code: def getSimpleTestPool(method_name):
      featureNames = ["f1", "f2", "f3"]
  
      srcSchemaData = [
          ("features", VectorUDT()),
          ("label", StringType()),
          ("groupId", LongType()),
          ("sampleId", IntegerType()),
      ]
  
      srcData = [
          Row(
              Vectors.dense(0.13, 0.22, 0.23),
              "0.34",
              0x86F1B93B695F9E6,
              0x23D794E,
          ),
          Row(
              Vectors.dense(0.1, 0.2, 0.11),
              "0.12",
              0xB337C6FEFE2E2F7,
              0xD34BFBD,
          ),
          Row(
              Vectors.dense(0.97, 0.82, 0.33),
              "0.22",
              0xB337C6FEFE2E2F7,
              0x19CE5B0,
          ),
          Row(
              Vectors.dense(0.9, 0.67, 0.17),
              "0.01",
              0xD9DBDD3199D6518,
              0x19CE5B0,
          ),
          Row(
              Vectors.dense(0.66, 0.1, 0.31),
              "0.0",
              0xD9DBDD3199D6518,
              0x1FA606F,
          ),
          Row(
              Vectors.dense(0.14, 0.18, 0.1),
              "0.42",
              0xD9DBDD3199D6518,
              0x62772D1,
          ),
      ]
      return pool_test_helpers.createRawPool(
          method_name,
          pool_test_helpers.createSchema(
              srcSchemaData, featureNames, addFeatureNamesMetadata=True
          ),
          srcData,
          {"groupId": "groupId", "sampleId": "sampleId"},
      )
  
file_path: catboost/catboost/spark/catboost4j-spark/core/src/test/python/pool_serialization_test.py
imports: import tempfile
  import pytest
  import test_helpers
  import pool_test_helpers
  from pyspark.ml.linalg import Vectors, VectorUDT
  from pyspark.sql import Row
  from pyspark.sql.types import *
label_desc: Use image node_id 1 for calling a global function with example usage: getSimpleTestPool(method_name) and returns: pool_test_helpers