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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 generate_X_grid(self, term, n=100, meshgrid=False):
 """create a nice grid of X data array is sorted by feature and uniformly spaced, so the marginal and joint distributions are likely wrong if term is >= 0, we generate n samples per feature, which results in n^deg samples, where deg is the degree of the interaction of the term Parameters term : int, Which term to process. n : int, optional number of data points to create meshgrid : bool, optional Whether to return a meshgrid (useful for 3d plotting) or a feature matrix (useful for inference like partial predictions) Returns ------- if meshgrid is False: np.array of shape (n, n_features) where m is the number of (sub)terms in the requested (tensor)term. else: tuple of len m, where m is the number of (sub)terms in the requested (tensor)term. each element in the tuple contains a np.ndarray of size (n)^m Raises ------ ValueError : If the term requested is an intercept since it does not make sense to process the intercept term. """

        if not self._is_fitted:
            raise AttributeError('GAM has not been fitted. Call fit first.')

        # cant do Intercept
        if self.terms[term].isintercept:
            raise ValueError('cannot create grid for intercept term')

        # process each subterm in a TensorTerm
        if self.terms[term].istensor:
            Xs = []
            for term_ in self.terms[term]:
                Xs.append(np.linspace(term_.edge_knots_[0],
                                      term_.edge_knots_[1],
                                      num=n))

            Xs = np.meshgrid(*Xs, indexing='ij')
            if meshgrid:
                return tuple(Xs)
            else:
                return self._flatten_mesh(Xs, term=term)

        # all other Terms
        elif hasattr(self.terms[term], 'edge_knots_'):
            x = np.linspace(self.terms[term].edge_knots_[0],
                            self.terms[term].edge_knots_[1],
                            num=n)

            if meshgrid:
                return (x,)

            # fill in feature matrix with only relevant features for this term
            X = np.zeros((n, self.statistics_['m_features']))
            X[:, self.terms[term].feature] = x
            if getattr(self.terms[term], 'by', None) is not None:
                X[:, self.terms[term].by] = 1.

            return X

        # dont know what to do here
        else:
            raise TypeError('Unexpected term type: {}'.format(self.terms[term]))