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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 pca_decomposition(X, y=None, ax=None, features=None, scale=True, proj_dim=2, proj_features=False, color=None, colormap=palettes.DEFAULT_SEQUENCE, alpha=0.75, random_state=None, **kwargs):
 """Produce a two or three dimensional principal component plot of the data array ``X`` projected onto it's largest sequential principal components. It is common practice to scale the data array ``X`` before applying a PC decomposition. Variable scaling can be controlled using the ``scale`` argument. 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. If None is passed in the current axes. will be used (or generated if required). features: list, default: None a list of feature names to use If a DataFrame is passed to fit and features is None, feature names are selected as the columns of the DataFrame. scale : bool, default: True Boolean that indicates if user wants to scale data. proj_dim : int, default: 2 Dimension of the PCA visualizer. proj_features : bool, default: False Boolean that indicates if the user wants to project the features in the projected space. If True the plot will be similar to a biplot. color : list or tuple of colors, default: None Specify the colors for each individual class. colormap : string or cmap, default: None Optional string or matplotlib cmap to colorize lines. Use either color to colorize the lines on a per class basis or colormap to color them on a continuous scale. alpha : float, default: 0.75 Specify a transparency where 1 is completely opaque and 0 is completely transparent. This property makes densely clustered points more visible. random_state : int, RandomState instance or None, optional (default None) If input data is larger than 500x500 and the number of components to extract is lower than 80% of the smallest dimension of the data, then the more efficient `randomized` solver is enabled, this parameter sets the random state on this solver. kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. Examples -------- """

    # Instantiate the visualizer
    visualizer = PCADecomposition(
        ax=ax, features=features, scale=scale, proj_dim=proj_dim,
        proj_features=proj_features, color=color, colormap=colormap,
        alpha=alpha, random_state=random_state, **kwargs
    )

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

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