code: def testBinaryClassificationWithScalePosWeight():
      spark = test_helpers.getOrCreateSparkSession(
          test_helpers.getCurrentMethodName()
      )
      import catboost_spark
  
      featureNames = ["f1", "f2", "f3"]
  
      srcSchemaData = [
          ("features", VectorUDT()),
          ("label", IntegerType()),
      ]
  
      srcData = [
          Row(Vectors.dense(0.1, 0.2, 0.11), 0),
          Row(Vectors.dense(0.97, 0.82, 0.33), 1),
          Row(Vectors.dense(0.13, 0.22, 0.23), 1),
          Row(Vectors.dense(0.14, 0.18, 0.1), 0),
          Row(Vectors.dense(0.9, 0.67, 0.17), 0),
          Row(Vectors.dense(0.66, 0.1, 0.31), 0),
      ]
      pool = pool_test_helpers.createRawPool(
          test_helpers.getCurrentMethodName,
          pool_test_helpers.createSchema(
              srcSchemaData, featureNames, addFeatureNamesMetadata=True
          ),
          srcData,
          {},
      )
  
      classifier = (
          catboost_spark.CatBoostClassifier()
          .setIterations(20)
          .setScalePosWeight(2.0)
          .setLoggingLevel(catboost_spark.ELoggingLevel.Debug)
          .setTrainDir(
              tempfile.mkdtemp(
                  prefix=test_helpers.getCurrentMethodName()
              )
          )
      )
  
      model = classifier.fit(pool)
      predictions = model.transform(pool.data)
      predictions.show(truncate=False)
  
imports: import collections
  import os
  import shutil
  import tempfile
  import test_helpers
  import pool_test_helpers
  from pyspark.ml import Pipeline, PipelineModel
  import pyspark.ml.evaluation
  from pyspark.ml.feature import StringIndexer, VectorAssembler
  from pyspark.ml.linalg import Vectors, VectorUDT
  import pyspark.ml.tuning
  from pyspark.sql import Row
  from pyspark.sql.types import *
label_desc: Use image node_id 6 for calling a global function with example usage: testBinaryClassificationWithScalePosWeight() without return types