code: def featurize_all(filenames, featurizer, topology, chunk=1000, stride=1):
      
      data = []
      indices = []
      fns = []
  
      for file in filenames:
          kwargs = {} if file.endswith('.h5') else {'top': topology}
          count = 0
          for t in md.iterload(file, chunk=chunk, stride=stride, **kwargs):
              x = featurizer.partial_transform(t)
              n_frames = len(x)
  
              data.append(x)
              indices.append(count + (stride * np.arange(n_frames)))
              fns.extend([file] * n_frames)
              count += (stride * n_frames)
      if len(data) == 0:
          raise ValueError("None!")
  
      return np.concatenate(data), np.concatenate(indices), np.array(fns)
docs: Load and featurize many trajectory files.
  
      Parameters
      ----------
      filenames : list of strings
          List of paths to MD trajectory files
      featurizer : Featurizer
          The featurizer to be invoked on each trajectory trajectory as
          it is loaded
      topology : str, Topology, Trajectory
          Topology or path to a topology file, used to load trajectories with
          MDTraj
      chunk : {int, None}
          If chunk is an int, load the trajectories up in chunks using
          md.iterload for better memory efficiency (less trajectory data needs
          to be in memory at once)
      stride : int, default=1
          Only read every stride-th frame.
  
      Returns
      -------
      data : np.ndarray, shape=(total_length_of_all_trajectories, n_features)
      indices : np.ndarray, shape=(total_length_of_all_trajectories)
      fns : np.ndarray shape=(total_length_of_all_trajectories)
          These three arrays all share the same indexing, such that data[i] is
          the featurized version of indices[i]-th frame in the MD trajectory
          with filename fns[i].