class_docstr: Computes F0 and loudness metrics.
class_docstr_tok: ["Computes","F0","and","loudness","metrics","."]
code: def evaluate(self, batch, outputs, losses):
      del losses  # Unused.
      audio_gen = outputs["audio_gen"]
      self._loudness_metrics.update_state(batch, audio_gen)
  
      if "f0_hz" in outputs and "f0_hz" in batch:
          self._f0_metrics.update_state(batch, outputs["f0_hz"])
      elif self._run_f0_crepe:
          self._f0_crepe_metrics.update_state(batch, audio_gen)
  
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 2 for calling the F0LdEvaluator obj's underlying member method code with example usage: obj.evaluate(batch, outputs, losses) without return types