code: def runTest(self):
      document_assembler = (
          DocumentAssembler()
          .setInputCol("ref")
          .setOutputCol("document")
      )
  
      sentence_embeddings = (
          BertSentenceEmbeddings.pretrained("sent_small_bert_L2_128")
          .setInputCols("document")
          .setOutputCol("sentence_embeddings")
      )
  
      multi_classifier = (
          MultiClassifierDLApproach()
          .setInputCols("sentence_embeddings")
          .setOutputCol("category")
          .setLabelColumn("labels")
          .setBatchSize(64)
          .setMaxEpochs(20)
          .setLr(0.001)
          .setThreshold(0.5)
          .setRandomSeed(44)
      )
  
      pipeline = Pipeline(
          stages=[
              document_assembler,
              sentence_embeddings,
              multi_classifier,
          ]
      )
  
      model = pipeline.fit(self.data)
      model.stages[-1].write().overwrite().save(
          "./tmp_multiClassifierDL_model"
      )
  
      multi_classsifierdl_model = (
          MultiClassifierDLModel.load("./tmp_multiClassifierDL_model")
          .setInputCols(["sentence_embeddings"])
          .setOutputCol("class")
      )
  
      print(multi_classsifierdl_model.getClasses())
  
file_path: spark-nlp/python/test/annotator/classifier_dl/multi_classifier_dl_test.py
imports: import os
  import unittest
  import pytest
  from pyspark.sql.functions import split
  from sparknlp.annotator import *
  from sparknlp.base import *
  from test.util import SparkSessionForTest
label_desc: Use image node_id 2 for calling the MultiClassifierDLTestSpec obj's underlying member method code with example usage: obj.runTest() without return types