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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 fit(self, X, y=None, **kwargs):
 """ The fit method is the primary drawing input for the UMAP projection since the visualization requires both X and an optional y value. The fit method expects an array of numeric vectors, so text documents must be vectorized before passing them to this method. 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 UMAP. 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. kwargs : dict Pass generic arguments to the drawing method Returns ------- self : instance Returns the instance of the transformer/visualizer """


        # Store the classes we observed in y
        if y is not None:
            self.classes_ = np.unique(y)
        elif y is None and self.labels is not None:
            self.classes_ = np.array([self.labels[0]])
        else:
            self.classes_ = np.array([self.NULL_CLASS])

        # Fit our internal transformer and transform the data.
        vecs = self.transformer_.fit_transform(X)
        self.n_instances_ = vecs.shape[0]

        # Draw the vectors
        self.draw(vecs, y, **kwargs)

        # Fit always returns self.
        return self