diff --git a/pyproject.toml b/pyproject.toml
index 735e7e554aa..84e8cc4adca 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -109,7 +109,6 @@ unfixable = ["F401"]
 "__init__.py" = ["D100"]
 "src/**" = [
     "D101",  # todo # Missing docstring in public class
-    "D102",  # todo # Missing docstring in public method
     "D103",  # todo # Missing docstring in public function
     "D105",  # todo # Missing docstring in magic method
     "D205",  # todo # 1 blank line required between summary line and description
diff --git a/src/torchmetrics/classification/accuracy.py b/src/torchmetrics/classification/accuracy.py
index f68a05db6db..0cfebff56d1 100644
--- a/src/torchmetrics/classification/accuracy.py
+++ b/src/torchmetrics/classification/accuracy.py
@@ -491,6 +491,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        """Initialize task metric."""
         task = ClassificationTask.from_str(task)
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
diff --git a/src/torchmetrics/classification/auroc.py b/src/torchmetrics/classification/auroc.py
index 14bb0b2e953..0f09abbabd4 100644
--- a/src/torchmetrics/classification/auroc.py
+++ b/src/torchmetrics/classification/auroc.py
@@ -108,6 +108,7 @@ def __init__(
         self.max_fpr = max_fpr
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
         return _binary_auroc_compute(state, self.thresholds, self.max_fpr)
 
@@ -207,6 +208,7 @@ def __init__(
         self.validate_args = validate_args
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
         return _multiclass_auroc_compute(state, self.num_classes, self.average, self.thresholds)
 
@@ -308,6 +310,7 @@ def __init__(
         self.validate_args = validate_args
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
         return _multilabel_auroc_compute(state, self.num_labels, self.average, self.thresholds, self.ignore_index)
 
@@ -353,6 +356,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        """Initialize task metric."""
         task = ClassificationTask.from_str(task)
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
         if task == ClassificationTask.BINARY:
diff --git a/src/torchmetrics/classification/average_precision.py b/src/torchmetrics/classification/average_precision.py
index c50d4708bf4..15deb00349a 100644
--- a/src/torchmetrics/classification/average_precision.py
+++ b/src/torchmetrics/classification/average_precision.py
@@ -98,6 +98,7 @@ class BinaryAveragePrecision(BinaryPrecisionRecallCurve):
     full_state_update: bool = False
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
         return _binary_average_precision_compute(state, self.thresholds)
 
@@ -201,6 +202,7 @@ def __init__(
         self.validate_args = validate_args
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
         return _multiclass_average_precision_compute(state, self.num_classes, self.average, self.thresholds)
 
@@ -307,6 +309,7 @@ def __init__(
         self.validate_args = validate_args
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
         return _multilabel_average_precision_compute(
             state, self.num_labels, self.average, self.thresholds, self.ignore_index
@@ -357,6 +360,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        """Initialize task metric."""
         task = ClassificationTask.from_str(task)
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
         if task == ClassificationTask.BINARY:
diff --git a/src/torchmetrics/classification/calibration_error.py b/src/torchmetrics/classification/calibration_error.py
index 3518870d7fa..082efa48b50 100644
--- a/src/torchmetrics/classification/calibration_error.py
+++ b/src/torchmetrics/classification/calibration_error.py
@@ -114,6 +114,7 @@ def __init__(
         self.add_state("accuracies", [], dist_reduce_fx="cat")
 
     def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
+        """Update metric states with predictions and targets."""
         if self.validate_args:
             _binary_calibration_error_tensor_validation(preds, target, self.ignore_index)
         preds, target = _binary_confusion_matrix_format(
@@ -124,6 +125,7 @@ def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
         self.accuracies.append(accuracies)
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         confidences = dim_zero_cat(self.confidences)
         accuracies = dim_zero_cat(self.accuracies)
         return _ce_compute(confidences, accuracies, self.n_bins, norm=self.norm)
@@ -217,6 +219,7 @@ def __init__(
         self.add_state("accuracies", [], dist_reduce_fx="cat")
 
     def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
+        """Update metric states with predictions and targets."""
         if self.validate_args:
             _multiclass_calibration_error_tensor_validation(preds, target, self.num_classes, self.ignore_index)
         preds, target = _multiclass_confusion_matrix_format(
@@ -227,6 +230,7 @@ def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
         self.accuracies.append(accuracies)
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         confidences = dim_zero_cat(self.confidences)
         accuracies = dim_zero_cat(self.accuracies)
         return _ce_compute(confidences, accuracies, self.n_bins, norm=self.norm)
@@ -268,6 +272,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        """Initialize task metric."""
         task = ClassificationTaskNoMultilabel.from_str(task)
         kwargs.update({"n_bins": n_bins, "norm": norm, "ignore_index": ignore_index, "validate_args": validate_args})
         if task == ClassificationTaskNoMultilabel.BINARY:
diff --git a/src/torchmetrics/classification/cohen_kappa.py b/src/torchmetrics/classification/cohen_kappa.py
index 524d8ff5d7d..65f87fe19ac 100644
--- a/src/torchmetrics/classification/cohen_kappa.py
+++ b/src/torchmetrics/classification/cohen_kappa.py
@@ -102,6 +102,7 @@ def __init__(
         self.validate_args = validate_args
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         return _cohen_kappa_reduce(self.confmat, self.weights)
 
 
@@ -184,6 +185,7 @@ def __init__(
         self.validate_args = validate_args
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         return _cohen_kappa_reduce(self.confmat, self.weights)
 
 
@@ -222,6 +224,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        """Initialize task metric."""
         task = ClassificationTaskNoMultilabel.from_str(task)
         kwargs.update({"weights": weights, "ignore_index": ignore_index, "validate_args": validate_args})
         if task == ClassificationTaskNoMultilabel.BINARY:
diff --git a/src/torchmetrics/classification/confusion_matrix.py b/src/torchmetrics/classification/confusion_matrix.py
index 2f9277d76a1..6f21d52ae5f 100644
--- a/src/torchmetrics/classification/confusion_matrix.py
+++ b/src/torchmetrics/classification/confusion_matrix.py
@@ -398,6 +398,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        """Initialize task metric."""
         task = ClassificationTask.from_str(task)
         kwargs.update({"normalize": normalize, "ignore_index": ignore_index, "validate_args": validate_args})
         if task == ClassificationTask.BINARY:
diff --git a/src/torchmetrics/classification/exact_match.py b/src/torchmetrics/classification/exact_match.py
index 82dfa3e8747..2a99048d911 100644
--- a/src/torchmetrics/classification/exact_match.py
+++ b/src/torchmetrics/classification/exact_match.py
@@ -118,6 +118,7 @@ def __init__(
         )
 
     def update(self, preds, target) -> None:
+        """Update metric states with predictions and targets."""
         if self.validate_args:
             _multiclass_stat_scores_tensor_validation(
                 preds, target, self.num_classes, self.multidim_average, self.ignore_index
@@ -132,6 +133,7 @@ def update(self, preds, target) -> None:
             self.total += total
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         correct = dim_zero_cat(self.correct) if isinstance(self.correct, list) else self.correct
         return _exact_match_reduce(correct, self.total)
 
@@ -250,6 +252,7 @@ def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
             self.total += total
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         correct = dim_zero_cat(self.correct) if isinstance(self.correct, list) else self.correct
         return _exact_match_reduce(correct, self.total)
 
@@ -289,6 +292,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        """Initialize task metric."""
         task = ClassificationTaskNoBinary.from_str(task)
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
diff --git a/src/torchmetrics/classification/f_beta.py b/src/torchmetrics/classification/f_beta.py
index 4dd02190e27..c24e4261673 100644
--- a/src/torchmetrics/classification/f_beta.py
+++ b/src/torchmetrics/classification/f_beta.py
@@ -117,6 +117,7 @@ def __init__(
         self.beta = beta
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         tp, fp, tn, fn = self._final_state()
         return _fbeta_reduce(tp, fp, tn, fn, self.beta, average="binary", multidim_average=self.multidim_average)
 
@@ -245,6 +246,7 @@ def __init__(
         self.beta = beta
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         tp, fp, tn, fn = self._final_state()
         return _fbeta_reduce(tp, fp, tn, fn, self.beta, average=self.average, multidim_average=self.multidim_average)
 
@@ -369,6 +371,7 @@ def __init__(
         self.beta = beta
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         tp, fp, tn, fn = self._final_state()
         return _fbeta_reduce(tp, fp, tn, fn, self.beta, average=self.average, multidim_average=self.multidim_average)
 
@@ -726,6 +729,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        """Initialize task metric."""
         assert multidim_average is not None
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
@@ -777,6 +781,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        """Initialize task metric."""
         task = ClassificationTask.from_str(task)
         assert multidim_average is not None
         kwargs.update(
diff --git a/src/torchmetrics/classification/hamming.py b/src/torchmetrics/classification/hamming.py
index ccdd13400f8..3d911c063d9 100644
--- a/src/torchmetrics/classification/hamming.py
+++ b/src/torchmetrics/classification/hamming.py
@@ -94,6 +94,7 @@ class BinaryHammingDistance(BinaryStatScores):
     full_state_update: bool = False
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         tp, fp, tn, fn = self._final_state()
         return _hamming_distance_reduce(tp, fp, tn, fn, average="binary", multidim_average=self.multidim_average)
 
@@ -199,6 +200,7 @@ class MulticlassHammingDistance(MulticlassStatScores):
     full_state_update: bool = False
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         tp, fp, tn, fn = self._final_state()
         return _hamming_distance_reduce(tp, fp, tn, fn, average=self.average, multidim_average=self.multidim_average)
 
@@ -302,6 +304,7 @@ class MultilabelHammingDistance(MultilabelStatScores):
     full_state_update: bool = False
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         tp, fp, tn, fn = self._final_state()
         return _hamming_distance_reduce(
             tp, fp, tn, fn, average=self.average, multidim_average=self.multidim_average, multilabel=True
@@ -345,6 +348,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        """Initialize task metric."""
         task = ClassificationTask.from_str(task)
         assert multidim_average is not None
         kwargs.update(
diff --git a/src/torchmetrics/classification/hinge.py b/src/torchmetrics/classification/hinge.py
index b68584e7f1b..247728f14ee 100644
--- a/src/torchmetrics/classification/hinge.py
+++ b/src/torchmetrics/classification/hinge.py
@@ -99,6 +99,7 @@ def __init__(
         self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
 
     def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
+        """Update metric state."""
         if self.validate_args:
             _binary_hinge_loss_tensor_validation(preds, target, self.ignore_index)
         preds, target = _binary_confusion_matrix_format(
@@ -109,6 +110,7 @@ def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
         self.total += total
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         return _hinge_loss_compute(self.measures, self.total)
 
 
@@ -203,6 +205,7 @@ def __init__(
         self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
 
     def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
+        """Update metric state."""
         if self.validate_args:
             _multiclass_hinge_loss_tensor_validation(preds, target, self.num_classes, self.ignore_index)
         preds, target = _multiclass_confusion_matrix_format(preds, target, self.ignore_index, convert_to_labels=False)
@@ -211,6 +214,7 @@ def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
         self.total += total
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         return _hinge_loss_compute(self.measures, self.total)
 
 
@@ -253,6 +257,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        """Initialize task metric."""
         task = ClassificationTaskNoMultilabel.from_str(task)
         kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
         if task == ClassificationTaskNoMultilabel.BINARY:
diff --git a/src/torchmetrics/classification/jaccard.py b/src/torchmetrics/classification/jaccard.py
index ae1c37fd93c..fa9e9317897 100644
--- a/src/torchmetrics/classification/jaccard.py
+++ b/src/torchmetrics/classification/jaccard.py
@@ -89,6 +89,7 @@ def __init__(
         )
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         return _jaccard_index_reduce(self.confmat, average="binary")
 
 
@@ -172,6 +173,7 @@ def __init__(
         self.average = average
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         return _jaccard_index_reduce(self.confmat, average=self.average)
 
 
@@ -259,6 +261,7 @@ def __init__(
         self.average = average
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         return _jaccard_index_reduce(self.confmat, average=self.average)
 
 
@@ -296,6 +299,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        """Initialize task metric."""
         task = ClassificationTask.from_str(task)
         kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
         if task == ClassificationTask.BINARY:
diff --git a/src/torchmetrics/classification/matthews_corrcoef.py b/src/torchmetrics/classification/matthews_corrcoef.py
index cceaa4e4b43..6084893b17d 100644
--- a/src/torchmetrics/classification/matthews_corrcoef.py
+++ b/src/torchmetrics/classification/matthews_corrcoef.py
@@ -80,6 +80,7 @@ def __init__(
         super().__init__(threshold, ignore_index, normalize=None, validate_args=validate_args, **kwargs)
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         return _matthews_corrcoef_reduce(self.confmat)
 
 
@@ -144,6 +145,7 @@ def __init__(
         super().__init__(num_classes, ignore_index, normalize=None, validate_args=validate_args, **kwargs)
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         return _matthews_corrcoef_reduce(self.confmat)
 
 
@@ -207,6 +209,7 @@ def __init__(
         super().__init__(num_labels, threshold, ignore_index, normalize=None, validate_args=validate_args, **kwargs)
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         return _matthews_corrcoef_reduce(self.confmat)
 
 
@@ -238,6 +241,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        """Initialize task metric."""
         task = ClassificationTask.from_str(task)
         kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
         if task == ClassificationTask.BINARY:
diff --git a/src/torchmetrics/classification/precision_recall.py b/src/torchmetrics/classification/precision_recall.py
index 300dc3364aa..bbe7c61e2ae 100644
--- a/src/torchmetrics/classification/precision_recall.py
+++ b/src/torchmetrics/classification/precision_recall.py
@@ -89,6 +89,7 @@ class BinaryPrecision(BinaryStatScores):
     full_state_update: bool = False
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         tp, fp, tn, fn = self._final_state()
         return _precision_recall_reduce(
             "precision", tp, fp, tn, fn, average="binary", multidim_average=self.multidim_average
@@ -193,6 +194,7 @@ class MulticlassPrecision(MulticlassStatScores):
     full_state_update: bool = False
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         tp, fp, tn, fn = self._final_state()
         return _precision_recall_reduce(
             "precision", tp, fp, tn, fn, average=self.average, multidim_average=self.multidim_average
@@ -295,6 +297,7 @@ class MultilabelPrecision(MultilabelStatScores):
     full_state_update: bool = False
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         tp, fp, tn, fn = self._final_state()
         return _precision_recall_reduce(
             "precision", tp, fp, tn, fn, average=self.average, multidim_average=self.multidim_average
@@ -368,6 +371,7 @@ class BinaryRecall(BinaryStatScores):
     full_state_update: bool = False
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         tp, fp, tn, fn = self._final_state()
         return _precision_recall_reduce(
             "recall", tp, fp, tn, fn, average="binary", multidim_average=self.multidim_average
@@ -472,6 +476,7 @@ class MulticlassRecall(MulticlassStatScores):
     full_state_update: bool = False
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         tp, fp, tn, fn = self._final_state()
         return _precision_recall_reduce(
             "recall", tp, fp, tn, fn, average=self.average, multidim_average=self.multidim_average
@@ -573,6 +578,7 @@ class MultilabelRecall(MultilabelStatScores):
     full_state_update: bool = False
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         tp, fp, tn, fn = self._final_state()
         return _precision_recall_reduce(
             "recall", tp, fp, tn, fn, average=self.average, multidim_average=self.multidim_average
@@ -617,6 +623,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        """Initialize task metric."""
         assert multidim_average is not None
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
@@ -671,6 +678,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        """Initialize task metric."""
         task = ClassificationTask.from_str(task)
         assert multidim_average is not None
         kwargs.update(
diff --git a/src/torchmetrics/classification/precision_recall_curve.py b/src/torchmetrics/classification/precision_recall_curve.py
index dafdea2e6cf..36fb608cff2 100644
--- a/src/torchmetrics/classification/precision_recall_curve.py
+++ b/src/torchmetrics/classification/precision_recall_curve.py
@@ -138,6 +138,7 @@ def __init__(
             )
 
     def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
+        """Update metric states."""
         if self.validate_args:
             _binary_precision_recall_curve_tensor_validation(preds, target, self.ignore_index)
         preds, target, _ = _binary_precision_recall_curve_format(preds, target, self.thresholds, self.ignore_index)
@@ -149,6 +150,7 @@ def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
             self.target.append(state[1])
 
     def compute(self) -> Tuple[Tensor, Tensor, Tensor]:
+        """Compute metric."""
         state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
         return _binary_precision_recall_curve_compute(state, self.thresholds)
 
@@ -264,6 +266,7 @@ def __init__(
             )
 
     def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
+        """Update metric states."""
         if self.validate_args:
             _multiclass_precision_recall_curve_tensor_validation(preds, target, self.num_classes, self.ignore_index)
         preds, target, _ = _multiclass_precision_recall_curve_format(
@@ -277,6 +280,7 @@ def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
             self.target.append(state[1])
 
     def compute(self) -> Union[Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]:
+        """Compute metric."""
         state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
         return _multiclass_precision_recall_curve_compute(state, self.num_classes, self.thresholds)
 
@@ -403,6 +407,7 @@ def __init__(
             )
 
     def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
+        """Update metric states."""
         if self.validate_args:
             _multilabel_precision_recall_curve_tensor_validation(preds, target, self.num_labels, self.ignore_index)
         preds, target, _ = _multilabel_precision_recall_curve_format(
@@ -416,6 +421,7 @@ def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
             self.target.append(state[1])
 
     def compute(self) -> Union[Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]:
+        """Compute metric."""
         state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
         return _multilabel_precision_recall_curve_compute(state, self.num_labels, self.thresholds, self.ignore_index)
 
@@ -467,6 +473,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        """Initialize task metric."""
         task = ClassificationTask.from_str(task)
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
         if task == ClassificationTask.BINARY:
diff --git a/src/torchmetrics/classification/ranking.py b/src/torchmetrics/classification/ranking.py
index a24a59866f4..0dcffb68f21 100644
--- a/src/torchmetrics/classification/ranking.py
+++ b/src/torchmetrics/classification/ranking.py
@@ -85,6 +85,7 @@ def __init__(
         self.add_state("total", torch.tensor(0.0), dist_reduce_fx="sum")
 
     def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
+        """Update metric states."""
         if self.validate_args:
             _multilabel_ranking_tensor_validation(preds, target, self.num_labels, self.ignore_index)
         preds, target = _multilabel_confusion_matrix_format(
@@ -95,6 +96,7 @@ def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
         self.total += n_elements
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         return _ranking_reduce(self.measure, self.total)
 
 
@@ -156,6 +158,7 @@ def __init__(
         self.add_state("total", torch.tensor(0.0), dist_reduce_fx="sum")
 
     def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
+        """Update metric states."""
         if self.validate_args:
             _multilabel_ranking_tensor_validation(preds, target, self.num_labels, self.ignore_index)
         preds, target = _multilabel_confusion_matrix_format(
@@ -166,6 +169,7 @@ def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
         self.total += n_elements
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         return _ranking_reduce(self.measure, self.total)
 
 
@@ -229,6 +233,7 @@ def __init__(
         self.add_state("total", torch.tensor(0.0), dist_reduce_fx="sum")
 
     def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
+        """Update metric states."""
         if self.validate_args:
             _multilabel_ranking_tensor_validation(preds, target, self.num_labels, self.ignore_index)
         preds, target = _multilabel_confusion_matrix_format(
@@ -239,4 +244,5 @@ def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
         self.total += n_elements
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         return _ranking_reduce(self.measure, self.total)
diff --git a/src/torchmetrics/classification/recall_at_fixed_precision.py b/src/torchmetrics/classification/recall_at_fixed_precision.py
index 188b71ce272..f32d118230e 100644
--- a/src/torchmetrics/classification/recall_at_fixed_precision.py
+++ b/src/torchmetrics/classification/recall_at_fixed_precision.py
@@ -111,6 +111,7 @@ def __init__(
         self.min_precision = min_precision
 
     def compute(self) -> Tuple[Tensor, Tensor]:  # type: ignore[override]
+        """Compute metric."""
         state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
         return _binary_recall_at_fixed_precision_compute(state, self.thresholds, self.min_precision)
 
@@ -201,6 +202,7 @@ def __init__(
         self.min_precision = min_precision
 
     def compute(self) -> Tuple[Tensor, Tensor]:  # type: ignore[override]
+        """Compute metric."""
         state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
         return _multiclass_recall_at_fixed_precision_arg_compute(
             state, self.num_classes, self.thresholds, self.min_precision
@@ -296,6 +298,7 @@ def __init__(
         self.min_precision = min_precision
 
     def compute(self) -> Tuple[Tensor, Tensor]:  # type: ignore[override]
+        """Compute metric."""
         state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
         return _multilabel_recall_at_fixed_precision_arg_compute(
             state, self.num_labels, self.thresholds, self.ignore_index, self.min_precision
@@ -324,6 +327,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        """Initialize task metric."""
         task = ClassificationTask.from_str(task)
         if task == ClassificationTask.BINARY:
             return BinaryRecallAtFixedPrecision(min_precision, thresholds, ignore_index, validate_args, **kwargs)
diff --git a/src/torchmetrics/classification/roc.py b/src/torchmetrics/classification/roc.py
index aa7d86c1cc0..12c5ae03888 100644
--- a/src/torchmetrics/classification/roc.py
+++ b/src/torchmetrics/classification/roc.py
@@ -103,6 +103,7 @@ class BinaryROC(BinaryPrecisionRecallCurve):
     full_state_update: bool = False
 
     def compute(self) -> Tuple[Tensor, Tensor, Tensor]:
+        """Compute metric."""
         state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
         return _binary_roc_compute(state, self.thresholds)
 
@@ -204,6 +205,7 @@ class MulticlassROC(MulticlassPrecisionRecallCurve):
     full_state_update: bool = False
 
     def compute(self) -> Tuple[Tensor, Tensor, Tensor]:
+        """Compute metric."""
         state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
         return _multiclass_roc_compute(state, self.num_classes, self.thresholds)
 
@@ -307,6 +309,7 @@ class MultilabelROC(MultilabelPrecisionRecallCurve):
     full_state_update: bool = False
 
     def compute(self) -> Tuple[Tensor, Tensor, Tensor]:
+        """Compute metric."""
         state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
         return _multilabel_roc_compute(state, self.num_labels, self.thresholds, self.ignore_index)
 
@@ -382,6 +385,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        """Initialize task metric."""
         task = ClassificationTask.from_str(task)
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
         if task == ClassificationTask.BINARY:
diff --git a/src/torchmetrics/classification/specificity.py b/src/torchmetrics/classification/specificity.py
index 0965a99bffc..d7f9e90a394 100644
--- a/src/torchmetrics/classification/specificity.py
+++ b/src/torchmetrics/classification/specificity.py
@@ -85,6 +85,7 @@ class BinarySpecificity(BinaryStatScores):
     """
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         tp, fp, tn, fn = self._final_state()
         return _specificity_reduce(tp, fp, tn, fn, average="binary", multidim_average=self.multidim_average)
 
@@ -184,6 +185,7 @@ class MulticlassSpecificity(MulticlassStatScores):
     """
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         tp, fp, tn, fn = self._final_state()
         return _specificity_reduce(tp, fp, tn, fn, average=self.average, multidim_average=self.multidim_average)
 
@@ -279,6 +281,7 @@ class MultilabelSpecificity(MultilabelStatScores):
     """
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         tp, fp, tn, fn = self._final_state()
         return _specificity_reduce(tp, fp, tn, fn, average=self.average, multidim_average=self.multidim_average)
 
@@ -321,6 +324,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        """Initialize task metric."""
         task = ClassificationTask.from_str(task)
         assert multidim_average is not None
         kwargs.update(
diff --git a/src/torchmetrics/classification/specificity_at_sensitivity.py b/src/torchmetrics/classification/specificity_at_sensitivity.py
index 05f4aa03c14..386d5742842 100644
--- a/src/torchmetrics/classification/specificity_at_sensitivity.py
+++ b/src/torchmetrics/classification/specificity_at_sensitivity.py
@@ -109,6 +109,7 @@ def __init__(
         self.min_sensitivity = min_sensitivity
 
     def compute(self) -> Tuple[Tensor, Tensor]:  # type: ignore[override]
+        """Compute metric."""
         if self.thresholds is None:
             state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)]  # type: ignore
         else:
@@ -202,6 +203,7 @@ def __init__(
         self.min_sensitivity = min_sensitivity
 
     def compute(self) -> Tuple[Tensor, Tensor]:  # type: ignore
+        """Compute metric."""
         if self.thresholds is None:
             state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)]  # type: ignore
         else:
@@ -297,6 +299,7 @@ def __init__(
         self.min_sensitivity = min_sensitivity
 
     def compute(self) -> Tuple[Tensor, Tensor]:  # type: ignore[override]
+        """Compute metric."""
         if self.thresholds is None:
             state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)]  # type: ignore
         else:
@@ -328,6 +331,7 @@ def __new__(  # type: ignore
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        """Initialize task metric."""
         task = ClassificationTask.from_str(task)
         if task == ClassificationTask.BINARY:
             return BinarySpecificityAtSensitivity(min_sensitivity, thresholds, ignore_index, validate_args, **kwargs)
diff --git a/src/torchmetrics/classification/stat_scores.py b/src/torchmetrics/classification/stat_scores.py
index 8fd61a10748..7082bef3e61 100644
--- a/src/torchmetrics/classification/stat_scores.py
+++ b/src/torchmetrics/classification/stat_scores.py
@@ -496,6 +496,7 @@ def __new__(
         validate_args: bool = True,
         **kwargs: Any,
     ) -> Metric:
+        """Initialize task metric."""
         task = ClassificationTask.from_str(task)
         assert multidim_average is not None
         kwargs.update(
diff --git a/src/torchmetrics/image/fid.py b/src/torchmetrics/image/fid.py
index 49bbc50b451..bac25fa11ce 100644
--- a/src/torchmetrics/image/fid.py
+++ b/src/torchmetrics/image/fid.py
@@ -54,6 +54,7 @@ def train(self, mode: bool) -> "NoTrainInceptionV3":
         return super().train(False)
 
     def forward(self, x: Tensor) -> Tensor:
+        """Forward pass of neural network with reshaping of output."""
         out = super().forward(x)
         return out[0].reshape(x.shape[0], -1)
 
@@ -66,6 +67,7 @@ class MatrixSquareRoot(Function):
 
     @staticmethod
     def forward(ctx: Any, input_data: Tensor) -> Tensor:
+        """Implements the forward pass for the matrix square root."""
         # TODO: update whenever pytorch gets an matrix square root function
         # Issue: https://github.com/pytorch/pytorch/issues/9983
         m = input_data.detach().cpu().numpy().astype(np.float_)
@@ -76,6 +78,7 @@ def forward(ctx: Any, input_data: Tensor) -> Tensor:
 
     @staticmethod
     def backward(ctx: Any, grad_output: Tensor) -> Tensor:
+        """Implements the backward pass for matrix square root."""
         grad_input = None
         if ctx.needs_input_grad[0]:
             (sqrtm,) = ctx.saved_tensors
@@ -288,6 +291,7 @@ def compute(self) -> Tensor:
         return _compute_fid(mean_real.squeeze(0), cov_real, mean_fake.squeeze(0), cov_fake).to(self.orig_dtype)
 
     def reset(self) -> None:
+        """Reset metric states."""
         if not self.reset_real_features:
             real_features_sum = deepcopy(self.real_features_sum)
             real_features_cov_sum = deepcopy(self.real_features_cov_sum)
diff --git a/src/torchmetrics/image/inception.py b/src/torchmetrics/image/inception.py
index 0dd01c2cdd0..ef137816796 100644
--- a/src/torchmetrics/image/inception.py
+++ b/src/torchmetrics/image/inception.py
@@ -141,6 +141,7 @@ def update(self, imgs: Tensor) -> None:  # type: ignore
         self.features.append(features)
 
     def compute(self) -> Tuple[Tensor, Tensor]:
+        """Compute metric."""
         features = dim_zero_cat(self.features)
         # random permute the features
         idx = torch.randperm(features.shape[0])
diff --git a/src/torchmetrics/image/kid.py b/src/torchmetrics/image/kid.py
index c4403d99d05..da956bcfa01 100644
--- a/src/torchmetrics/image/kid.py
+++ b/src/torchmetrics/image/kid.py
@@ -268,6 +268,7 @@ def compute(self) -> Tuple[Tensor, Tensor]:
         return kid_scores.mean(), kid_scores.std(unbiased=False)
 
     def reset(self) -> None:
+        """Reset metric states."""
         if not self.reset_real_features:
             # remove temporarily to avoid resetting
             value = self._defaults.pop("real_features")
diff --git a/src/torchmetrics/metric.py b/src/torchmetrics/metric.py
index 70e191273fc..552efaaf841 100644
--- a/src/torchmetrics/metric.py
+++ b/src/torchmetrics/metric.py
@@ -687,6 +687,15 @@ def state_dict(
         prefix: str = "",
         keep_vars: bool = False,
     ) -> Optional[Dict[str, Any]]:
+        """Get the current state of metric as an dictionary.
+
+        Args:
+            destination: Optional dictionary, that if provided, the state of module will be updated into the dict and
+                the same object is returned. Otherwise, an ``OrderedDict`` will be created and returned.
+            prefix: optional string, a prefix added to parameter and buffer names to compose the keys in state_dict.
+            keep_vars: by default the :class:`~torch.Tensor`s returned in the state dict are detached from autograd.
+                If set to ``True``, detaching will not be performed.
+        """
         destination = super().state_dict(destination=destination, prefix=prefix, keep_vars=keep_vars)
         # Register metric states to be part of the state_dict
         for key in self._defaults:
@@ -912,6 +921,7 @@ def _sync_dist(self, dist_sync_fn: Optional[Callable] = None, process_group: Opt
         pass
 
     def update(self, *args: Any, **kwargs: Any) -> None:
+        """Update metric state."""
         if isinstance(self.metric_a, Metric):
             self.metric_a.update(*args, **self.metric_a._filter_kwargs(**kwargs))
 
@@ -919,6 +929,7 @@ def update(self, *args: Any, **kwargs: Any) -> None:
             self.metric_b.update(*args, **self.metric_b._filter_kwargs(**kwargs))
 
     def compute(self) -> Any:
+        """Compute metric."""
         # also some parsing for kwargs?
         val_a = self.metric_a.compute() if isinstance(self.metric_a, Metric) else self.metric_a
 
@@ -931,6 +942,7 @@ def compute(self) -> Any:
 
     @torch.jit.unused
     def forward(self, *args: Any, **kwargs: Any) -> Any:
+        """Calculate metric on current batch and accumulate to global state."""
         val_a = (
             self.metric_a(*args, **self.metric_a._filter_kwargs(**kwargs))
             if isinstance(self.metric_a, Metric)
@@ -956,6 +968,7 @@ def forward(self, *args: Any, **kwargs: Any) -> Any:
         return self.op(val_a, val_b)
 
     def reset(self) -> None:
+        """Reset metric state."""
         if isinstance(self.metric_a, Metric):
             self.metric_a.reset()
 
@@ -963,6 +976,12 @@ def reset(self) -> None:
             self.metric_b.reset()
 
     def persistent(self, mode: bool = False) -> None:
+        """Change if metric state is persistent (save as part of state_dict) or not.
+
+        Args:
+            mode: bool indicating if all states should be persistent or not
+
+        """
         if isinstance(self.metric_a, Metric):
             self.metric_a.persistent(mode=mode)
         if isinstance(self.metric_b, Metric):
diff --git a/src/torchmetrics/regression/cosine_similarity.py b/src/torchmetrics/regression/cosine_similarity.py
index 93e4a6d0f83..33cf0908df6 100644
--- a/src/torchmetrics/regression/cosine_similarity.py
+++ b/src/torchmetrics/regression/cosine_similarity.py
@@ -80,6 +80,7 @@ def update(self, preds: Tensor, target: Tensor) -> None:
         self.target.append(target)
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         preds = dim_zero_cat(self.preds)
         target = dim_zero_cat(self.target)
         return _cosine_similarity_compute(preds, target, self.reduction)
diff --git a/src/torchmetrics/regression/kl_divergence.py b/src/torchmetrics/regression/kl_divergence.py
index 581ed539078..7d33ff8199b 100644
--- a/src/torchmetrics/regression/kl_divergence.py
+++ b/src/torchmetrics/regression/kl_divergence.py
@@ -98,6 +98,7 @@ def __init__(
         self.add_state("total", torch.tensor(0), dist_reduce_fx="sum")
 
     def update(self, p: Tensor, q: Tensor) -> None:  # type: ignore
+        """Update metric states with predictions and targets."""
         measures, total = _kld_update(p, q, self.log_prob)
         if self.reduction is None or self.reduction == "none":
             self.measures.append(measures)
@@ -106,5 +107,6 @@ def update(self, p: Tensor, q: Tensor) -> None:  # type: ignore
             self.total += total
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         measures = dim_zero_cat(self.measures) if self.reduction is None or self.reduction == "none" else self.measures
         return _kld_compute(measures, self.total, self.reduction)
diff --git a/src/torchmetrics/regression/tweedie_deviance.py b/src/torchmetrics/regression/tweedie_deviance.py
index e0acbc83dcc..77dee0c4626 100644
--- a/src/torchmetrics/regression/tweedie_deviance.py
+++ b/src/torchmetrics/regression/tweedie_deviance.py
@@ -97,4 +97,5 @@ def update(self, preds: Tensor, targets: Tensor) -> None:
         self.num_observations += num_observations
 
     def compute(self) -> Tensor:
+        """Compute metric."""
         return _tweedie_deviance_score_compute(self.sum_deviance_score, self.num_observations)
diff --git a/src/torchmetrics/retrieval/precision_recall_curve.py b/src/torchmetrics/retrieval/precision_recall_curve.py
index aecbb5bca4d..d2dafa1a12c 100644
--- a/src/torchmetrics/retrieval/precision_recall_curve.py
+++ b/src/torchmetrics/retrieval/precision_recall_curve.py
@@ -172,6 +172,7 @@ def update(self, preds: Tensor, target: Tensor, indexes: Tensor) -> None:  # typ
         self.target.append(target)
 
     def compute(self) -> Tuple[Tensor, Tensor, Tensor]:
+        """Compute metric."""
         # concat all data
         indexes = dim_zero_cat(self.indexes)
         preds = dim_zero_cat(self.preds)
@@ -304,6 +305,7 @@ def __init__(
         self.min_precision = min_precision
 
     def compute(self) -> Tuple[Tensor, Tensor]:  # type: ignore
+        """Compute metric."""
         precisions, recalls, top_k = super().compute()
 
         return _retrieval_recall_at_fixed_precision(precisions, recalls, top_k, self.min_precision)
diff --git a/src/torchmetrics/wrappers/classwise.py b/src/torchmetrics/wrappers/classwise.py
index fa17e009981..ba571b60c2c 100644
--- a/src/torchmetrics/wrappers/classwise.py
+++ b/src/torchmetrics/wrappers/classwise.py
@@ -90,15 +90,19 @@ def _convert(self, x: Tensor) -> Dict[str, Any]:
         return {f"{name}_{lab}": val for lab, val in zip(self.labels, x)}
 
     def forward(self, *args: Any, **kwargs: Any) -> Any:
+        """Calculate on batch and accumulate to global state."""
         return self._convert(self.metric(*args, **kwargs))
 
     def update(self, *args: Any, **kwargs: Any) -> None:
+        """Update state."""
         self.metric.update(*args, **kwargs)
 
     def compute(self) -> Dict[str, Tensor]:
+        """Compute metric."""
         return self._convert(self.metric.compute())
 
     def reset(self) -> None:
+        """Reset metric."""
         self.metric.reset()
 
     def _wrap_update(self, update: Callable) -> Callable: