code: def _calculate_partitions(
      df: pd.DataFrame, index: IndexLabel, npartitions: int
  ):
      index = _prepare_index_for_partitioning(df, index)
      from dask.dataframe.shuffle import partitioning_index
  
      meta = df._meta._constructor_sliced([0])
      # Ensure that we have the same index as before to avoid alignment
      # when calculating meta dtypes later on
      meta.index = df._meta_nonempty.index[:1]
      partitions = index.map_partitions(
          partitioning_index,
          npartitions=npartitions or df.npartitions,
          meta=meta,
          transform_divisions=False,
      )
      df2 = df.assign(**{_HASH_COLUMN_NAME: partitions})
      df2._meta.index.name = df._meta.index.name
      return df2
  
imports: from __future__ import annotations
  from collections.abc import Iterable, Sequence
  from typing import TYPE_CHECKING, Any
  import dask
  from dask.base import is_dask_collection, tokenize
  from dask.highlevelgraph import HighLevelGraph
  from dask.layers import Layer
  from distributed.shuffle._arrow import check_minimal_arrow_version
  from distributed.shuffle._core import ShuffleId, barrier_key, get_worker_plugin
  from distributed.shuffle._shuffle import shuffle_barrier, shuffle_transfer
label_desc: Use image node_id 2 for calling a global function with example usage: _calculate_partitions(df, index, npartitions) and returns: df2