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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_transform(self, traj):
 """Featurize an MD trajectory into a vector space via calculation of soft-bins over dihdral angle space. Parameters traj : mdtraj.Trajectory A molecular dynamics trajectory to featurize. Returns ------- features : np.ndarray, dtype=float, shape=(n_samples, n_features) A featurized trajectory is a 2D array of shape `(length_of_trajectory x n_features)` where each `features[i]` vector is computed by applying the featurization function to the `i`th snapshot of the input trajectory. See Also -------- transform : simultaneously featurize a collection of MD trajectories """

        x = []
        for a in self.types:
            func = getattr(md, 'compute_%s' % a)
            _, y = func(traj)
            res = vm.pdf(y[..., np.newaxis],
                         loc=self.loc, kappa=self.kappa)
            #we reshape the results using a  Fortran-like index order,
            #so that it goes over the columns first. This should put the results
            #phi dihedrals(all bin0 then all bin1), psi dihedrals(all_bin1)
            x.extend(np.reshape(res, (1, -1, self.n_bins*y.shape[1]), order='F'))
        return np.hstack(x)