1) - (
          num_devs * np.std(frames, axis=-1)
      )
  
      return low_pooled < log_amps
  
file_path: ddsp/ddsp/training/heuristics.py
imports: import ddsp
  import gin
  import note_seq
  import numpy
  import tensorflow.compat.v2
label_desc: Use image node_id 11 for calling a global function with example usage: amp_pooled_outliers(controls, frame_width, num_devs, pad_mode) and returns: unknown