diff --git a/pyproject.toml b/pyproject.toml
index 9114383c58a..067d1a0007c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -122,7 +122,6 @@ ignore = [
     "S310",   # todo: Audit URL open for permitted schemes. Allowing use of `file:` or custom schemes is often unexpected.  # todo
     "B905",   # todo: `zip()` without an explicit `strict=` parameter
     "PYI024",  # todo: Use `typing.NamedTuple` instead of `collections.namedtuple`
-    "PYI041",  # todo: Use `float` instead of `int | float``
 ]
 # Exclude a variety of commonly ignored directories.
 exclude = [
diff --git a/src/torchmetrics/functional/image/_deprecated.py b/src/torchmetrics/functional/image/_deprecated.py
index 00162753586..0b46b25d32a 100644
--- a/src/torchmetrics/functional/image/_deprecated.py
+++ b/src/torchmetrics/functional/image/_deprecated.py
@@ -42,7 +42,7 @@ def _spectral_distortion_index(
 def _error_relative_global_dimensionless_synthesis(
     preds: Tensor,
     target: Tensor,
-    ratio: Union[int, float] = 4,
+    ratio: float = 4,
     reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
 ) -> Tensor:
     """Wrapper for deprecated import.
diff --git a/src/torchmetrics/functional/image/ergas.py b/src/torchmetrics/functional/image/ergas.py
index 5df44018506..d237115fa2e 100644
--- a/src/torchmetrics/functional/image/ergas.py
+++ b/src/torchmetrics/functional/image/ergas.py
@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Tuple, Union
+from typing import Tuple
 
 import torch
 from torch import Tensor
@@ -46,7 +46,7 @@ def _ergas_update(preds: Tensor, target: Tensor) -> Tuple[Tensor, Tensor]:
 def _ergas_compute(
     preds: Tensor,
     target: Tensor,
-    ratio: Union[int, float] = 4,
+    ratio: float = 4,
     reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
 ) -> Tensor:
     """Erreur Relative Globale Adimensionnelle de Synthèse.
@@ -86,7 +86,7 @@ def _ergas_compute(
 def error_relative_global_dimensionless_synthesis(
     preds: Tensor,
     target: Tensor,
-    ratio: Union[int, float] = 4,
+    ratio: float = 4,
     reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
 ) -> Tensor:
     """Erreur Relative Globale Adimensionnelle de Synthèse.
diff --git a/src/torchmetrics/functional/multimodal/clip_iqa.py b/src/torchmetrics/functional/multimodal/clip_iqa.py
index 4f5a6ccbb5f..078760dedde 100644
--- a/src/torchmetrics/functional/multimodal/clip_iqa.py
+++ b/src/torchmetrics/functional/multimodal/clip_iqa.py
@@ -178,7 +178,7 @@ def _clip_iqa_update(
     images: Tensor,
     model: _CLIPModel,
     processor: _CLIPProcessor,
-    data_range: Union[int, float],
+    data_range: float,
     device: Union[str, torch.device],
 ) -> Tensor:
     images = images / float(data_range)
@@ -221,7 +221,7 @@ def clip_image_quality_assessment(
         "openai/clip-vit-large-patch14-336",
         "openai/clip-vit-large-patch14",
     ] = "clip_iqa",
-    data_range: Union[int, float] = 1.0,
+    data_range: float = 1.0,
     prompts: Tuple[Union[str, Tuple[str, str]]] = ("quality",),
 ) -> Union[Tensor, Dict[str, Tensor]]:
     """Calculates `CLIP-IQA`_, that can be used to measure the visual content of images.
diff --git a/src/torchmetrics/functional/nominal/cramers.py b/src/torchmetrics/functional/nominal/cramers.py
index 46d4058010c..75a606f6c19 100644
--- a/src/torchmetrics/functional/nominal/cramers.py
+++ b/src/torchmetrics/functional/nominal/cramers.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import itertools
-from typing import Optional, Union
+from typing import Optional
 
 import torch
 from torch import Tensor
@@ -34,7 +34,7 @@ def _cramers_v_update(
     target: Tensor,
     num_classes: int,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     """Compute the bins to update the confusion matrix with for Cramer's V calculation.
 
@@ -90,7 +90,7 @@ def cramers_v(
     target: Tensor,
     bias_correction: bool = True,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     r"""Compute `Cramer's V`_ statistic measuring the association between two categorical (nominal) data series.
 
@@ -142,7 +142,7 @@ def cramers_v_matrix(
     matrix: Tensor,
     bias_correction: bool = True,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     r"""Compute `Cramer's V`_ statistic between a set of multiple variables.
 
diff --git a/src/torchmetrics/functional/nominal/pearson.py b/src/torchmetrics/functional/nominal/pearson.py
index b519b105a7a..bd25c701fe4 100644
--- a/src/torchmetrics/functional/nominal/pearson.py
+++ b/src/torchmetrics/functional/nominal/pearson.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import itertools
-from typing import Optional, Union
+from typing import Optional
 
 import torch
 from torch import Tensor
@@ -32,7 +32,7 @@ def _pearsons_contingency_coefficient_update(
     target: Tensor,
     num_classes: int,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     """Compute the bins to update the confusion matrix with for Pearson's Contingency Coefficient calculation.
 
@@ -76,7 +76,7 @@ def pearsons_contingency_coefficient(
     preds: Tensor,
     target: Tensor,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     r"""Compute `Pearson's Contingency Coefficient`_ for measuring the association between two categorical data series.
 
@@ -131,7 +131,7 @@ def pearsons_contingency_coefficient(
 def pearsons_contingency_coefficient_matrix(
     matrix: Tensor,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     r"""Compute `Pearson's Contingency Coefficient`_ statistic between a set of multiple variables.
 
diff --git a/src/torchmetrics/functional/nominal/theils_u.py b/src/torchmetrics/functional/nominal/theils_u.py
index a010ee48c1d..8bdaf38aa8a 100644
--- a/src/torchmetrics/functional/nominal/theils_u.py
+++ b/src/torchmetrics/functional/nominal/theils_u.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import itertools
-from typing import Optional, Union
+from typing import Optional
 
 import torch
 from torch import Tensor
@@ -57,7 +57,7 @@ def _theils_u_update(
     target: Tensor,
     num_classes: int,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     """Compute the bins to update the confusion matrix with for Theil's U calculation.
 
@@ -109,7 +109,7 @@ def theils_u(
     preds: Tensor,
     target: Tensor,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     r"""Compute `Theils Uncertainty coefficient`_ statistic measuring the association between two nominal data series.
 
@@ -154,7 +154,7 @@ def theils_u(
 def theils_u_matrix(
     matrix: Tensor,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     r"""Compute `Theil's U`_ statistic between a set of multiple variables.
 
diff --git a/src/torchmetrics/functional/nominal/tschuprows.py b/src/torchmetrics/functional/nominal/tschuprows.py
index 1ee8756d243..2ea20d57f19 100644
--- a/src/torchmetrics/functional/nominal/tschuprows.py
+++ b/src/torchmetrics/functional/nominal/tschuprows.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import itertools
-from typing import Optional, Union
+from typing import Optional
 
 import torch
 from torch import Tensor
@@ -34,7 +34,7 @@ def _tschuprows_t_update(
     target: Tensor,
     num_classes: int,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     """Compute the bins to update the confusion matrix with for Tschuprow's T calculation.
 
@@ -92,7 +92,7 @@ def tschuprows_t(
     target: Tensor,
     bias_correction: bool = True,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     r"""Compute `Tschuprow's T`_ statistic measuring the association between two categorical (nominal) data series.
 
@@ -148,7 +148,7 @@ def tschuprows_t_matrix(
     matrix: Tensor,
     bias_correction: bool = True,
     nan_strategy: Literal["replace", "drop"] = "replace",
-    nan_replace_value: Optional[Union[int, float]] = 0.0,
+    nan_replace_value: Optional[float] = 0.0,
 ) -> Tensor:
     r"""Compute `Tschuprow's T`_ statistic between a set of multiple variables.
 
diff --git a/src/torchmetrics/functional/nominal/utils.py b/src/torchmetrics/functional/nominal/utils.py
index c1baefb977f..258209326da 100644
--- a/src/torchmetrics/functional/nominal/utils.py
+++ b/src/torchmetrics/functional/nominal/utils.py
@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Optional, Tuple, Union
+from typing import Optional, Tuple
 
 import torch
 from torch import Tensor
@@ -20,12 +20,12 @@
 from torchmetrics.utilities.prints import rank_zero_warn
 
 
-def _nominal_input_validation(nan_strategy: str, nan_replace_value: Optional[Union[int, float]]) -> None:
+def _nominal_input_validation(nan_strategy: str, nan_replace_value: Optional[float]) -> None:
     if nan_strategy not in ["replace", "drop"]:
         raise ValueError(
             f"Argument `nan_strategy` is expected to be one of `['replace', 'drop']`, but got {nan_strategy}"
         )
-    if nan_strategy == "replace" and not isinstance(nan_replace_value, (int, float)):
+    if nan_strategy == "replace" and not isinstance(nan_replace_value, (float, int)):
         raise ValueError(
             "Argument `nan_replace` is expected to be of a type `int` or `float` when `nan_strategy = 'replace`, "
             f"but got {nan_replace_value}"
diff --git a/src/torchmetrics/functional/pairwise/minkowski.py b/src/torchmetrics/functional/pairwise/minkowski.py
index 857e0d23943..298cedd1486 100644
--- a/src/torchmetrics/functional/pairwise/minkowski.py
+++ b/src/torchmetrics/functional/pairwise/minkowski.py
@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Optional, Union
+from typing import Optional
 
 import torch
 from torch import Tensor
@@ -22,7 +22,7 @@
 
 
 def _pairwise_minkowski_distance_update(
-    x: Tensor, y: Optional[Tensor] = None, exponent: Union[int, float] = 2, zero_diagonal: Optional[bool] = None
+    x: Tensor, y: Optional[Tensor] = None, exponent: float = 2, zero_diagonal: Optional[bool] = None
 ) -> Tensor:
     """Calculate the pairwise minkowski distance matrix.
 
@@ -49,7 +49,7 @@ def _pairwise_minkowski_distance_update(
 def pairwise_minkowski_distance(
     x: Tensor,
     y: Optional[Tensor] = None,
-    exponent: Union[int, float] = 2,
+    exponent: float = 2,
     reduction: Literal["mean", "sum", "none", None] = None,
     zero_diagonal: Optional[bool] = None,
 ) -> Tensor:
diff --git a/src/torchmetrics/image/_deprecated.py b/src/torchmetrics/image/_deprecated.py
index 45afe6dc178..7aa7a63743d 100644
--- a/src/torchmetrics/image/_deprecated.py
+++ b/src/torchmetrics/image/_deprecated.py
@@ -28,7 +28,7 @@ class _ErrorRelativeGlobalDimensionlessSynthesis(ErrorRelativeGlobalDimensionles
 
     def __init__(
         self,
-        ratio: Union[int, float] = 4,
+        ratio: float = 4,
         reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
         **kwargs: Any,
     ) -> None:
diff --git a/src/torchmetrics/image/ergas.py b/src/torchmetrics/image/ergas.py
index 77695b976de..2e22c34846a 100644
--- a/src/torchmetrics/image/ergas.py
+++ b/src/torchmetrics/image/ergas.py
@@ -75,7 +75,7 @@ class ErrorRelativeGlobalDimensionlessSynthesis(Metric):
 
     def __init__(
         self,
-        ratio: Union[int, float] = 4,
+        ratio: float = 4,
         reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
         **kwargs: Any,
     ) -> None:
diff --git a/src/torchmetrics/metric.py b/src/torchmetrics/metric.py
index 2c4493e9877..f574320dc44 100644
--- a/src/torchmetrics/metric.py
+++ b/src/torchmetrics/metric.py
@@ -922,81 +922,67 @@ def __hash__(self) -> int:
 
         return hash(tuple(hash_vals))
 
-    def __add__(self, other: Union["Metric", int, builtins.float, Tensor]) -> "CompositionalMetric":
+    def __add__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":
         """Construct compositional metric using the addition operator."""
         return CompositionalMetric(torch.add, self, other)
 
-    def __and__(self, other: Union["Metric", int, builtins.float, Tensor]) -> "CompositionalMetric":
+    def __and__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":
         """Construct compositional metric using the logical and operator."""
         return CompositionalMetric(torch.bitwise_and, self, other)
 
-    def __eq__(  # type: ignore[override]
-        self, other: Union["Metric", int, builtins.float, Tensor]
-    ) -> "CompositionalMetric":
+    def __eq__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":  # type: ignore[override]
         """Construct compositional metric using the equal operator."""
         return CompositionalMetric(torch.eq, self, other)
 
-    def __floordiv__(self, other: Union["Metric", int, builtins.float, Tensor]) -> "CompositionalMetric":
+    def __floordiv__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":
         """Construct compositional metric using the floor division operator."""
         return CompositionalMetric(torch.floor_divide, self, other)
 
-    def __ge__(  # type: ignore[misc]
-        self, other: Union["Metric", int, builtins.float, Tensor]
-    ) -> "CompositionalMetric":
+    def __ge__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":  # type: ignore[misc]
         """Construct compositional metric using the greater than or equal operator."""
         return CompositionalMetric(torch.ge, self, other)
 
-    def __gt__(  # type: ignore[misc]
-        self, other: Union["Metric", int, builtins.float, Tensor]
-    ) -> "CompositionalMetric":
+    def __gt__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":  # type: ignore[misc]
         """Construct compositional metric using the greater than operator."""
         return CompositionalMetric(torch.gt, self, other)
 
-    def __le__(  # type: ignore[misc]
-        self, other: Union["Metric", int, builtins.float, Tensor]
-    ) -> "CompositionalMetric":
+    def __le__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":  # type: ignore[misc]
         """Construct compositional metric using the less than or equal operator."""
         return CompositionalMetric(torch.le, self, other)
 
-    def __lt__(  # type: ignore[misc]
-        self, other: Union["Metric", int, builtins.float, Tensor]
-    ) -> "CompositionalMetric":
+    def __lt__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":  # type: ignore[misc]
         """Construct compositional metric using the less than operator."""
         return CompositionalMetric(torch.lt, self, other)
 
-    def __matmul__(self, other: Union["Metric", int, builtins.float, Tensor]) -> "CompositionalMetric":
+    def __matmul__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":
         """Construct compositional metric using the matrix multiplication operator."""
         return CompositionalMetric(torch.matmul, self, other)
 
-    def __mod__(self, other: Union["Metric", int, builtins.float, Tensor]) -> "CompositionalMetric":
+    def __mod__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":
         """Construct compositional metric using the remainder operator."""
         return CompositionalMetric(torch.fmod, self, other)
 
-    def __mul__(self, other: Union["Metric", int, builtins.float, Tensor]) -> "CompositionalMetric":
+    def __mul__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":
         """Construct compositional metric using the multiplication operator."""
         return CompositionalMetric(torch.mul, self, other)
 
-    def __ne__(  # type: ignore[override]
-        self, other: Union["Metric", int, builtins.float, Tensor]
-    ) -> "CompositionalMetric":
+    def __ne__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":  # type: ignore[override]
         """Construct compositional metric using the not equal operator."""
         return CompositionalMetric(torch.ne, self, other)
 
-    def __or__(self, other: Union["Metric", int, builtins.float, Tensor]) -> "CompositionalMetric":
+    def __or__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":
         """Construct compositional metric using the logical or operator."""
         return CompositionalMetric(torch.bitwise_or, self, other)
 
-    def __pow__(self, other: Union["Metric", int, builtins.float, Tensor]) -> "CompositionalMetric":
+    def __pow__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":
         """Construct compositional metric using the exponential/power operator."""
         return CompositionalMetric(torch.pow, self, other)
 
-    def __radd__(  # type: ignore[misc]
-        self, other: Union["Metric", int, builtins.float, Tensor]
-    ) -> "CompositionalMetric":
+    def __radd__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":  # type: ignore[misc]
         """Construct compositional metric using the addition operator."""
         return CompositionalMetric(torch.add, other, self)
 
-    def __rand__(self, other: Union["Metric", int, builtins.float, Tensor]) -> "CompositionalMetric":
+    def __rand__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":
         """Construct compositional metric using the logical and operator."""
         # swap them since bitwise_and only supports that way and it's commutative
         return CompositionalMetric(torch.bitwise_and, self, other)
@@ -1005,55 +991,47 @@ def __rfloordiv__(self, other: "CompositionalMetric") -> "Metric":
         """Construct compositional metric using the floor division operator."""
         return CompositionalMetric(torch.floor_divide, other, self)
 
-    def __rmatmul__(self, other: Union["Metric", int, builtins.float, Tensor]) -> "CompositionalMetric":
+    def __rmatmul__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":
         """Construct compositional metric using the matrix multiplication operator."""
         return CompositionalMetric(torch.matmul, other, self)
 
-    def __rmod__(  # type: ignore[misc]
-        self, other: Union["Metric", int, builtins.float, Tensor]
-    ) -> "CompositionalMetric":
+    def __rmod__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":  # type: ignore[misc]
         """Construct compositional metric using the remainder operator."""
         return CompositionalMetric(torch.fmod, other, self)
 
-    def __rmul__(  # type: ignore[misc]
-        self, other: Union["Metric", int, builtins.float, Tensor]
-    ) -> "CompositionalMetric":
+    def __rmul__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":  # type: ignore[misc]
         """Construct compositional metric using the multiplication operator."""
         return CompositionalMetric(torch.mul, other, self)
 
-    def __ror__(self, other: Union["Metric", int, builtins.float, Tensor]) -> "CompositionalMetric":
+    def __ror__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":
         """Construct compositional metric using the logical or operator."""
         return CompositionalMetric(torch.bitwise_or, other, self)
 
-    def __rpow__(self, other: Union["Metric", int, builtins.float, Tensor]) -> "CompositionalMetric":
+    def __rpow__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":
         """Construct compositional metric using the exponential/power operator."""
         return CompositionalMetric(torch.pow, other, self)
 
-    def __rsub__(  # type: ignore[misc]
-        self, other: Union["Metric", int, builtins.float, Tensor]
-    ) -> "CompositionalMetric":
+    def __rsub__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":  # type: ignore[misc]
         """Construct compositional metric using the subtraction operator."""
         return CompositionalMetric(torch.sub, other, self)
 
-    def __rtruediv__(  # type: ignore[misc]
-        self, other: Union["Metric", int, builtins.float, Tensor]
-    ) -> "CompositionalMetric":
+    def __rtruediv__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":  # type: ignore[misc]
         """Construct compositional metric using the true divide operator."""
         return CompositionalMetric(torch.true_divide, other, self)
 
-    def __rxor__(self, other: Union["Metric", int, builtins.float, Tensor]) -> "CompositionalMetric":
+    def __rxor__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":
         """Construct compositional metric using the logical xor operator."""
         return CompositionalMetric(torch.bitwise_xor, other, self)
 
-    def __sub__(self, other: Union["Metric", int, builtins.float, Tensor]) -> "CompositionalMetric":
+    def __sub__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":
         """Construct compositional metric using the subtraction operator."""
         return CompositionalMetric(torch.sub, self, other)
 
-    def __truediv__(self, other: Union["Metric", int, builtins.float, Tensor]) -> "CompositionalMetric":
+    def __truediv__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":
         """Construct compositional metric using the true divide operator."""
         return CompositionalMetric(torch.true_divide, self, other)
 
-    def __xor__(self, other: Union["Metric", int, builtins.float, Tensor]) -> "CompositionalMetric":
+    def __xor__(self, other: Union["Metric", builtins.float, Tensor]) -> "CompositionalMetric":
         """Construct compositional metric using the logical xor operator."""
         return CompositionalMetric(torch.bitwise_xor, self, other)
 
@@ -1100,8 +1078,8 @@ class CompositionalMetric(Metric):
     def __init__(
         self,
         operator: Callable,
-        metric_a: Union[Metric, int, float, Tensor],
-        metric_b: Union[Metric, int, float, Tensor, None],
+        metric_a: Union[Metric, float, Tensor],
+        metric_b: Union[Metric, float, Tensor, None],
     ) -> None:
         """Class for creating compositions of metrics.
 
diff --git a/src/torchmetrics/multimodal/clip_iqa.py b/src/torchmetrics/multimodal/clip_iqa.py
index dff9e05f2e2..de48df1bbae 100644
--- a/src/torchmetrics/multimodal/clip_iqa.py
+++ b/src/torchmetrics/multimodal/clip_iqa.py
@@ -178,7 +178,7 @@ def __init__(
             "openai/clip-vit-large-patch14-336",
             "openai/clip-vit-large-patch14",
         ] = "clip_iqa",
-        data_range: Union[int, float] = 1.0,
+        data_range: float = 1.0,
         prompts: Tuple[Union[str, Tuple[str, str]]] = ("quality",),
         **kwargs: Any
     ) -> None:
diff --git a/src/torchmetrics/nominal/cramers.py b/src/torchmetrics/nominal/cramers.py
index 1481a28a73d..2d780e7e379 100644
--- a/src/torchmetrics/nominal/cramers.py
+++ b/src/torchmetrics/nominal/cramers.py
@@ -91,7 +91,7 @@ def __init__(
         num_classes: int,
         bias_correction: bool = True,
         nan_strategy: Literal["replace", "drop"] = "replace",
-        nan_replace_value: Optional[Union[int, float]] = 0.0,
+        nan_replace_value: Optional[float] = 0.0,
         **kwargs: Any,
     ) -> None:
         super().__init__(**kwargs)
diff --git a/src/torchmetrics/nominal/pearson.py b/src/torchmetrics/nominal/pearson.py
index 143e9d20411..2fc88c8e851 100644
--- a/src/torchmetrics/nominal/pearson.py
+++ b/src/torchmetrics/nominal/pearson.py
@@ -94,7 +94,7 @@ def __init__(
         self,
         num_classes: int,
         nan_strategy: Literal["replace", "drop"] = "replace",
-        nan_replace_value: Optional[Union[int, float]] = 0.0,
+        nan_replace_value: Optional[float] = 0.0,
         **kwargs: Any,
     ) -> None:
         super().__init__(**kwargs)
diff --git a/src/torchmetrics/nominal/theils_u.py b/src/torchmetrics/nominal/theils_u.py
index f21a8efa385..f82c7658b1f 100644
--- a/src/torchmetrics/nominal/theils_u.py
+++ b/src/torchmetrics/nominal/theils_u.py
@@ -80,7 +80,7 @@ def __init__(
         self,
         num_classes: int,
         nan_strategy: Literal["replace", "drop"] = "replace",
-        nan_replace_value: Optional[Union[int, float]] = 0.0,
+        nan_replace_value: Optional[float] = 0.0,
         **kwargs: Any,
     ) -> None:
         super().__init__(**kwargs)
diff --git a/src/torchmetrics/nominal/tschuprows.py b/src/torchmetrics/nominal/tschuprows.py
index 540fd016fd6..a14832b4121 100644
--- a/src/torchmetrics/nominal/tschuprows.py
+++ b/src/torchmetrics/nominal/tschuprows.py
@@ -91,7 +91,7 @@ def __init__(
         num_classes: int,
         bias_correction: bool = True,
         nan_strategy: Literal["replace", "drop"] = "replace",
-        nan_replace_value: Optional[Union[int, float]] = 0.0,
+        nan_replace_value: Optional[float] = 0.0,
         **kwargs: Any,
     ) -> None:
         super().__init__(**kwargs)
diff --git a/src/torchmetrics/wrappers/minmax.py b/src/torchmetrics/wrappers/minmax.py
index c2bf40a0fbf..09684c55919 100644
--- a/src/torchmetrics/wrappers/minmax.py
+++ b/src/torchmetrics/wrappers/minmax.py
@@ -91,9 +91,7 @@ def compute(self) -> Dict[str, Tensor]:
         """
         val = self._base_metric.compute()
         if not self._is_suitable_val(val):
-            raise RuntimeError(
-                f"Returned value from base metric should be a scalar (int, float or tensor of size 1, but got {val}"
-            )
+            raise RuntimeError(f"Returned value from base metric should be a float or scalar tensor, but got {val}.")
         self.max_val = val if self.max_val.to(val.device) < val else self.max_val.to(val.device)
         self.min_val = val if self.min_val.to(val.device) > val else self.min_val.to(val.device)
         return {"raw": val, "max": self.max_val, "min": self.min_val}
@@ -108,7 +106,7 @@ def reset(self) -> None:
         self._base_metric.reset()
 
     @staticmethod
-    def _is_suitable_val(val: Union[int, float, Tensor]) -> bool:
+    def _is_suitable_val(val: Union[float, Tensor]) -> bool:
         """Check whether min/max is a scalar value."""
         if isinstance(val, (int, float)):
             return True
diff --git a/tests/unittests/image/test_ergas.py b/tests/unittests/image/test_ergas.py
index 313967cbf85..e87b4b63a0f 100644
--- a/tests/unittests/image/test_ergas.py
+++ b/tests/unittests/image/test_ergas.py
@@ -13,7 +13,6 @@
 # limitations under the License.
 from collections import namedtuple
 from functools import partial
-from typing import Union
 
 import pytest
 import torch
@@ -44,7 +43,7 @@
 def _baseline_ergas(
     preds: Tensor,
     target: Tensor,
-    ratio: Union[int, float] = 4,
+    ratio: float = 4,
     reduction: str = "elementwise_mean",
 ) -> Tensor:
     """Baseline implementation of Erreur Relative Globale Adimensionnelle de Synthèse."""
diff --git a/tests/unittests/wrappers/test_minmax.py b/tests/unittests/wrappers/test_minmax.py
index ea3af7da2f7..90df27a3992 100644
--- a/tests/unittests/wrappers/test_minmax.py
+++ b/tests/unittests/wrappers/test_minmax.py
@@ -108,5 +108,5 @@ def test_no_scalar_compute() -> None:
     """Tests that an assertion error is thrown if the wrapped basemetric gives a non-scalar on compute."""
     min_max_nsm = MinMaxMetric(BinaryConfusionMatrix())
 
-    with pytest.raises(RuntimeError, match=r"Returned value from base metric should be a scalar .*"):
+    with pytest.raises(RuntimeError, match=r"Returned value from base metric should be a float.*"):
         min_max_nsm.compute()