code: def run_with_model_class(
      self, model_class: Type[ABCClassificationModel], epochs: int
  ):
      bert_path = get_bert_path()
  
      train_x, train_y = SMP2018ECDTCorpus.load_data("train")
      valid_x, valid_y = SMP2018ECDTCorpus.load_data("valid")
      test_x, test_y = SMP2018ECDTCorpus.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)]
  
      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/classifications.py
imports: import logging
  import os
  import time
  from datetime import datetime
  from typing import Type
  import pandas
  import tensorflow
  from kashgari.callbacks import EvalCallBack
  from kashgari.corpus import SMP2018ECDTCorpus
  from kashgari.embeddings import BertEmbedding
  from kashgari.tasks.classification import ABCClassificationModel
  from kashgari.tasks.classification import ALL_MODELS
  from examples.tools import get_bert_path
label_desc: Use image node_id 1 for calling the ClassificationPerformance obj's underlying member method code with example usage: obj.run_with_model_class(model_class, epochs) and returns: report