docstr: This function trains a Voting Regressor for select models passed in the
  ``estimator_list`` param. The output of this function is a score grid with
  CV scores by fold. Metrics evaluated during CV can be accessed using the
  ``get_metrics`` function. Custom metrics can be added or removed using
  ``add_metric`` and ``remove_metric`` function.
  
  
  Example
  --------
  >>> from pycaret.datasets import get_data
  >>> boston = get_data('boston')
  >>> from pycaret.regression import *
  >>> exp_name = setup(data = boston,  target = 'medv')
  >>> top3 = compare_models(n_select = 3)
  >>> blender = blend_models(top3)
  
  
  estimator_list: list of scikit-learn compatible objects
      List of trained model objects
  
  
  fold: int or scikit-learn compatible CV generator, default = None
      Controls cross-validation. If None, the CV generator in the ``fold_strategy``
      parameter of the ``setup`` function is used. When an integer is passed,
      it is interpreted as the 'n_splits' parameter of the CV generator in the
      ``setup`` function.
  
  
  round: int, default = 4
      Number of decimal places the metrics in the score grid will be rounded to.
  
  
  choose_better: bool, default = False
      When set to True, the returned object is always better performing. The
      metric used for comparison is defined by the ``optimize`` parameter.
  
  
  optimize: str, default = 'R2'
      Metric to compare for model selection when ``choose_better`` is True.
  
  
  weights: list, default = None
      Sequence of weights (float or int) to weight the occurrences of predicted class
      labels (hard voting) or class probabilities before averaging (soft voting). Uses
      uniform weights when None.
  
  
  fit_kwargs: dict, default = {} (empty dict)
      Dictionary of arguments passed to the fit method of the model.
  
  
  groups: str or array-like, with shape (n_samples,), default = None
      Optional group labels when GroupKFold is used for the cross validation.
      It takes an array with shape (n_samples, ) where n_samples is the number
      of rows in training dataset. When string is passed, it is interpreted as
      the column name in the dataset containing group labels.
  
  
  verbose: bool, default = True
      Score grid is not printed when verbose is set to False.
  
  
  return_train_score: bool, default = False
      If False, returns the CV Validation scores only.
      If True, returns the CV training scores along with the CV validation scores.
      This is useful when the user wants to do bias-variance tradeoff. A high CV
      training score with a low corresponding CV validation score indicates overfitting.
  
  
  Returns:
      Trained Model
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code: def blend_models(
      self,
      estimator_list: list,
      fold: Optional[Union[int, Any]] = None,
      round: int = 4,
      choose_better: bool = False,
      optimize: str = "R2",
      weights: Optional[List[float]] = None,
      fit_kwargs: Optional[dict] = None,
      groups: Optional[Union[str, Any]] = None,
      verbose: bool = True,
      return_train_score: bool = False,
  ):
      """
      This function trains a Voting Regressor for select models passed in the
      ``estimator_list`` param. The output of this function is a score grid with
      CV scores by fold. Metrics evaluated during CV can be accessed using the
      ``get_metrics`` function. Custom metrics can be added or removed using
      ``add_metric`` and ``remove_metric`` function.
  
  
      Example
      --------
      >>> from pycaret.datasets import get_data
      >>> boston = get_data('boston')
      >>> from pycaret.regression import *
      >>> exp_name = setup(data = boston,  target = 'medv')
      >>> top3 = compare_models(n_select = 3)
      >>> blender = blend_models(top3)
  
  
      estimator_list: list of scikit-learn compatible objects
          List of trained model objects
  
  
      fold: int or scikit-learn compatible CV generator, default = None
          Controls cross-validation. If None, the CV generator in the ``fold_strategy``
          parameter of the ``setup`` function is used. When an integer is passed,
          it is interpreted as the 'n_splits' parameter of the CV generator in the
          ``setup`` function.
  
  
      round: int, default = 4
          Number of decimal places the metrics in the score grid will be rounded to.
  
  
      choose_better: bool, default = False
          When set to True, the returned object is always better performing. The
          metric used for comparison is defined by the ``optimize`` parameter.
  
  
      optimize: str, default = 'R2'
          Metric to compare for model selection when ``choose_better`` is True.
  
  
      weights: list, default = None
          Sequence of weights (float or int) to weight the occurrences of predicted class
          labels (hard voting) or class probabilities before averaging (soft voting). Uses
          uniform weights when None.
  
  
      fit_kwargs: dict, default = {} (empty dict)
          Dictionary of arguments passed to the fit method of the model.
  
  
      groups: str or array-like, with shape (n_samples,), default = None
          Optional group labels when GroupKFold is used for the cross validation.
          It takes an array with shape (n_samples, ) where n_samples is the number
          of rows in training dataset. When string is passed, it is interpreted as
          the column name in the dataset containing group labels.
  
  
      verbose: bool, default = True
          Score grid is not printed when verbose is set to False.
  
  
      return_train_score: bool, default = False
          If False, returns the CV Validation scores only.
          If True, returns the CV training scores along with the CV validation scores.
          This is useful when the user wants to do bias-variance tradeoff. A high CV
          training score with a low corresponding CV validation score indicates overfitting.
  
  
      Returns:
          Trained Model
  
  
      """
  
      return super().blend_models(
          estimator_list=estimator_list,
          fold=fold,
          round=round,
          choose_better=choose_better,
          optimize=optimize,
          method="auto",
          weights=weights,
          fit_kwargs=fit_kwargs,
          groups=groups,
          verbose=verbose,
          return_train_score=return_train_score,
      )
  
imports: import logging
  import re
  import time
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import pandas
  from joblib.memory import Memory
  from pycaret.containers.metrics.regression import get_all_metric_containers
  from pycaret.containers.models.regression import ALL_ALLOWED_ENGINES, get_all_model_containers, get_container_default_engines
  from pycaret.internal.display import CommonDisplay
  from pycaret.internal.logging import get_logger
  from pycaret.internal.parallel.parallel_backend import ParallelBackend
  from pycaret.internal.pipeline import Pipeline
  from pycaret.internal.preprocess.preprocessor import Preprocessor
  from pycaret.internal.pycaret_experiment.non_ts_supervised_experiment import _NonTSSupervisedExperiment
  from pycaret.loggers.base_logger import BaseLogger
  from pycaret.utils.constants import DATAFRAME_LIKE, SEQUENCE_LIKE, TARGET_LIKE
  from pycaret.utils.generic import MLUsecase, highlight_setup
label_desc: Use image node_id 10 for calling the RegressionExperiment obj's underlying member method code with example usage: obj.blend_models(estimator_list, fold, round, choose_better, optimize, weights, fit_kwargs, groups, verbose, return_train_score) and returns: super