class_docstr: A helper class to compute metrics for MIDI output.
class_docstr_tok: ["A","helper","class","to","compute","metrics","for","MIDI","output","."]
code: def __init__(self, frames_per_second, tag, align_midi_with_f0=True):
      self.tag = tag
      self.note_counts = EvalCounts(0, 0, 0)
      self.note_offset_counts = EvalCounts(0, 0, 0)
      self.frame_counts = EvalCounts(0, 0, 0)
      self._frames_per_second = frames_per_second
      self._align_midi_with_f0 = align_midi_with_f0
  
imports: import dataclasses
  from absl import logging
  import ddsp
  import librosa
  import mir_eval
  import note_seq
  from note_seq import sequences_lib
  import numpy
  import tensorflow.compat.v2
label_desc: Use image node_id 1 to create a new MidiMetrics object from inherited base classes: object with example: obj = MidiMetrics(frames_per_second, tag, align_midi_with_f0)