code: def _copy_partial_doc(cls):
      for base in cls.mro():
          if base.__module__.startswith("sklearn"):
              break
      lines = base.__doc__.split(os.linesep)
      header, rest = lines[0], lines[1:]
  
      insert = """
  
      .. deprecated:: 0.6.0
         Use the :class:`dask_ml.wrappers.Incremental` meta-estimator instead.
  
      This class wraps scikit-learn's {classname}. When a dask-array is passed
      to our ``fit`` method, the array is passed block-wise to the scikit-learn
      class' ``partial_fit`` method. This will allow you to fit the estimator
      on larger-than memory datasets sequentially (block-wise), but without an
      parallelism, or any ability to distribute across a cluster.""".format(
          classname=base.__name__
      )
  
      doc = "\n".join([header + insert] + rest)
  
      cls.__doc__ = doc
      return cls
  
file_path: dask-ml/dask_ml/_partial.py
imports: from __future__ import absolute_import, division, print_function
  import logging
  import os
  import dask
  import numpy
  import sklearn.utils
  from dask.delayed import Delayed
  from dask.highlevelgraph import HighLevelGraph
  from toolz import partial
  from ._compat import DASK_2022_01_0
label_desc: Use image node_id 6 for calling a global function with example usage: _copy_partial_doc(cls) and returns: cls