class_docstr: The API necessary to provide a new Parquet reader/writer
docstr: Perform engine-specific initialization steps for this dataset
  
  Parameters
  ----------
  df: dask.dataframe.DataFrame
  fs: FileSystem
  path: str
      Destination directory for data.  Prepend with protocol like ``s3://``
      or ``hdfs://`` for remote data.
  append: bool
      If True, may use existing metadata (if any) and perform checks
      against the new data being stored.
  partition_on: List(str)
      Column(s) to use for dataset partitioning in parquet.
  ignore_divisions: bool
      Whether or not to ignore old divisions when appending.  Otherwise,
      overlapping divisions will lead to an error being raised.
  division_info: dict
      Dictionary containing the divisions and corresponding column name.
  **kwargs: dict
      Other keyword arguments (including `index_cols`)
  
  Returns
  -------
  tuple:
      engine-specific instance
      list of filenames, one per partition
docstr_tok: ["Perform","engine-specific","initialization","steps","for","this","dataset","Parameters","--","--","--","--","--","df",":","dask.dataframe.DataFrame","fs",":","FileSystem","path",":","str","Destination","directory","for","data",".","Prepend","with","protocol","like","``","s3",":","\/\/","``","or","``","hdfs",":","\/\/","``","for","remote","data",".","append",":","bool","If","True",",","may","use","existing","metadata","(","if","any",")","and","perform","checks","against","the","new","data","being","stored",".","partition_on",":","List","(","str",")","Column","(","s",")","to","use","for","dataset","partitioning","in","parquet",".","ignore_divisions",":","bool","Whether","or","not","to","ignore","old","divisions","when","appending",".","Otherwise",",","overlapping","divisions","will","lead","to","an","error","being","raised",".","division_info",":","dict","Dictionary","containing","the","divisions","and","corresponding","column","name",".","*","*","kwargs",":","dict","Other","keyword","arguments","(","including","`","index_cols","`",")","Returns","--","--","--","-","tuple",":","engine-specific","instance","list","of","filenames",",","one","per","partition"]
code: def initialize_write(
      cls,
      df,
      fs,
      path,
      append=False,
      partition_on=None,
      ignore_divisions=False,
      division_info=None,
      **kwargs,
  ):
      """Perform engine-specific initialization steps for this dataset
  
      Parameters
      ----------
      df: dask.dataframe.DataFrame
      fs: FileSystem
      path: str
          Destination directory for data.  Prepend with protocol like ``s3://``
          or ``hdfs://`` for remote data.
      append: bool
          If True, may use existing metadata (if any) and perform checks
          against the new data being stored.
      partition_on: List(str)
          Column(s) to use for dataset partitioning in parquet.
      ignore_divisions: bool
          Whether or not to ignore old divisions when appending.  Otherwise,
          overlapping divisions will lead to an error being raised.
      division_info: dict
          Dictionary containing the divisions and corresponding column name.
      **kwargs: dict
          Other keyword arguments (including `index_cols`)
  
      Returns
      -------
      tuple:
          engine-specific instance
          list of filenames, one per partition
      """
      raise NotImplementedError
  
imports: from __future__ import annotations
  import re
  import warnings
  import numpy
  import pandas
  from fsspec.core import expand_paths_if_needed, get_fs_token_paths, stringify_path
  from fsspec.spec import AbstractFileSystem
  from dask import config
  from dask.dataframe.io.utils import _is_local_fs
  from dask.utils import natural_sort_key, parse_bytes
label_desc: Use image node_id 5 for calling the Engine obj's underlying member method code with example usage: obj.initialize_write(cls, df, fs, path, append, partition_on, ignore_divisions, division_info) without return types