def create_supervised_evaluator(model, metrics=None,
                                device=None, non_blocking=False,
                                prepare_batch=_prepare_batch,
                                output_transform=lambda x, y, y_pred: (y_pred, y,)):
    """
    Factory function for creating an evaluator for supervised models.

    Args:
        model (`torch.nn.Module`): the model to train.
        metrics (dict of str - :class:`~ignite.metrics.Metric`): a map of metric names to Metrics.
        device (str, optional): device type specification (default: None).
            Applies to both model and batches.
        non_blocking (bool, optional): if True and this copy is between CPU and GPU, the copy may occur asynchronously
            with respect to the host. For other cases, this argument has no effect.
        prepare_batch (callable, optional): function that receives `batch`, `device`, `non_blocking` and outputs
            tuple of tensors `(batch_x, batch_y)`.
        output_transform (callable, optional): function that receives 'x', 'y', 'y_pred' and returns value
            to be assigned to engine's state.output after each iteration. Default is returning `(y_pred, y,)` which fits
            output expected by metrics. If you change it you should use `output_transform` in metrics.

    Note: `engine.state.output` for this engine is defind by `output_transform` parameter and is
        a tuple of `(batch_pred, batch_y)` by default.

    Returns:
        Engine: an evaluator engine with supervised inference function.
    """
    metrics = metrics or {}

    if device:
        model.to(device)

    def _inference(engine, batch):
        model.eval()
        with torch.no_grad():
            x, y = prepare_batch(batch, device=device, non_blocking=non_blocking)
            y_pred = model(x)
            return output_transform(x, y, y_pred)

    engine = Engine(_inference)

    for name, metric in metrics.items():
        metric.attach(engine, name)

    return engine