from ignite.engine import Events, create_supervised_trainer from ignite.contrib.handlers import ProgressBar # Create a trainer trainer = create_supervised_trainer(model, optimizer, loss_function) # Create a progress bar handler pbar = ProgressBar() # Attach the handler to the trainer pbar.attach(trainer, metric_names='all', output_transform=lambda x: {'loss': x}) # Start the training loop trainer.run(train_loader, max_epochs=num_epochs)In this example, we create a supervised trainer using Ignite's `create_supervised_trainer` function. We then create a `ProgressBar` instance and attach it to the trainer using the `attach` method. We specify the `metric_names` argument as `'all'`, which means that the progress bar will display all the metrics collected by the trainer. Finally, we start the training loop by calling the `run` method of the trainer. Overall, the `ignite.contrib.handlers.ProgressBar` is a useful tool for monitoring the progress of model training. It is part of the Ignite library, which is available on PyPI and can be installed using pip.