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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 solvent fingerprints 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 """

        # The result vector
        fingerprints = np.zeros((traj.n_frames, self.n_features))
        atom_pairs = np.zeros((len(self.solvent_indices), 2))
        sigma = self.sigma

        for i, solute_i in enumerate(self.solute_indices):
            # For each solute atom, calculate distance to all solvent
            # molecules
            atom_pairs[:, 0] = solute_i
            atom_pairs[:, 1] = self.solvent_indices

            distances = md.compute_distances(traj, atom_pairs, periodic=True)
            distances = np.exp(-distances / (2 * sigma * sigma))

            # Sum over water atoms for all frames
            fingerprints[:, i] = np.sum(distances, axis=1)

        return fingerprints