fix: use float64 accumulators in PearsonCorrelation to prevent catastrophic cancellation

[tejasae-afk]

Fixes #3662.

The naive E[X²] - (E[X])² formula for variance is mathematically correct but numerically unstable. When values have large magnitude relative to their variance — for example an offset of 1e8 with small inter-sample differences — both E[X²] and (E[X])² are around 1e16 while their difference (the actual variance) is O(1). In float32, the unit in the last place at that scale is roughly 10^9, so the variance is completely lost and the metric returns 0.0.

The fix is to accumulate in float64 on devices that support it. MPS does not support float64, so it falls back to float32 and retains the previous behaviour. The public return type (Python float) is unchanged.

# before (float32 accumulators)
offset = 1e8
y_pred = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0], dtype=torch.float64) + offset
y      = torch.tensor([2.0, 4.0, 6.0, 8.0, 10.0], dtype=torch.float64) + offset
m = PearsonCorrelation()
m.update((y_pred, y))
m.compute()  # 0.0  (wrong; expected 1.0)

# after (float64 accumulators)
m.compute()  # 1.0  ✓

A new test test_numerical_stability_large_offset covers this case. All existing non-distributed tests pass.

you know the codebase far better than I do — happy to adjust if a different approach (e.g., a shared Welford-based utility across PearsonCorrelation, R2Score, and FID as discussed in the issue) is preferred.