code: def run_with_model_class(
      self, model_class: Type[ABCLabelingModel], epochs: int
  ):
      bert_path = get_bert_path()
  
      train_x, train_y = ChineseDailyNerCorpus.load_data("train")
      valid_x, valid_y = ChineseDailyNerCorpus.load_data("valid")
      test_x, test_y = ChineseDailyNerCorpus.load_data("test")
  
      bert_embed = BertEmbedding(bert_path)
      model = model_class(bert_embed)
  
      log_path = os.path.join(log_root, model_class.__name__)
      file_writer = tf.summary.create_file_writer(log_path + "/metrics")
      file_writer.set_as_default()
      callbacks = [
          EvalCallBack(model, test_x, test_y, step=1, truncating=True)
      ]
      # callbacks = []
      model.fit(
          train_x,
          train_y,
          valid_x,
          valid_y,
          epochs=epochs,
          callbacks=callbacks,
      )
  
      report = model.evaluate(test_x, test_y)
      del model
      del bert_embed
      return report
  
file_path: Kashgari/test_performance/labeling.py
imports: import os
  from datetime import datetime
  from typing import Type
  import tensorflow
  from kashgari.callbacks import EvalCallBack
  from kashgari.corpus import ChineseDailyNerCorpus
  from kashgari.embeddings import BertEmbedding
  from kashgari.tasks.labeling import ABCLabelingModel
  from kashgari.tasks.labeling import ALL_MODELS
  from test_performance.classifications import ClassificationPerformance
  from examples.tools import get_bert_path
label_desc: Use image node_id 1 for calling the LabelingPerformance obj's underlying member method code with example usage: obj.run_with_model_class(model_class, epochs) and returns: report