code: def to_csv(
      df,
      filename,
      single_file=False,
      encoding="utf-8",
      mode="wt",
      name_function=None,
      compression=None,
      compute=True,
      scheduler=None,
      storage_options=None,
      header_first_partition_only=None,
      compute_kwargs=None,
      **kwargs,
  ):
      """
      Store Dask DataFrame to CSV files
  
      One filename per partition will be created. You can specify the
      filenames in a variety of ways.
  
      Use a globstring::
  
      >>> df.to_csv('/path/to/data/export-*.csv')  # doctest: +SKIP
  
      The * will be replaced by the increasing sequence 0, 1, 2, ...
  
      ::
  
          /path/to/data/export-0.csv
          /path/to/data/export-1.csv
  
      Use a globstring and a ``name_function=`` keyword argument.  The
      name_function function should expect an integer and produce a string.
      Strings produced by name_function must preserve the order of their
      respective partition indices.
  
      >>> from datetime import date, timedelta
      >>> def name(i):
      ...     return str(date(2015, 1, 1) + i * timedelta(days=1))
  
      >>> name(0)
      '2015-01-01'
      >>> name(15)
      '2015-01-16'
  
      >>> df.to_csv('/path/to/data/export-*.csv', name_function=name)  # doctest: +SKIP
  
      ::
  
          /path/to/data/export-2015-01-01.csv
          /path/to/data/export-2015-01-02.csv
          ...
  
      You can also provide an explicit list of paths::
  
      >>> paths = ['/path/to/data/alice.csv', '/path/to/data/bob.csv', ...]  # doctest: +SKIP
      >>> df.to_csv(paths) # doctest: +SKIP
  
      You can also provide a directory name:
  
      >>> df.to_csv('/path/to/data') # doctest: +SKIP
  
      The files will be numbered 0, 1, 2, (and so on) suffixed with '.part':
  
      ::
  
          /path/to/data/0.part
          /path/to/data/1.part
  
      Parameters
      ----------
      df : dask.DataFrame
          Data to save
      filename : string or list
          Absolute or relative filepath(s). Prefix with a protocol like ``s3://``
          to save to remote filesystems.
      single_file : bool, default False
          Whether to save everything into a single CSV file. Under the
          single file mode, each partition is appended at the end of the
          specified CSV file.
      encoding : string, default 'utf-8'
          A string representing the encoding to use in the output file.
      mode : str, default 'w'
          Python file mode. The default is 'w' (or 'wt'), for writing
          a new file or overwriting an existing file in text mode. 'a'
          (or 'at') will append to an existing file in text mode or
          create a new file if it does not already exist. See :py:func:`open`.
      name_function : callable, default None
          Function accepting an integer (partition index) and producing a
          string to replace the asterisk in the given filename globstring.
          Should preserve the lexicographic order of partitions. Not
          supported when ``single_file`` is True.
      compression : string, optional
          A string representing the compression to use in the output file,
          allowed values are 'gzip', 'bz2', 'xz',
          only used when the first argument is a filename.
      compute : bool, default True
          If True, immediately executes. If False, returns a set of delayed
          objects, which can be computed at a later time.
      storage_options : dict
          Parameters passed on to the backend filesystem class.
      header_first_partition_only : bool, default None
          If set to True, only write the header row in the first output
          file. By default, headers are written to all partitions under
          the multiple file mode (``single_file`` is False) and written
          only once under the single file mode (``single_file`` is True).
          It must be True under the single file mode.
      compute_kwargs : dict, optional
          Options to be passed in to the compute method
      kwargs : dict, optional
          Additional parameters to pass to :meth:`pandas.DataFrame.to_csv`.
  
      Returns
      -------
      The names of the file written if they were computed right away.
      If not, the delayed tasks associated with writing the files.
  
      Raises
      ------
      ValueError
          If ``header_first_partition_only`` is set to False or
          ``name_function`` is specified when ``single_file`` is True.
  
      See Also
      --------
      fsspec.open_files
      """
      if single_file and name_function is not None:
          raise ValueError(
              "name_function is not supported under the single file mode"
          )
      if header_first_partition_only is None:
          header_first_partition_only = single_file
      elif not header_first_partition_only and single_file:
          raise ValueError(
              "header_first_partition_only cannot be False in the single file mode."
          )
      file_options = dict(
          compression=compression,
          encoding=encoding,
          newline="",
          **(storage_options or {}),
      )
      to_csv_chunk = delayed(_write_csv, pure=False)
      dfs = df.to_delayed()
      if single_file:
          first_file = open_file(filename, mode=mode, **file_options)
          value = to_csv_chunk(dfs[0], first_file, **kwargs)
          append_mode = mode if "a" in mode else mode + "a"
          append_mode = append_mode.replace("w", "").replace("x", "")
          append_file = open_file(
              filename, mode=append_mode, **file_options
          )
          kwargs["header"] = False
          for d in dfs[1:]:
              value = to_csv_chunk(
                  d, append_file, depend_on=value, **kwargs
              )
          values = [value]
          files = [first_file]
      else:
          files = open_files(
              filename,
              mode=mode,
              name_function=name_function,
              num=df.npartitions,
              **file_options,
          )
          values = [to_csv_chunk(dfs[0], files[0], **kwargs)]
          if header_first_partition_only:
              kwargs["header"] = False
          values.extend(
              [
                  to_csv_chunk(d, f, **kwargs)
                  for d, f in zip(dfs[1:], files[1:])
              ]
          )
      if compute:
          if compute_kwargs is None:
              compute_kwargs = dict()
  
          if scheduler is not None:
              warn(
                  "The 'scheduler' keyword argument for `to_csv()` is deprecated and"
                  "will be removed in a future version. "
                  "Please use the `compute_kwargs` argument instead. "
                  f"For example, df.to_csv(..., compute_kwargs={{scheduler: {scheduler}}})",
                  FutureWarning,
              )
  
          if (
              scheduler is not None
              and compute_kwargs.get("scheduler") is not None
              and compute_kwargs.get("scheduler") != scheduler
          ):
              raise ValueError(
                  f"Differing values for 'scheduler' have been passed in.\n"
                  f"scheduler argument: {scheduler}\n"
                  f"via compute_kwargs: {compute_kwargs.get('scheduler')}"
              )
  
          if (
              scheduler is not None
              and compute_kwargs.get("scheduler") is None
          ):
              compute_kwargs["scheduler"] = scheduler
  
          import dask
  
          return list(dask.compute(*values, **compute_kwargs))
      else:
          return values
  
imports: from __future__ import annotations
  import os
  from collections.abc import Mapping
  from io import BytesIO
  from warnings import catch_warnings, simplefilter, warn
  import numpy
  import pandas
  from fsspec.compression import compr
  from fsspec.core import get_fs_token_paths
  from fsspec.core import open
  from fsspec.core import open_files
  from fsspec.utils import infer_compression
  from pandas.api.types import CategoricalDtype, is_datetime64_any_dtype, is_float_dtype, is_integer_dtype, is_object_dtype
  from dask.base import tokenize
  from dask.bytes import read_bytes
  from dask.core import flatten
  from dask.dataframe.backends import dataframe_creation_dispatch
  from dask.dataframe.io.io import from_map
  from dask.dataframe.io.utils import DataFrameIOFunction
  from dask.dataframe.utils import clear_known_categories
  from dask.delayed import delayed
  from dask.utils import asciitable, parse_bytes
  from dask.dataframe.core import _Frame
label_desc: Use image node_id 11 for calling a global function with example usage: to_csv(df, filename, single_file, encoding, mode, name_function, compression, compute, scheduler, storage_options, header_first_partition_only, compute_kwargs) and returns: list, values