how to print the second best pipeline?

Hello,
I was just wondering if there's any way to print out the confusion matrix, classification report and the pipeline for the second-best pipeline?

the model now is printing the best pipeline as below but would also like to print the second-best pipeline.

```
model = TPOTClassifier(generations=10, scoring='balanced_accuracy', verbosity=2)
model.fit(X_train, y_train)
```

```
Optimization Progress: 48%
530/1100 [2:07:27<3:41:41, 23.34s/pipeline]

Generation 1 - Current best internal CV score: 0.8820838802533277
Generation 2 - Current best internal CV score: 0.8828284663262757
Generation 3 - Current best internal CV score: 0.8828284663262757
Generation 4 - Current best internal CV score: 0.8842320902149032
Generation 5 - Current best internal CV score: 0.8842320902149032
Generation 6 - Current best internal CV score: 0.8842320902149032
Generation 7 - Current best internal CV score: 0.8842320902149032
Generation 8 - Current best internal CV score: 0.8842320902149032
Generation 9 - Current best internal CV score: 0.8842320902149032
Generation 10 - Current best internal CV score: 0.8842320902149032
```

```
Best pipeline: BernoulliNB(KNeighborsClassifier(input_matrix, n_neighbors=41, p=1, weights=uniform), alpha=0.01, fit_prior=True)
TPOTClassifier(config_dict=None, crossover_rate=0.1, cv=5,
disable_update_check=False, early_stop=None, generations=10,
log_file=None, max_eval_time_mins=5, max_time_mins=None,
memory=None, mutation_rate=0.9, n_jobs=1, offspring_size=None,
periodic_checkpoint_folder=None, population_size=100,
random_state=None, scoring='balanced_accuracy', subsample=1.0,
template=None, use_dask=False, verbosity=2, warm_start=False)
```

```
Acc.: 0.8521771865980675
precision recall f1-score support

0 1.00 0.85 0.92 7902
1 0.05 0.97 0.10 67

accuracy 0.85 7969
macro avg 0.53 0.91 0.51 7969
weighted avg 0.99 0.85 0.91 7969
```

```
Confusion Matrix:
[[6726 1176]
[ 2 65]]
```

Apologies if this was previously asked but searching Second Best returned nothing

Thanks,
m-alshehri