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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 interpolate(self, traj, ti, k=3, der=0, ext=2):
 r""" Parametric B-spline interpolation in N-dimensions. Parameters traj : array_like (float) Solution trajectory providing the data points for constructing the B-spline representation. ti : array_like (float) Array of values for the independent variable at which to interpolate the value of the B-spline. k : int, optional(default=3) Degree of the desired B-spline. Degree must satisfy :math:`1 \le k \le 5`. der : int, optional(default=0) The order of derivative of the spline to compute (must be less than or equal to `k`). ext : int, optional(default=2) Controls the value of returned elements for outside the original knot sequence provided by traj. For extrapolation, set `ext=0`; `ext=1` returns zero; `ext=2` raises a `ValueError`. Returns ------- interp_traj: ndarray (float) The interpolated trajectory. """

        # array of parameter values
        u = traj[:, 0]

        # build list of input arrays
        n = traj.shape[1]
        x = [traj[:, i] for i in range(1, n)]

        # construct the B-spline representation (s=0 forces interpolation!)
        tck, t = interpolate.splprep(x, u=u, k=k, s=0)

        # evaluate the B-spline (returns a list)
        out = interpolate.splev(ti, tck, der, ext)

        # convert to a 2D array
        interp_traj = np.hstack((ti[:, np.newaxis], np.array(out).T))

        return interp_traj