class_docstr: Utility to map Kinesis shards to Flink subtask indices. Users can provide a Java
  KinesisShardAssigner in Python if they want to provide custom shared assigner.
class_docstr_tok: ["Utility","to","map","Kinesis","shards","to","Flink","subtask","indices",".","Users","can","provide","a","Java","KinesisShardAssigner","in","Python","if","they","want","to","provide","custom","shared","assigner","."]
docstr: A Default KinesisShardAssigner that maps Kinesis shard hash-key ranges to Flink subtasks.
docstr_tok: ["A","Default","KinesisShardAssigner","that","maps","Kinesis","shard","hash-key","ranges","to","Flink","subtasks","."]
code: def default_shard_assigner() -> "KinesisShardAssigner":
      """
      A Default KinesisShardAssigner that maps Kinesis shard hash-key ranges to Flink subtasks.
      """
      return KinesisShardAssigner(
          get_gateway().jvm.org.apache.flink.streaming.connectors.kinesis.internals.KinesisDataFetcher.DEFAULT_SHARD_ASSIGNER
      )
  
imports: from typing import Dict, Union, List
  from pyflink.common import SerializationSchema, DeserializationSchema, AssignerWithPeriodicWatermarksWrapper
  from pyflink.datastream.functions import SourceFunction
  from pyflink.datastream.connectors import Sink
  from pyflink.java_gateway import get_gateway
label_desc: Use image node_id 2 for calling the KinesisShardAssigner obj's underlying member method code with example usage: obj.default_shard_assigner() and returns: KinesisShardAssigner