def evaluate(self, size):
        """Evaluate the model performance of the whole dataset after processing
        all batches.

        Args:
            size (int): Length of the entire validation dataset. When batch
                size > 1, the dataloader may pad some data samples to make
                sure all ranks have the same length of dataset slice. The
                ``collect_results`` function will drop the padded data based on
                this size.
        Returns:
            dict: Evaluation metrics dict on the val dataset. The keys are
            "{task_name}_{metric_name}" , and the values
            are corresponding results.
        """
        metrics = {}
        for task_name in self._metrics:
            for metric in self._metrics[task_name]:
                name = metric.__class__.__name__
                if name == 'MultiTasksMetric' or metric.results:
                    results = metric.evaluate(size)
                else:
                    results = {metric.__class__.__name__: 0}
                for key in results:
                    name = f'{task_name}_{key}'
                    if name in results:
                        """Inspired from https://github.com/open-
                        mmlab/mmengine/ bl ob/ed20a9cba52ceb371f7c825131636b9e2
                        747172e/mmengine/evalua tor/evaluator.py#L84-L87."""
                        raise ValueError(
                            'There are multiple metric results with the same'
                            f'metric name {name}. Please make sure all metrics'
                            'have different prefixes.')
                    metrics[name] = results[key]
        return metrics
Evaluate the model performance of the whole dataset after processing
        all batches.

        Args:
            size (int): Length of the entire validation dataset. When batch
                size > 1, the dataloader may pad some data samples to make
                sure all ranks have the same length of dataset slice. The
                ``collect_results`` function will drop the padded data based on
                this size.
        Returns:
            dict: Evaluation metrics dict on the val dataset. The keys are
            "{task_name}_{metric_name}" , and the values
            are corresponding results.
        
mmpretrain/evaluation/metrics/multi_task.py