class_docstr: Anomaly detection analysis.
  
  The analysis automatically creates cross-validation splits and fits detectors on each of them using
  `train_data` input. The scores produced for the training data are produced using out-of-folds predictions.
  All other datasets scores are produced using average of scores from detectors trained on individual folds (bag).
  
  Please note, the analysis expects the data is ready to for fitting; all numeric columns must not have NaNs.
  Pre-processing can be performed using :py:class:`~autogluon.eda.analysis.transform.ApplyFeatureGenerator`
  and :py:class:`~autogluon.eda.analysis.dataset.ProblemTypeControl` (see example for more details).
  
  State attributes
  
  - `anomaly_detection.scores.<dataset>`
      scores for each of the datasets passed into analysis (i.e. `train_data`, `test_data`)
  - `anomaly_detection.explain_rows_fns.<dataset>`
      if `store_explainability_data=True`, then analysis will store helper functions into this
      variable. The function can be used later via :py:meth:`~autogluon.eda.auto.simple.explain_rows`
      and automatically pre-populates `train_data`, `model` and `rows` parameters when called
      (see example for more details)
  
  
  Parameters
  ----------
  n_folds: int, default = 5
      number of folds to use when training detectors; default is 5 folds.
  store_explainability_data: bool, default = False
      if `True` analysis will store helper functions into this variable.
      The function can be used later via :py:meth:`~autogluon.eda.auto.simple.explain_rows`
      and automatically pre-populates `train_data`, `model` and `rows` parameters when called
      (see example for more details)
  parent: Optional[AbstractAnalysis], default = None
      parent Analysis
  children: List[AbstractAnalysis], default = []
      wrapped analyses; these will receive sampled `args` during `fit` call
  state: Optional[AnalysisState], default = None
      state to be updated by this fit function
  anomaly_detector_kwargs
      kwargs for :py:class:`~autogluon.eda.analysis.anomaly.AnomalyDetector`
  
  
  See Also
  --------
  :py:class:`~autogluon.eda.analysis.anomaly.AnomalyDetector`
  :py:class:`~autogluon.eda.visualization.anomaly.AnomalyScoresVisualization`
  :py:class:`~autogluon.eda.analysis.transform.ApplyFeatureGenerator`
  :py:class:`~autogluon.eda.analysis.dataset.ProblemTypeControl`
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code: def _create_detector(self, args) -> AnomalyDetector:
      return AnomalyDetector(
          label=args.label, n_folds=self.n_folds, **self.args
      )
  
imports: from __future__ import print_function
  import builtins
  import contextlib
  import logging
  from functools import partial
  from typing import Any, Dict, List, Optional
  import joblib
  import numpy
  import pandas
  from pyod.models.base import BaseDetector
  from pyod.models.copod import COPOD
  from pyod.models.iforest import IForest
  from pyod.models.lof import LOF
  from pyod.models.suod import SUOD
  from autogluon.common.utils.resource_utils import ResourceManager
  from autogluon.core.utils import CVSplitter
  from ..None import AnalysisState
  from .base import AbstractAnalysis
label_desc: Use image node_id 4 for calling the AnomalyDetectorAnalysis obj's underlying member method code with example usage: obj._create_detector(args) and returns: AnomalyDetector