Fix NMI/AMI returning 0.0 instead of 1.0 for two single-cluster labelings

[AlejandroCoronadoN]

Fixes #3469.

normalized_mutual_info_score and adjusted_mutual_info_score return 0.0 when both preds and target are a single cluster (two zero-entropy labelings). scikit-learn, the documented reference for these metrics, returns 1.0 for this limit case because the two labelings trivially agree.

Repro:

import torch, numpy as np
import sklearn.metrics as skm
import torchmetrics.functional.clustering as tmc

a = np.array([0, 0, 0, 0]); ta = torch.tensor(a)
print(float(tmc.normalized_mutual_info_score(ta, ta)), skm.normalized_mutual_info_score(a, a))  # 0.0 vs 1.0
print(float(tmc.adjusted_mutual_info_score(ta, ta)), skm.adjusted_mutual_info_score(a, a))        # 0.0 vs 1.0

Fix: return 1.0 early when both labelings have exactly one unique value, matching sklearn. For adjusted_mutual_info_score I also added the check_cluster_labels(preds, target) call that the other clustering metrics already use, so the early return does not bypass input validation (float inputs still raise, verified). No non-degenerate result changes.

Tests: added a both_single_cluster case parametrized over every average_method to both functional test files, using sklearn as the reference oracle. ruff is clean.

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Additional details and impacted files

@@           Coverage Diff           @@
##           master   #3470    +/-   ##
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- Coverage      37%     33%    -4%     
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  Files         349     349            
  Lines       19901   19914    +13     
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- Hits         7264    6524   -740     
- Misses      12637   13390   +753     

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[AlejandroCoronadoN]

Thanks for the review, @justusschock. This has drifted a bit behind master; happy to rebase it if that helps it land, and glad to make any other changes you need.