class_docstr: Fit a quick model using AutoGluon.
  
  `train_data`, `val_data` and `label` must be present in args.
  
  Note: this component can be wrapped into :py:class:`~autogluon.eda.analysis.dataset.TrainValidationSplit` and `~autogluon.eda.analysis.dataset.Sampler`
  to perform automated sampling and train-test split. This whole logic is implemented in :py:meth:`~autogluon.eda.auto.simple.quick_fit` shortcut.
  
  Examples
  --------
  >>> import autogluon.eda.analysis as eda
  >>>
  >>> # Quick fit
  >>> state = auto.quick_fit(
  >>>     train_data=..., label=...,
  >>>     return_state=True,  # return state object from call
  >>>     hyperparameters={'GBM': {}}  # train specific model
  >>> )
  >>>
  >>> # Using quick fit model
  >>> model = state.model
  >>> y_pred = model.predict(test_data)
  
  Parameters
  ----------
  problem_type: str, default = 'auto'
      problem type to use. Valid problem_type values include ['auto', 'binary', 'multiclass', 'regression', 'quantile', 'softclass']
      auto means it will be Auto-detected using AutoGluon methods.
  estimator_args: Optional[Dict[str, Any]], default = None,
      kwargs to pass into estimator constructor (`TabularPredictor`)
  save_model_to_state: bool, default = True,
      save fitted model into `state` under `model` key.
      This functionality might be helpful in cases when the fitted model could be usable for other purposes (i.e. imputers)
  parent: Optional[AbstractAnalysis], default = None
      parent Analysis
  children: Optional[List[AbstractAnalysis]], default None
      wrapped analyses; these will receive sampled `args` during `fit` call
  kwargs
  
  See Also
  --------
  :py:meth:`~autogluon.eda.auto.simple.quick_fit`
  :py:class:`~autogluon.eda.analysis.dataset.TrainValidationSplit`
  :py:class:`~autogluon.eda.analysis.dataset.Sampler`
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code: def _fit(
      self, state: AnalysisState, args: AnalysisState, **fit_kwargs
  ) -> None:
      estimator: TabularPredictor = TabularPredictor(
          label=args.label,
          problem_type=self.problem_type,
          **self.estimator_args,
      )
      estimator.fit(train_data=args.train_data, **self.args)
      self.args["model"] = estimator
  
      if self.save_model_to_state:
          state["model"] = estimator
  
file_path: autogluon/eda/src/autogluon/eda/analysis/model.py
imports: from typing import Any, Dict, List, Optional, Union
  import numpy
  import pandas
  from sklearn.metrics import confusion_matrix
  from autogluon.core.constants import BINARY, MULTICLASS, PROBLEM_TYPES_CLASSIFICATION, PROBLEM_TYPES_REGRESSION, REGRESSION
  from autogluon.tabular import TabularPredictor
  from .base import AbstractAnalysis, AnalysisState
label_desc: Use image node_id 3 for calling the AutoGluonModelQuickFit obj's underlying member method code with example usage: obj._fit(state, args) without return types