code: def testDurationParam():
      spark = test_helpers.getOrCreateSparkSession(
          test_helpers.getCurrentMethodName()
      )
      import catboost_spark
  
      featureNames = ["f1", "f2", "f3"]
  
      srcSchemaData = [
          ("features", VectorUDT()),
          ("label", StringType()),
          ("groupId", LongType()),
          ("subgroupId", IntegerType()),
          ("weight", FloatType()),
      ]
  
      srcData = [
          Row(
              Vectors.dense(0.13, 0.22, 0.23),
              "0.34",
              0x86F1B93B695F9E6,
              0x23D794E,
              1.0,
          ),
          Row(
              Vectors.dense(0.1, 0.2, 0.11),
              "0.12",
              0xB337C6FEFE2E2F7,
              0xD34BFBD,
              0.12,
          ),
          Row(
              Vectors.dense(0.97, 0.82, 0.33),
              "0.22",
              0xB337C6FEFE2E2F7,
              0x19CE5B0,
              0.18,
          ),
          Row(
              Vectors.dense(0.9, 0.67, 0.17),
              "0.01",
              0xD9DBDD3199D6518,
              0x19CE5B0,
              1.0,
          ),
          Row(
              Vectors.dense(0.66, 0.1, 0.31),
              "0.0",
              0xD9DBDD3199D6518,
              0x1FA606F,
              2.0,
          ),
          Row(
              Vectors.dense(0.14, 0.18, 0.1),
              "0.42",
              0xD9DBDD3199D6518,
              0x62772D1,
              0.45,
          ),
      ]
      pool = pool_test_helpers.createRawPool(
          test_helpers.getCurrentMethodName,
          pool_test_helpers.createSchema(
              srcSchemaData, featureNames, addFeatureNamesMetadata=True
          ),
          srcData,
          {
              "groupId": "groupId",
              "subgroupId": "subgroupId",
              "weight": "weight",
          },
      )
  
      regressor = (
          catboost_spark.CatBoostRegressor()
          .setIterations(20)
          .setTrainDir(
              tempfile.mkdtemp(
                  prefix=test_helpers.getCurrentMethodName()
              )
          )
          .setSnapshotInterval(datetime.timedelta(hours=10))
      )
      model = regressor.fit(pool)
      predictions = model.transform(pool.data)
  
      print("predictions")
      predictions.show(truncate=False)
  
imports: import collections
  import datetime
  import os
  import shutil
  import tempfile
  import test_helpers
  import pool_test_helpers
  from pyspark.ml import Pipeline, PipelineModel
  from pyspark.ml.feature import StringIndexer, VectorAssembler
  from pyspark.ml.linalg import Vectors, VectorUDT
  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: testDurationParam() without return types