class_docstr: FugueBackend for Distributed Computation.
  [Source code](https://github.com/Nixtla/statsforecast/blob/main/statsforecast/distributed/fugue.py).
  
  This class uses [Fugue](https://github.com/fugue-project/fugue) backend capable of distributing
  computation on Spark, Dask and Ray without any rewrites.
  
  Parameters
  ----------
  engine : fugue.ExecutionEngine
      A selection between Spark, Dask, and Ray.
  conf : fugue.Config
      Engine configuration.
  **transform_kwargs
      Additional kwargs for Fugue's transform method.
  
  Notes
  -----
  A short introduction to Fugue, with examples on how to scale pandas code to Spark, Dask or Ray
   is available [here](https://fugue-tutorials.readthedocs.io/tutorials/quick_look/ten_minutes.html).
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code: def __getstate__(self) -> Dict[str, Any]:
      return {}
  
imports: import inspect
  from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
  import cloudpickle
  import fugue.api
  import numpy
  import pandas
  from fugue import transform, DataFrame, FugueWorkflow, ExecutionEngine, AnyDataFrame
  from fugue.collections.yielded import Yielded
  from fugue.constants import FUGUE_CONF_WORKFLOW_EXCEPTION_INJECT
  from triad import Schema
  from statsforecast.core import _StatsForecast, ParallelBackend, _id_as_idx, _param_descriptions, make_backend
  from ..utils import ConformalIntervals
label_desc: Use image node_id 2 for calling the FugueBackend obj's underlying member method code with example usage: obj.__getstate__() and returns: dict