How to use the dill.dump function in dill

for epoch in range(args.epochs):
        print('epoch=%d | train' % epoch, file=sys.stderr)
        _ = train_epoch(model, train_iter, opt)
        
        print('epoch=%d | test' % epoch, file=sys.stderr)
        preds = pd.DataFrame(list(test(model, test_iter)))
        print('acc=%f\n--' % (preds.act == preds.pred).mean(), file=sys.stderr)
    
    # --
    # Save model
    
    print("saving model to %s" % args.outpath, file=sys.stderr)
    torch.save(model.state_dict(), args.outpath + '.pt')
    json.dump((args.attention, model_config), open(args.outpath + '.json', 'w'))
    pickle.dump(dataset['vocabs'], open(args.outpath + '.vocab', 'w'))

niters = np.array([item[1] for item in iter_counts])
    out = '   Mean number of iterations: %4.2f' % np.mean(niters)
    print(out)
    out = '   Range of values for number of iterations: %2i ' % np.ptp(niters)
    print(out)
    out = '   Position of max/min number of iterations: %2i -- %2i' % \
          (int(np.argmax(niters)), int(np.argmin(niters)))
    print(out)
    out = '   Std and var for number of iterations: %4.2f -- %4.2f' % (float(np.std(niters)), float(np.var(niters)))
    print(out)

    assert np.mean(niters) <= maxmeaniter, 'Mean number of iterations is too high, got %s' % np.mean(niters)

    fname = 'data/' + prob + '.dat'
    f = open(fname, 'wb')
    dill.dump(stats, f)
    f.close()

    assert os.path.isfile(fname), 'Run for %s did not create stats file' % prob

for song in album_songs:
                song_path = os.path.join(album_path, song)

                # Create mel spectrogram and convert it to the log scale
                y, sr = librosa.load(song_path, sr=sr)
                S = librosa.feature.melspectrogram(y, sr=sr, n_mels=n_mels,
                                                   n_fft=n_fft,
                                                   hop_length=hop_length)
                log_S = librosa.logamplitude(S, ref_power=1.0)
                data = (artist, log_S, song)

                # Save each song
                save_name = artist + '_%%-%%_' + album + '_%%-%%_' + song
                with open(os.path.join(save_folder, save_name), 'wb') as fp:
                    dill.dump(data, fp)

func = module.FUNC
    args,kwds = pickle.load(open(argfilename,'rb'))

    if world.rank == 0:
        log.info('funcname: %s' % funcname)        # sys.argv[1]
        log.info('argfilename: %s' % argfilename)  # sys.argv[2] 
        log.info('outfilename: %s' % outfilename)  # sys.argv[3]
        log.info('workdir: %s' % workdir)          # sys.argv[4]
        log.info('func: %s' % func)
        log.info('args: %s' % str(args))
        log.info('kwds: %s' % str(kwds))
    res = parallel_map(func, *args, **kwds) #XXX: called on ALL nodes ?

    if world.rank == 0:
        log.info('res: %s' % str(res))
        pickle.dump(res, open(outfilename,'wb'))

print 'Binarizing with threshold value of ' + str(binary_threshold)
  inv_binary = cv2.bitwise_not(clean.binarize(gray, threshold=binary_threshold))
  binary = clean.binarize(gray, threshold=binary_threshold)

  segmented_image = seg.segment_image(gray)
  segmented_image = segmented_image[:,:,2]
  components = cc.get_connected_components(segmented_image)
  cc.draw_bounding_boxes(img,components,color=(255,0,0),line_size=2)
  imsave(outfile, img)

  blurbs = ocr.ocr_on_bounding_boxes(img, components)

  long_blurb = max(blurbs, key=lambda b: len(b.text))
  translated = translate.translate_blurb(long_blurb)
  pickle.dump(to_typeset, open("tts.pkl", mode="w"))
  pickle.dump(translated, open("blurb.pkl", mode="w"))  
  typeset.typeset_blurb(to_typeset, translated)
  to_typeset.show()

def dump_session(sess):
    temp = utils.tmp_dir()
    try:
        with open("{}/session.pkl".format(temp), "wb") as f:
            dill.dump(sess, f)
    except:
        utils.log_err("Dump session failed")

def to_dill(self, filename):
        import dill
        rubi = self.load()
        with open(filename, "wb") as fout:
            dill.dump(rubi, fout)

print("Distributed dual subgradient")
_, dds_seq = dds.run(iterations=num_iterations, stepsize=step_gen, verbose=True)

if agent_id == 0:
    print("Distributed primal decomposition")
dpd_seq, _ = dpd.run(iterations=num_iterations, stepsize=step_gen, M=30.0, verbose=True)

# save information
if agent_id == 0:
    with open('info.pkl', 'wb') as output:
        pickle.dump({'N': NN, 'iterations': num_iterations, 'n_coupling': TT}, output, pickle.HIGHEST_PROTOCOL)

with open('agent_{}_objective_func.pkl'.format(agent_id), 'wb') as output:
    pickle.dump(obj_func, output, pickle.HIGHEST_PROTOCOL)
with open('agent_{}_coupling_func.pkl'.format(agent_id), 'wb') as output:
    pickle.dump(coupling_func, output, pickle.HIGHEST_PROTOCOL)
with open('agent_{}_local_constr.pkl'.format(agent_id), 'wb') as output:
    pickle.dump(constr, output, pickle.HIGHEST_PROTOCOL)
np.save("agent_{}_seq_dds.npy".format(agent_id), dds_seq)
np.save("agent_{}_seq_dpd.npy".format(agent_id), dpd_seq)

def cache_bvp(self, problem, filename=None):
        """
        \brief Saves BVP object into file on disk
        Arguments:
            problem : Problem object
            filename: Full path to cache file (optional)
                      default value: _bvp.dat
        \date  01/27/2016
        """
        if filename is None:
            filename = problem.name+'_bvp.dat'

        with open(filename,'wb') as f:
            try:
                logging.info('Caching BVP information to file')
                bvp = dill.dump(self.bvp,f)
                return True
            except:
                logging.warn('Failed to save BVP to '+filename)
                return False

# filter statistics by type (number of iterations)
            filtered_stats = filter_stats(stats, type='niter')

            # convert filtered statistics to list of iterations count, sorted by process
            iter_counts = sort_stats(filtered_stats, sortby='time')

            niters[i] = np.mean(np.array([item[1] for item in iter_counts]))

            # print('Worked on k = %s, took %s iterations' % (k, results[i]))

        results[qd] = niters

    fname = 'data/harmonic_k.dat'
    f = open(fname, 'wb')
    dill.dump(results, f)
    f.close()

    assert os.path.isfile(fname), 'Run for %s did not create stats file' % prob