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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 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