docstr: Constructor for distributed estimators.
docstr_tok: ["Constructor","for","distributed","estimators","."]
code: def __init__(self, *, client=None, verbose=False, **kwargs):
      """
      Constructor for distributed estimators.
      """
      self.client = get_client(client)
  
      # set client verbosity
      self.verbose = verbose
  
      # kwargs transmits the verbosity level to workers
      kwargs["verbose"] = verbose
      self.kwargs = kwargs
  
      self.internal_model = None
  
imports: from distributed.client import Future
  from functools import wraps
  from dask_cudf.core import Series
  from cuml.internals.safe_imports import gpu_only_import_from
  from cuml.internals.base import Base
  from cuml.internals import BaseMetaClass
  from cuml.dask.common import parts_to_ranks
  from cuml.dask.common.input_utils import DistributedDataHandler
  from raft_dask.common.comms import Comms
  from cuml.dask.common.utils import wait_and_raise_from_futures
  from cuml.internals.array import CumlArray
  from cuml.dask.common.utils import get_client
  from collections.abc import Iterable
  from toolz import first
  from cuml.internals.safe_imports import cpu_only_import
  import dask
  import cudf.comm.serialize
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 1 to create a new BaseEstimator object from inherited base classes: object with example: obj = BaseEstimator()