class_docstr: Convenient builder to create a :class:`~pyflink.common.serialization.BulkWriterFactory` that
  writes records with a predefined schema into Parquet files in a batch fashion.
  
  Example:
  ::
  
      >>> row_type = DataTypes.ROW([
      ...     DataTypes.FIELD('string', DataTypes.STRING()),
      ...     DataTypes.FIELD('int_array', DataTypes.ARRAY(DataTypes.INT()))
      ... ])
      >>> sink = FileSink.for_bulk_format(
      ...     OUTPUT_DIR, ParquetBulkWriters.for_row_type(
      ...         row_type,
      ...         hadoop_config=Configuration(),
      ...         utc_timestamp=True,
      ...     )
      ... ).build()
      >>> ds.sink_to(sink)
  
  .. versionadded:: 1.16.0
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docstr: Create a :class:`~pyflink.common.serialization.BulkWriterFactory` that writes records
  with a predefined schema into Parquet files in a batch fashion.
  
  :param row_type: The RowType of records, it should match the RowTypeInfo of Row records.
  :param hadoop_config: Hadoop configuration.
  :param utc_timestamp: Use UTC timezone or local timezone to the conversion between epoch
      time and LocalDateTime. Hive 0.x/1.x/2.x use local timezone. But Hive 3.x use UTC
      timezone.
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code: def for_row_type(
      row_type: "RowType",
      hadoop_config: Optional[Configuration] = None,
      utc_timestamp: bool = False,
  ) -> "BulkWriterFactory":
      """
      Create a :class:`~pyflink.common.serialization.BulkWriterFactory` that writes records
      with a predefined schema into Parquet files in a batch fashion.
  
      :param row_type: The RowType of records, it should match the RowTypeInfo of Row records.
      :param hadoop_config: Hadoop configuration.
      :param utc_timestamp: Use UTC timezone or local timezone to the conversion between epoch
          time and LocalDateTime. Hive 0.x/1.x/2.x use local timezone. But Hive 3.x use UTC
          timezone.
      """
      if not hadoop_config:
          hadoop_config = Configuration()
  
      from pyflink.table.types import _to_java_data_type
  
      jvm = get_gateway().jvm
      JParquetRowDataBuilder = (
          jvm.org.apache.flink.formats.parquet.row.ParquetRowDataBuilder
      )
      return RowDataBulkWriterFactory(
          JParquetRowDataBuilder.createWriterFactory(
              _to_java_data_type(row_type).getLogicalType(),
              create_hadoop_configuration(hadoop_config),
              utc_timestamp,
          ),
          row_type,
      )
  
file_path: flink/flink-python/pyflink/datastream/formats/parquet.py
imports: from typing import Optional, TYPE_CHECKING
  from pyflink.common import Configuration
  from pyflink.common.serialization import BulkWriterFactory, RowDataBulkWriterFactory
  from pyflink.datastream.connectors.file_system import StreamFormat, BulkFormat
  from pyflink.datastream.formats.avro import AvroSchema
  from pyflink.datastream.utils import create_hadoop_configuration
  from pyflink.java_gateway import get_gateway
label_desc: Use image node_id 1 for calling the ParquetBulkWriters obj's underlying member method code with example usage: obj.for_row_type(row_type, hadoop_config, utc_timestamp) and returns: RowDataBulkWriterFactory