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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def decisionviz(model, X, y, colors=None, classes=None, features=None, show_scatter=True, step_size=0.0025, markers=None, pcolormesh_alpha=0.8, scatter_alpha=1.0, title=None, **kwargs):
 """DecisionBoundariesVisualizer is a bivariate data visualization algorithm that plots the decision boundaries of each class. This helper function is a quick wrapper to utilize the DecisionBoundariesVisualizers for one-off analysis. Parameters model : the Scikit-Learn estimator, required Should be an instance of a classifier, else the __init__ will return an error. x : matrix, required The feature name that corresponds to a column name or index postion in the matrix that will be plotted against the x-axis y : array, required The feature name that corresponds to a column name or index postion in the matrix that will be plotted against the y-axis classes : a list of class names for the legend, default: None If classes is None and a y value is passed to fit then the classes are selected from the target vector. features : list of strings, default: None The names of the features or columns show_scatter : boolean, default: True If boolean is True, then a scatter plot with points will be drawn on top of the decision boundary graph step_size : float percentage, default: 0.0025 Determines the step size for creating the numpy meshgrid that will later become the foundation of the decision boundary graph. The default value of 0.0025 means that the step size for constructing the meshgrid will be 0.25%% of differenes of the max and min of x and y for each feature. markers : iterable of strings, default: ,od*vh+ Matplotlib style markers for points on the scatter plot points pcolormesh_alpha : float, default: 0.8 Sets the alpha transparency for the meshgrid of model boundaries scatter_alpha : float, default: 1.0 Sets the alpha transparency for the scatter plot points title : string, default: stringified feature_one and feature_two Sets the title of the visualization kwargs : keyword arguments passed to the super class. Returns ------- ax : matplotlib axes Returns the axes that the decision boundaries graph were drawn on. """

    # Instantiate the visualizer
    visualizer = DecisionBoundariesVisualizer(model,
                    X,
                    y,
                    colors=colors,
                    classes=classes,
                    features=features,
                    show_scatter=show_scatter,
                    step_size=step_size,
                    markers=markers,
                    pcolormesh_alpha=pcolormesh_alpha,
                    scatter_alpha=scatter_alpha,
                    title=title,
                    **kwargs)

    # Fit, draw and poof the visualizer
    visualizer.fit_draw_poof(X, y, **kwargs)

    # Return the axes object on the visualizer
    return visualizer.ax