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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 tsne(X, y=None, ax=None, decompose='svd', decompose_by=50, classes=None, colors=None, colormap=None, alpha=0.7, **kwargs):
 """ Display a projection of a vectorized corpus in two dimensions using TSNE, a nonlinear dimensionality reduction method that is particularly well suited to embedding in two or three dimensions for visualization as a scatter plot. TSNE is widely used in text analysis to show clusters or groups of documents or utterances and their relative proximities. Parameters X : ndarray or DataFrame of shape n x m A matrix of n instances with m features representing the corpus of vectorized documents to visualize with tsne. y : ndarray or Series of length n An optional array or series of target or class values for instances. If this is specified, then the points will be colored according to their class. Often cluster labels are passed in to color the documents in cluster space, so this method is used both for classification and clustering methods. ax : matplotlib axes The axes to plot the figure on. decompose : string or None A preliminary decomposition is often used prior to TSNE to make the projection faster. Specify `"svd"` for sparse data or `"pca"` for dense data. If decompose is None, the original data set will be used. decompose_by : int Specify the number of components for preliminary decomposition, by default this is 50; the more components, the slower TSNE will be. classes : list of strings The names of the classes in the target, used to create a legend. colors : list or tuple of colors Specify the colors for each individual class colormap : string or matplotlib cmap Sequential colormap for continuous target alpha : float, default: 0.7 Specify a transparency where 1 is completely opaque and 0 is completely transparent. This property makes densely clustered points more visible. kwargs : dict Pass any additional keyword arguments to the TSNE transformer. Returns ------- ax : matplotlib axes Returns the axes that the parallel coordinates were drawn on. """

    # Instantiate the visualizer
    visualizer = TSNEVisualizer(
        ax, decompose, decompose_by, classes, colors, 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