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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 transform(self, traj):
 """ Maps an mdtraj Trajectory object to the selected output features Parameters traj : mdtraj Trajectory Trajectory object used as an input Returns ------- out : ndarray((T, n), dtype=float32) Output features: For each of T time steps in the given trajectory, a vector with all n output features selected. """

        # if there are no features selected, return given trajectory
        if not self.active_features:
            self.add_selection(np.arange(self.topology.n_atoms))
            warnings.warn("You have not selected any features. Returning plain coordinates.")

        # otherwise build feature vector.
        feature_vec = []

        # TODO: consider parallel evaluation computation here, this effort is
        # only worth it, if computation time dominates memory transfers
        for f in self.active_features:
            # perform sanity checks for custom feature input
            if isinstance(f, CustomFeature):
                # NOTE: casting=safe raises in numpy>=1.9
                vec = f.transform(traj).astype(np.float32, casting='safe')
                if vec.shape[0] == 0:
                    vec = np.empty((0, f.dimension))

                if not isinstance(vec, np.ndarray):
                    raise ValueError('Your custom feature %s did not return'
                                     ' a numpy.ndarray!' % str(f.describe()))
                if not vec.ndim == 2:
                    raise ValueError('Your custom feature %s did not return'
                                     ' a 2d array. Shape was %s'
                                     % (str(f.describe()),
                                        str(vec.shape)))
                if not vec.shape[0] == traj.xyz.shape[0]:
                    raise ValueError('Your custom feature %s did not return'
                                     ' as many frames as it received!'
                                     'Input was %i, output was %i'
                                     % (str(f.describe()),
                                        traj.xyz.shape[0],
                                        vec.shape[0]))
            else:
                vec = f.transform(traj).astype(np.float32)
            feature_vec.append(vec)

        if len(feature_vec) > 1:
            res = np.hstack(feature_vec)
        else:
            res = feature_vec[0]

        return res