class_docstr: emotion keras solver 
class_docstr_tok: ["emotion","keras","solver"]
docstr: metric_specific callbacks
docstr_tok: ["metric_specific","callbacks"]
code: def get_metric_callbacks(
      self, eval_gen, eval_task, monitor_used, decoder_type=None
  ):
      """metric_specific callbacks"""
      callbacks = []
  
      if monitor_used == "ClassReport":
          metric_func = self.model
          metric_cal = ClassReportCallBack(
              metric_func, eval_gen, eval_task
          )
          callbacks.append(metric_cal)
  
      logging.info(f"CallBack: Val Metric on {monitor_used}")
      return callbacks
  
imports: import librosa
  from absl import logging
  import delta.compat
  from tensorflow.keras import backend
  from tensorflow.python.keras.utils import losses_utils
  from delta import utils
  from delta.utils.solver.estimator_solver import EstimatorSolver
  from delta.utils.solver.utils.callbacks import ClassReportCallBack
  from delta.utils.solver.asr_solver import AsrSolver
  from delta.utils.register import registers
label_desc: Use image node_id 9 for calling the EmoKerasSolver obj's underlying member method code with example usage: obj.get_metric_callbacks(eval_gen, eval_task, monitor_used, decoder_type) and returns: callbacks