code: def _create_parquet_basic_row_and_data() -> (
      Tuple[RowType, RowTypeInfo, List[Row]]
  ):
      row_type = DataTypes.ROW(
          [
              DataTypes.FIELD("char", DataTypes.CHAR(10)),
              DataTypes.FIELD("varchar", DataTypes.VARCHAR(10)),
              DataTypes.FIELD("binary", DataTypes.BINARY(10)),
              DataTypes.FIELD("varbinary", DataTypes.VARBINARY(10)),
              DataTypes.FIELD("boolean", DataTypes.BOOLEAN()),
              DataTypes.FIELD("decimal", DataTypes.DECIMAL(2, 0)),
              DataTypes.FIELD("int", DataTypes.INT()),
              DataTypes.FIELD("bigint", DataTypes.BIGINT()),
              DataTypes.FIELD("double", DataTypes.DOUBLE()),
              DataTypes.FIELD(
                  "date", DataTypes.DATE().bridged_to("java.sql.Date")
              ),
              DataTypes.FIELD(
                  "time", DataTypes.TIME().bridged_to("java.sql.Time")
              ),
              DataTypes.FIELD(
                  "timestamp",
                  DataTypes.TIMESTAMP(3).bridged_to(
                      "java.sql.Timestamp"
                  ),
              ),
              DataTypes.FIELD(
                  "timestamp_ltz", DataTypes.TIMESTAMP_LTZ(3)
              ),
          ]
      )
      row_type_info = Types.ROW_NAMED(
          [
              "char",
              "varchar",
              "binary",
              "varbinary",
              "boolean",
              "decimal",
              "int",
              "bigint",
              "double",
              "date",
              "time",
              "timestamp",
              "timestamp_ltz",
          ],
          [
              Types.STRING(),
              Types.STRING(),
              Types.PRIMITIVE_ARRAY(Types.BYTE()),
              Types.PRIMITIVE_ARRAY(Types.BYTE()),
              Types.BOOLEAN(),
              Types.BIG_DEC(),
              Types.INT(),
              Types.LONG(),
              Types.DOUBLE(),
              Types.SQL_DATE(),
              Types.SQL_TIME(),
              Types.SQL_TIMESTAMP(),
              Types.INSTANT(),
          ],
      )
      datetime_ltz = datetime.datetime(
          1970, 2, 3, 4, 5, 6, 700000, tzinfo=pytz.timezone("UTC")
      )
      timestamp_ltz = Instant.of_epoch_milli(
          (
              calendar.timegm(datetime_ltz.utctimetuple())
              + calendar.timegm(time.localtime(0))
          )
          * 1000
          + datetime_ltz.microsecond // 1000
      )
      data = [
          Row(
              char="char",
              varchar="varchar",
              binary=b"binary",
              varbinary=b"varbinary",
              boolean=True,
              decimal=Decimal(1.5),
              int=2147483647,
              bigint=-9223372036854775808,
              double=2e-308,
              date=datetime.date(1970, 1, 1),
              time=datetime.time(1, 1, 1),
              timestamp=datetime.datetime(1970, 1, 2, 3, 4, 5, 600000),
              timestamp_ltz=timestamp_ltz,
          )
      ]
      return row_type, row_type_info, data
  
imports: import calendar
  import datetime
  import glob
  import os
  import tempfile
  import time
  import unittest
  from decimal import Decimal
  from typing import List, Tuple
  import pandas
  import pytz
  from pyflink.common.time import Instant
  from pyflink.common import Configuration, Row
  from pyflink.common.typeinfo import RowTypeInfo, Types
  from pyflink.common.watermark_strategy import WatermarkStrategy
  from pyflink.datastream.connectors.file_system import FileSource, FileSink
  from pyflink.datastream.formats.tests.test_avro import _create_basic_avro_schema_and_py_objects, _check_basic_avro_schema_results, _create_enum_avro_schema_and_py_objects, _check_enum_avro_schema_results, _create_union_avro_schema_and_py_objects, _check_union_avro_schema_results, _create_array_avro_schema_and_py_objects, _check_array_avro_schema_results, _create_map_avro_schema_and_py_objects, _check_map_avro_schema_results, _create_map_avro_schema_and_records, _create_array_avro_schema_and_records, _create_union_avro_schema_and_records, _create_enum_avro_schema_and_records, _create_basic_avro_schema_and_records, _import_avro_classes
  from pyflink.datastream.formats.avro import GenericRecordAvroTypeInfo, AvroSchema
  from pyflink.datastream.formats.parquet import AvroParquetReaders, ParquetColumnarRowInputFormat, AvroParquetWriters, ParquetBulkWriters
  from pyflink.datastream.tests.test_util import DataStreamTestSinkFunction
  from pyflink.datastream.utils import create_hadoop_configuration
  from pyflink.java_gateway import get_gateway
  from pyflink.table.types import RowType, DataTypes, _to_java_data_type
  from pyflink.testing.test_case_utils import PyFlinkStreamingTestCase, to_java_data_structure
label_desc: Use image node_id 2 for calling a global function with example usage: _create_parquet_basic_row_and_data() and returns: row_type,  row_type_info,  data