Repository: Lightning-AI/pytorch-lightning
File: pytorch_lightning/metrics/metric.py (Python)

Code before:
    return -torch.abs(tensor)


class MetricCollection(nn.ModuleDict):
    """
    MetricCollection class can be used to chain metrics that have the same
    call pattern into one single class.

    Args:
        metrics: One of the following

            * list or tuple: if metrics are passed in as a list, will use the
              metrics class name as key for output dict. Therefore, two metrics
              of the same class cannot be chained this way.

            * dict: if metrics are passed in as a dict, will use each key in the
              dict as key for output dict. Use this format if you want to chain
              together multiple of the same metric with different parameters.

    Raises:
        ValueError:
            If one of the elements of ``metrics`` is not an instance of ``pl.metrics.Metric``
        ValueError:
            If two elements in ``metrics`` have the same ``name``
        ValueError:
            If ``metrics`` is not a ``list``, ``tuple`` or a ``dict``

    Example (input as list):

        >>> from pytorch_lightning.metrics import MetricCollection, Accuracy, Precision, Recall
        >>> target = torch.tensor([0, 2, 0, 2, 0, 1, 0, 2])
        >>> preds = torch.tensor([2, 1, 2, 0, 1, 2, 2, 2])
        >>> metrics = MetricCollection([Accuracy(),
        ...                             Precision(num_classes=3, average='macro'),
        ...                             Recall(num_classes=3, average='macro')])
        >>> metrics(preds, target)
        {'Accuracy': tensor(0.1250), 'Precision': tensor(0.0667), 'Recall': tensor(0.1111)}

    Example (input as dict):

        >>> metrics = MetricCollection({'micro_recall': Recall(num_classes=3, average='micro'),
        ...                             'macro_recall': Recall(num_classes=3, average='macro')})
        >>> metrics(preds, target)
        {'micro_recall': tensor(0.1250), 'macro_recall': tensor(0.1111)}

    """

    def __init__(self, metrics: Union[List[Metric], Tuple[Metric], Dict[str, Metric]]):
        super().__init__()
        if isinstance(metrics, dict):
            # Check all values are metrics

Review comment (suggestion):
```suggestion
            If ``metrics`` is not a ``list``, ``tuple`` or a ``dict``.
```

Code after:
    return -torch.abs(tensor)


class MetricCollection(nn.ModuleDict):
    """
    MetricCollection class can be used to chain metrics that have the same
    call pattern into one single class.

    Args:
        metrics: One of the following

            * list or tuple: if metrics are passed in as a list, will use the
              metrics class name as key for output dict. Therefore, two metrics
              of the same class cannot be chained this way.

            * dict: if metrics are passed in as a dict, will use each key in the
              dict as key for output dict. Use this format if you want to chain
              together multiple of the same metric with different parameters.

    Raises:
        ValueError:
            If one of the elements of ``metrics`` is not an instance of ``pl.metrics.Metric``.
        ValueError:
            If two elements in ``metrics`` have the same ``name``.
        ValueError:
            If ``metrics`` is not a ``list``, ``tuple`` or a ``dict``.

    Example (input as list):

        >>> from pytorch_lightning.metrics import MetricCollection, Accuracy, Precision, Recall
        >>> target = torch.tensor([0, 2, 0, 2, 0, 1, 0, 2])
        >>> preds = torch.tensor([2, 1, 2, 0, 1, 2, 2, 2])
        >>> metrics = MetricCollection([Accuracy(),
        ...                             Precision(num_classes=3, average='macro'),
        ...                             Recall(num_classes=3, average='macro')])
        >>> metrics(preds, target)
        {'Accuracy': tensor(0.1250), 'Precision': tensor(0.0667), 'Recall': tensor(0.1111)}

    Example (input as dict):

        >>> metrics = MetricCollection({'micro_recall': Recall(num_classes=3, average='micro'),
        ...                             'macro_recall': Recall(num_classes=3, average='macro')})
        >>> metrics(preds, target)
        {'micro_recall': tensor(0.1250), 'macro_recall': tensor(0.1111)}

    """

    def __init__(self, metrics: Union[List[Metric], Tuple[Metric], Dict[str, Metric]]):
        super().__init__()
        if isinstance(metrics, dict):
            # Check all values are metrics