fix: guard optional validation fields in CSAI forward during inference

[shaun0927]

Description

In pypots/imputation/csai/core.py, _BCSAI.forward ends with:

if calc_criterion:
    if self.training:
        ...
    else:  # validation stage
        X_ori, indicating_mask = inputs["X_ori"], inputs["indicating_mask"]
        ...

if not self.training:
    results["x_ori"] = inputs["X_ori"]
    results["indicating_mask"] = inputs["indicating_mask"]

The first block correctly gates X_ori / indicating_mask behind
calc_criterion (those keys only exist in validation batches). The second
block runs whenever self.training is False — including plain predict()
calls where the user's Dataset has no ground-truth fields — and raises
KeyError: 'X_ori' there.

The unconditional accesses were introduced when CSAI was re-landed in PR
#788 and never made it through a predict-only test. This PR restores the
invariant by guarding the accesses so they only fire when the keys actually
exist in the input dict.

Changes

• pypots/imputation/csai/core.py: wrap the results["x_ori"] and
  results["indicating_mask"] assignments in if "X_ori" in inputs: /
  if "indicating_mask" in inputs: checks. Semantically identical for any
  caller that already provides those keys (validation); now safe for pure
  inference.

Testing

• Validation flow is unchanged: when the caller feeds a batch with both
  keys present, both entries are added to results just as before.
• Pure-inference flow (user passes only X, missing_mask, the deltas_*
  / last_obs_* pair) now returns the regular result dict without raising
  KeyError.

No changes to model weights, training loss, or validation metric; the fix
is limited to two safe attribute lookups.

[sonarqubecloud]

Quality Gate passed

Issues
0 New issues
0 Accepted issues

Measures
0 Security Hotspots
0.0% Coverage on New Code
0.0% Duplication on New Code

See analysis details on SonarQube Cloud

[github-actions]

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