code: def setUpClass(cls):
      from sparknlp.training import POS
  
      cls.trainDataset = POS().readDataset(
          SparkContextForTest.spark,
          "../src/test/resources/anc-pos-corpus-small/test-training.txt",
          delimiter="|",
          outputPosCol="tags",
          outputDocumentCol="document",
          outputTextCol="text",
      )
  
      document_assembler = (
          DocumentAssembler()
          .setInputCol("text")
          .setOutputCol("document")
      )
  
      tokenizer = (
          Tokenizer().setInputCols("document").setOutputCol("token")
      )
  
      embds = (
          WordEmbeddings()
          .setStoragePath(
              "../src/test/resources/ner-corpus/embeddings.100d.test.txt",
              ReadAs.TEXT,
          )
          .setDimension(100)
          .setStorageRef("glove_100d")
          .setInputCols("document", "token")
          .setOutputCol("embeddings")
      )
  
      cls.classifier = (
          NerDLApproach()
          .setInputCols("document", "token", "embeddings")
          .setLabelColumn("tags")
          .setOutputCol("out")
          .setMaxEpochs(1)
          .setEnableOutputLogs(True)
          .setOutputLogsPath(cls.OUTPUT_LOG_PATH)
      )
  
      cls.pipeline = Pipeline(
          stages=[document_assembler, tokenizer, embds, cls.classifier]
      )
  
      # TODO: Does not work for SentenceDetectorDLApproach due to log naming scheme.
      # cls.trainDataset = SparkContextForTest.spark.createDataFrame(
      #     [["This is a sentence."]], ["text"]
      # )
      #
      # document_assembler = (
      #     DocumentAssembler().setInputCol("text").setOutputCol("document")
      # )
      # cls.classifier = (
      #     SentenceDetectorDLApproach()
      #     .setInputCols("document")
      #     .setOutputCol("sentence")
      #     .setOutputLogsPath(cls.OUTPUT_LOG_PATH)
      #     .setEpochsNumber(1)
      # )
      #
      # cls.pipeline = Pipeline(stages=[document_assembler, cls.classifier])
  
      comet_ml.init(
          project_name="sparknlp-testing", offline_directory="/tmp"
      )
      cls.logger = CometLogger(
          comet_mode="offline", offline_directory=cls.OUTPUT_LOG_PATH
      )
  
file_path: spark-nlp/python/test/thirdparty/comet.py
imports: import unittest
  from sparknlp.base import *
  from sparknlp.annotator import *
  from sparknlp.logging.comet import CometLogger
  import comet_ml
  import os
  import glob
  from test.util import SparkContextForTest
label_desc: Use image node_id 1 for calling the CometTestSpec obj's underlying member method code with example usage: obj.setUpClass(cls) without return types