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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 run(self, data, max_epochs=1):
 """Runs the process_function over the passed data. Args: data (Iterable):
 Collection of batches allowing repeated iteration (e.g., list or `DataLoader`). max_epochs (int, optional):
 max epochs to run for (default: 1). Returns: State: output state. """


        self.state = State(dataloader=data, epoch=0, max_epochs=max_epochs, metrics={})

        try:
            self._logger.info("Engine run starting with max_epochs={}.".format(max_epochs))
            start_time = time.time()
            self._fire_event(Events.STARTED)
            while self.state.epoch < max_epochs and not self.should_terminate:
                self.state.epoch += 1
                self._fire_event(Events.EPOCH_STARTED)
                hours, mins, secs = self._run_once_on_dataset()
                self._logger.info("Epoch[%s] Complete. Time taken: %02d:%02d:%02d", self.state.epoch, hours, mins, secs)
                if self.should_terminate:
                    break
                self._fire_event(Events.EPOCH_COMPLETED)

            self._fire_event(Events.COMPLETED)
            time_taken = time.time() - start_time
            hours, mins, secs = _to_hours_mins_secs(time_taken)
            self._logger.info("Engine run complete. Time taken %02d:%02d:%02d" % (hours, mins, secs))

        except BaseException as e:
            self._logger.error("Engine run is terminating due to exception: %s.", str(e))
            self._handle_exception(e)

        return self.state