code: def pick_worker(self, partition: int, workers: Sequence[str]) -> str:
      return _get_worker_for_range_sharding(
          self.npartitions, partition, workers
      )
  
imports: from __future__ import annotations
  import logging
  import os
  from collections import defaultdict
  from collections.abc import Callable, Collection, Generator, Hashable, Iterable, Iterator, Sequence
  from concurrent.futures import ThreadPoolExecutor
  from dataclasses import dataclass
  from pathlib import Path
  from typing import TYPE_CHECKING, Any
  import toolz
  from tornado.ioloop import IOLoop
  import dask
  from dask.base import tokenize
  from dask.highlevelgraph import HighLevelGraph
  from dask.layers import Layer
  from dask.typing import Key
  from distributed.core import PooledRPCCall
  from distributed.exceptions import Reschedule
  from distributed.metrics import context_meter
  from distributed.shuffle._arrow import buffers_to_table, check_dtype_support, check_minimal_arrow_version, convert_shards, deserialize_table, read_from_disk, serialize_table
  from distributed.shuffle._core import NDIndex, ShuffleId, ShuffleRun, ShuffleSpec, barrier_key, get_worker_plugin, handle_transfer_errors, handle_unpack_errors
  from distributed.shuffle._limiter import ResourceLimiter
  from distributed.shuffle._worker_plugin import ShuffleWorkerPlugin
  from distributed.sizeof import sizeof
label_desc: Use image node_id 2 for calling the DataFrameShuffleSpec obj's underlying member method code with example usage: obj.pick_worker(partition, workers) and returns: _get_worker_for_range_sharding