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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 dispersion visualization. Parameters X : list or generator Should be provided as a list of documents or a generator that yields a list of documents that contain a list of words in the order they appear in the document. 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. kwargs : dict Pass generic arguments to the drawing method Returns ------- self : instance Returns the instance of the transformer/visualizer """


        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])

        # Create an index (e.g. the y position) for the target words
        self.indexed_words_ = np.flip(self.target_words, axis=0)
        if self.ignore_case:
            self.indexed_words_ = np.array([w.lower() for w in self.indexed_words_])

        # Stack is used to create a 2D array from the generator
        try:
            points_target = np.stack(self._compute_dispersion(X, y))
        except ValueError:
            raise YellowbrickValueError((
                "No indexed words were found in the corpus"
                ))
        points = np.stack(zip(points_target[:,0].astype(int),
                              points_target[:,1].astype(int)))

        self.target = points_target[:,2]

        self._check_missing_words(points)

        self.draw(points, self.target)
        return self