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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 partial_dependence(self, term, X=None, width=None, quantiles=None, meshgrid=False):
 """ Computes the term functions for the GAM and possibly their confidence intervals. if both width=None and quantiles=None, then no confidence intervals are computed Parameters term : int, optional Term for which to compute the partial dependence functions. X : array-like with input data, optional if `meshgrid=False`, then `X` should be an array-like of shape (n_samples, m_features). if `meshgrid=True`, then `X` should be a tuple containing an array for each feature in the term. if None, an equally spaced grid of points is generated. width : float on (0, 1), optional Width of the confidence interval. quantiles : array-like of floats on (0, 1), optional instead of specifying the prediciton width, one can specify the quantiles. so width=.95 is equivalent to quantiles=[.025, .975]. if None, defaults to width. meshgrid : bool, whether to return and accept meshgrids. Useful for creating outputs that are suitable for 3D plotting. Note, for simple terms with no interactions, the output of this function will be the same for ``meshgrid=True`` and ``meshgrid=False``, but the inputs will need to be different. Returns ------- pdeps : np.array of shape (n_samples,) conf_intervals : list of length len(term) containing np.arrays of shape (n_samples, 2 or len(quantiles)) Raises ------ ValueError : If the term requested is an intercept since it does not make sense to process the intercept term. See Also -------- generate_X_grid : for help creating meshgrids. """

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

        if not isinstance(term, int):
            raise ValueError('term must be an integer, but found term: {}'.format(term))

        # ensure term exists
        if (term >= len(self.terms)) or (term < -1):
            raise ValueError('Term {} out of range for model with {} terms'\
                             .format(term, len(self.terms)))

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

        if X is None:
            X = self.generate_X_grid(term=term, meshgrid=meshgrid)

        if meshgrid:
            if not isinstance(X, tuple):
                raise ValueError('X must be a tuple of grids if `meshgrid=True`, '\
                                 'but found X: {}'.format(X))
            shape = X[0].shape

            X = self._flatten_mesh(X, term=term)
            X = check_X(X, n_feats=self.statistics_['m_features'],
                        edge_knots=self.edge_knots_, dtypes=self.dtype,
                        features=self.feature, verbose=self.verbose)

        modelmat = self._modelmat(X, term=term)
        pdep = self._linear_predictor(modelmat=modelmat, term=term)
        out = [pdep]

        compute_quantiles = (width is not None) or (quantiles is not None)
        if compute_quantiles:
            conf_intervals = self._get_quantiles(X, width=width,
                                                 quantiles=quantiles,
                                                 modelmat=modelmat,
                                                 lp=pdep,
                                                 term=term,
                                                 xform=False)

            out += [conf_intervals]

        if meshgrid:
            for i, array in enumerate(out):
                # add extra dimensions arising from multiple confidence intervals
                if array.ndim > 1:
                    depth = array.shape[-1]
                    shape += (depth,)
                out[i] = np.reshape(array, shape)

        if compute_quantiles:
            return out

        return out[0]