human
I wrote the below code

    def plot_with_labels(low_dim_embs, labels, figsize=(18, 18), second=5, saveable=True, name='tsne', fig_idx=9862):
        assert (low_dim_embs.shape[0] >= len(labels)), 'More    labels    than    embeddings'
        if (saveable is False):
            plt.ion()
            plt.figure(fig_idx)
        plt.figure(figsize=figsize)
        for (i, label) in enumerate(labels):
            (x, y) = low_dim_embs[i, :]
            plt.scatter(x, y)
            plt.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset    points', ha='right', va='bottom')
        if saveable:
            plt.savefig((name + '.pdf'), format='pdf')
        else:
            plt.draw()
            plt.pause(second)
    try:
        from sklearn.manifold import TSNE
        import matplotlib.pyplot as plt
        from six.moves import xrange
        tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
        low_dim_embs = tsne.fit_transform(embeddings[:plot_only, :])
        labels = [reverse_dictionary[i] for i in xrange(plot_only)]
        plot_with_labels(low_dim_embs, labels, second=second, saveable=saveable, name=name, fig_idx=fig_idx)
    except ImportError:
        print 'Please    install    sklearn    and    matplotlib    to    visualize    embeddings.'

What's a good function header?
assistant
#Here is Python code to print 'Please    install    sklearn    and    matplotlib    to    visualize    embeddings.

def tsne_embedding(embeddings, reverse_dictionary, plot_only=500, second=5, saveable=False, name='tsne', fig_idx=9862):