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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 radviz(X, y=None, ax=None, features=None, classes=None, color=None, colormap=None, alpha=1.0, **kwargs):
 """ Displays each feature as an axis around a circle surrounding a scatter plot whose points are each individual instance. This helper function is a quick wrapper to utilize the RadialVisualizer (Transformer) for one-off analysis. Parameters 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 or class values ax : matplotlib Axes, default: None The axes to plot the figure on. features : list of strings, default: None The names of the features or columns classes : list of strings, default: None The names of the classes in the target color : list or tuple of colors, default: None Specify the colors for each individual class colormap : string or matplotlib cmap, default: None Sequential colormap for continuous target alpha : float, default: 1.0 Specify a transparency where 1 is completely opaque and 0 is completely transparent. This property makes densely clustered points more visible. Returns ------- ax : matplotlib axes Returns the axes that the parallel coordinates were drawn on. """

    # Instantiate the visualizer
    visualizer = RadialVisualizer(
        ax, features, classes, color, colormap, alpha, **kwargs
    )

    # Fit and transform the visualizer (calls draw)
    visualizer.fit(X, y, **kwargs)
    visualizer.transform(X)

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