code: def partial_transform(self, traj):
          
  
          ca = [a.index for a in traj.top.atoms if a.name == 'CA']
          if len(ca) < 4:
              return np.zeros((len(traj), 0), dtype=np.float32)
  
          alpha_indices = np.array(
              [(ca[i - 1], ca[i], ca[i + 1], ca[i + 2]) for i in range(1, len(ca) - 2)])
          result = md.compute_dihedrals(traj, alpha_indices)
  
          x = []
          if self.atom_indices is None:
              self.atom_indices = np.vstack(alpha_indices)
          if self.sincos:
              x.extend([np.cos(result), np.sin(result)])
          else:
              x.append(result)
          return np.hstack(x)
docs: Featurize an MD trajectory into a vector space via calculation
          of dihedral (torsion) angles of alpha carbon backbone
  
          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.