class_docstr: Metrics for MIDI Autoencoder.
class_docstr_tok: ["Metrics","for","MIDI","Autoencoder","."]
code: def __init__(
      self,
      sample_rate,
      frame_rate,
      db_key="loudness_db",
      f0_key="f0_hz",
  ):
      super().__init__(sample_rate, frame_rate)
      self._midi_metrics = metrics.MidiMetrics(
          frames_per_second=frame_rate, tag="learned"
      )
      self._db_key = db_key
      self._f0_key = f0_key
  
imports: import ddsp
  from ddsp.training import heuristics
  from ddsp.training import metrics
  from ddsp.training import summaries
  import gin
  import numpy
  import tensorflow.compat.v2
label_desc: Use image node_id 1 to create a new MidiAutoencoderEvaluator object from inherited base classes: BaseEvaluator with example: obj = MidiAutoencoderEvaluator(sample_rate, frame_rate, db_key, f0_key)