Improve CI stability for clustering examples (#2785)

* Update src/torchmetrics/functional/clustering/calinski_harabasz_score.py
* Update src/torchmetrics/functional/clustering/davies_bouldin_score.py

src/torchmetrics/clustering/calinski_harabasz_score.py

@@ -56,11 +56,11 @@ class CalinskiHarabaszScore(Metric):
     Example::
         >>> from torch import randn, randint
         >>> from torchmetrics.clustering import CalinskiHarabaszScore
-        >>> data = randn(10, 3)
-        >>> labels = randint(3, (10,))
+        >>> data = randn(20, 3)
+        >>> labels = randint(3, (20,))
         >>> metric = CalinskiHarabaszScore()
         >>> metric(data, labels)
-        tensor(3.0053)
+        tensor(2.2128)
 
     """
 
@@ -108,7 +108,7 @@ def plot(self, val: Union[Tensor, Sequence[Tensor], None] = None, ax: Optional[_
             >>> import torch
             >>> from torchmetrics.clustering import CalinskiHarabaszScore
             >>> metric = CalinskiHarabaszScore()
-            >>> metric.update(torch.randn(10, 3), torch.randint(0, 2, (10,)))
+            >>> metric.update(torch.randn(20, 3), torch.randint(3, (20,)))
             >>> fig_, ax_ = metric.plot(metric.compute())
 
         .. plot::
@@ -120,7 +120,7 @@ def plot(self, val: Union[Tensor, Sequence[Tensor], None] = None, ax: Optional[_
             >>> metric = CalinskiHarabaszScore()
             >>> values = [ ]
             >>> for _ in range(10):
-            ...     values.append(metric(torch.randn(10, 3), torch.randint(0, 2, (10,))))
+            ...     values.append(metric(torch.randn(20, 3), torch.randint(3, (20,))))
             >>> fig_, ax_ = metric.plot(values)
 
         """

src/torchmetrics/functional/clustering/calinski_harabasz_score.py

@@ -33,10 +33,10 @@ def calinski_harabasz_score(data: Tensor, labels: Tensor) -> Tensor:
     Example:
         >>> from torch import randn, randint
         >>> from torchmetrics.functional.clustering import calinski_harabasz_score
-        >>> data = randn(10, 3)
-        >>> labels = randint(0, 2, (10,))
+        >>> data = randn(20, 3)
+        >>> labels = randint(0, 3, (20,))
         >>> calinski_harabasz_score(data, labels)
-        tensor(3.4998)
+        tensor(2.2128)
 
     """
     _validate_intrinsic_cluster_data(data, labels)

src/torchmetrics/functional/clustering/davies_bouldin_score.py

@@ -33,10 +33,10 @@ def davies_bouldin_score(data: Tensor, labels: Tensor) -> Tensor:
     Example:
         >>> from torch import randn, randint
         >>> from torchmetrics.functional.clustering import davies_bouldin_score
-        >>> data = randn(10, 3)
-        >>> labels = randint(0, 2, (10,))
+        >>> data = randn(20, 3)
+        >>> labels = randint(0, 3, (20,))
         >>> davies_bouldin_score(data, labels)
-        tensor(1.3249)
+        tensor(2.7418)
 
     """
     _validate_intrinsic_cluster_data(data, labels)