diff --git a/src/lightning/pytorch/trainer/connectors/logger_connector/result.py b/src/lightning/pytorch/trainer/connectors/logger_connector/result.py
index 81630362a7..7e0ef43303 100644
--- a/src/lightning/pytorch/trainer/connectors/logger_connector/result.py
+++ b/src/lightning/pytorch/trainer/connectors/logger_connector/result.py
@@ -19,7 +19,7 @@ import torch
 from lightning_utilities.core.apply_func import apply_to_collection
 from torch import Tensor
 from torchmetrics import Metric
-from typing_extensions import TypedDict
+from typing_extensions import TypedDict, override
 
 from lightning.fabric.utilities import move_data_to_device
 from lightning.fabric.utilities.apply_func import convert_tensors_to_scalars
@@ -204,6 +204,7 @@ class _ResultMetric(Metric):
         # this is defined here only because upstream is missing the type annotation
         self._forward_cache: Optional[Any] = None
 
+    @override
     def update(self, value: _VALUE, batch_size: int) -> None:
         if self.is_tensor:
             value = cast(Tensor, value)
@@ -242,6 +243,7 @@ class _ResultMetric(Metric):
             self.value = value
             self._forward_cache = value._forward_cache
 
+    @override
     def compute(self) -> Tensor:
         if self.is_tensor:
             value = self.meta.sync(self.value.clone())  # `clone` because `sync` is in-place
@@ -251,6 +253,7 @@ class _ResultMetric(Metric):
             return value
         return self.value.compute()
 
+    @override
     def reset(self) -> None:
         if self.is_tensor:
             super().reset()
@@ -258,6 +261,7 @@ class _ResultMetric(Metric):
             self.value.reset()
         self.has_reset = True
 
+    @override
     def forward(self, value: _VALUE, batch_size: int) -> None:
         if self.meta.enable_graph:
             with torch.no_grad():
@@ -266,6 +270,7 @@ class _ResultMetric(Metric):
             # performance: skip the `torch.no_grad` context manager by calling `update` directly
             self.update(value, batch_size)
 
+    @override
     def _wrap_compute(self, compute: Any) -> Any:
         # Override to avoid syncing - we handle it ourselves.
         @wraps(compute)
@@ -286,16 +291,19 @@ class _ResultMetric(Metric):
 
         return wrapped_func
 
+    @override
     def __setattr__(self, key: str, value: Any) -> None:
         # performance: skip the `torch.nn.Module.__setattr__` checks
         object.__setattr__(self, key, value)
 
+    @override
     def __repr__(self) -> str:
         state = f"{repr(self.meta.name)}, value={self.value}"
         if self.is_tensor and self.meta.is_mean_reduction:
             state += f", cumulated_batch_size={self.cumulated_batch_size}"
         return f"{self.__class__.__name__}({state})"
 
+    @override
     def to(self, *args: Any, **kwargs: Any) -> "_ResultMetric":
         d = self.__dict__
         if _TORCH_GREATER_EQUAL_2_0:  # https://github.com/pytorch/pytorch/issues/96198