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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, categorical=None):
 """Compute k-prototypes clustering. Parameters X : array-like, shape=[n_samples, n_features] categorical : Index of columns that contain categorical data """

        if categorical is not None:
            assert isinstance(categorical, (int, list, tuple)), "The 'categorical' \
                argument needs to be an integer with the index of the categorical \
                column in your data, or a list or tuple of several of them, \
                but it is a {}.".format(type(categorical))

        X = pandas_to_numpy(X)

        random_state = check_random_state(self.random_state)
        # If self.gamma is None, gamma will be automatically determined from
        # the data. The function below returns its value.
        self._enc_cluster_centroids, self._enc_map, self.labels_, self.cost_,\
            self.n_iter_, self.gamma = k_prototypes(X,
                                                    categorical,
                                                    self.n_clusters,
                                                    self.max_iter,
                                                    self.num_dissim,
                                                    self.cat_dissim,
                                                    self.gamma,
                                                    self.init,
                                                    self.n_init,
                                                    self.verbose,
                                                    random_state,
                                                    self.n_jobs)
        return self