Fix the dice metric. Incorrect anotation (#1684)

src/torchmetrics/classification/dice.py

@@ -56,7 +56,7 @@ class Dice(Metric):
 
  Args:
  num_classes:
- Number of classes. Necessary for ``'macro'``, ``'weighted'`` and ``None`` average methods.
+ Number of classes. Necessary for ``'macro'``, and ``None`` average methods.
  threshold:
  Threshold for transforming probability or logit predictions to binary (0,1) predictions, in the case
  of binary or multi-label inputs. Default value of 0.5 corresponds to input being probabilities.
@@ -116,7 +116,7 @@ class Dice(Metric):
 
  Raises:
  ValueError:
- If ``average`` is none of ``"micro"``, ``"macro"``, ``"weighted"``, ``"samples"``, ``"none"``, ``None``.
+ If ``average`` is none of ``"micro"``, ``"macro"``, ``"samples"``, ``"none"``, ``None``.
  ValueError:
  If ``mdmc_average`` is not one of ``None``, ``"samplewise"``, ``"global"``.
  ValueError:
@@ -146,15 +146,15 @@ def __init__(
  zero_division: int = 0,
  num_classes: Optional[int] = None,
  threshold: float = 0.5,
- average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
+ average: Optional[Literal["micro", "macro", "none"]] = "micro",
  mdmc_average: Optional[str] = "global",
  ignore_index: Optional[int] = None,
  top_k: Optional[int] = None,
  multiclass: Optional[bool] = None,
  **kwargs: Any,
  ) -> None:
  super().__init__(**kwargs)
- allowed_average = ("micro", "macro", "weighted", "samples", "none", None)
+ allowed_average = ("micro", "macro", "samples", "none", None)
  if average not in allowed_average:
  raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.")