code: def power_pooled_outliers(
      controls, frame_width=80, num_devs=2.5, pad_mode="center"
  ):
      """Finds loudnesses that are n std devs below the mean of their neighbors."""
      shifted_power = (
          ddsp.spectral_ops.compute_power(
              controls["audio"], frame_size=256
          )
          + ddsp.spectral_ops.LD_RANGE
      )
      padded_power = pad_for_frame(
          shifted_power, mode=pad_mode, frame_width=frame_width, axis=0
      )
      frames = tf.signal.frame(padded_power, frame_width, 1)
      low_pooled = np.mean(frames, axis=-1) - (
          num_devs * np.std(frames, axis=-1)
      )
      return np.asarray(
          (low_pooled < shifted_power) & (shifted_power > 0)
      )
  
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 13 for calling a global function with example usage: power_pooled_outliers(controls, frame_width, num_devs, pad_mode) and returns: np