code: def alphas(
      estimator,
      X,
      y=None,
      ax=None,
      is_fitted="auto",
      show=True,
      **kwargs,
  ):
      """Quick Method:
      The 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 : a Scikit-Learn regressor
          Should be an instance of a regressor, and specifically one whose name
          ends with "CV" otherwise a will raise a YellowbrickTypeError exception
          on instantiation. To use non-CV regressors see: ``ManualAlphaSelection``.
          If the estimator is not fitted, it is fit when the visualizer is fitted,
          unless otherwise specified by ``is_fitted``.
  
      X  : ndarray or DataFrame of shape n x m
          A matrix of n instances with m features.
  
      y  : ndarray or Series of length n
          An array or series of target values.
  
      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).
  
      is_fitted : bool or str, default='auto'
          Specify if the wrapped estimator is already fitted. If False, the estimator
          will be fit when the visualizer is fit, otherwise, the estimator will not be
          modified. If 'auto' (default), a helper method will check if the estimator
          is fitted before fitting it again.
  
      show : bool, default: True
          If True, calls ``show()``, which in turn calls ``plt.show()`` however
          you cannot call ``plt.savefig`` from this signature, nor
          ``clear_figure``. If False, simply calls ``finalize()``
  
      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 = AlphaSelection(
          estimator, ax, is_fitted=is_fitted, **kwargs
      )
  
      visualizer.fit(X, y)
      visualizer.score(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 1 for calling a global function with example usage: alphas(estimator, X, y, ax, is_fitted, show) and returns: visualizer