code: def manual_alphas(
      estimator,
      X,
      y=None,
      ax=None,
      alphas=None,
      cv=None,
      scoring=None,
      show=True,
      **kwargs,
  ):
      """Quick Method:
      The Manual Alpha Selection Visualizer demonstrates how different values of alpha
      influence model selection during the regularization of linear models.
      Generally speaking, alpha increases the affect of regularization, e.g. if
      alpha is zero there is no regularization and the higher the alpha, the
      more the regularization parameter influences the final model.
  
      Parameters
      ----------
  
      estimator : an unfitted Scikit-Learn regressor
          Should be an instance of an unfitted regressor, and specifically one
          whose name doesn't end with "CV". The regressor must support a call to
          ``set_params(alpha=alpha)`` and be fit multiple times. If the
          regressor name ends with "CV" a ``YellowbrickValueError`` is raised.
  
      ax : matplotlib Axes, default: None
          The axes to plot the figure on. If None is passed in the current axes
          will be used (or generated if required).
  
      alphas : ndarray or Series, default: np.logspace(-10, 2, 200)
          An array of alphas to fit each model with
  
      cv : int, cross-validation generator or an iterable, optional
          Determines the cross-validation splitting strategy.
          Possible inputs for cv are:
  
          - None, to use the default 3-fold cross validation,
          - integer, to specify the number of folds in a `(Stratified)KFold`,
          - An object to be used as a cross-validation generator.
          - An iterable yielding train, test splits.
  
          This argument is passed to the
          ``sklearn.model_selection.cross_val_score`` method to produce the
          cross validated score for each alpha.
  
      scoring : string, callable or None, optional, default: None
          A string (see model evaluation documentation) or
          a scorer callable object / function with signature
          ``scorer(estimator, X, y)``.
  
          This argument is passed to the
          ``sklearn.model_selection.cross_val_score`` method to produce the
          cross validated score for each alpha.
  
      kwargs : dict
          Keyword arguments that are passed to the base class and may influence
          the visualization as defined in other Visualizers.
  
      Returns
      -------
      visualizer : AlphaSelection
          Returns the alpha selection visualizer
      """
      # Instantiate the visualizer
      visualizer = ManualAlphaSelection(
          estimator, ax, alphas=alphas, scoring=scoring, cv=cv, **kwargs
      )
  
      visualizer.fit(X, y)
  
      if show:
          visualizer.show()
      else:
          visualizer.finalize()
  
      # Return the visualizer
      return visualizer
  
imports: import numpy
  from functools import partial
  from yellowbrick.exceptions import YellowbrickTypeError
  from yellowbrick.exceptions import YellowbrickValueError
  from yellowbrick.regressor.base import RegressionScoreVisualizer
  from sklearn.model_selection import cross_val_score
label_desc: Use image node_id 2 for calling a global function with example usage: manual_alphas(estimator, X, y, ax, alphas, cv, scoring, show) and returns: visualizer