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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 featurize_all(filenames, featurizer, topology, chunk=1000, stride=1):
 """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]. """

    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)