Ensure inference fallback respects dynamic policy size

[lukifer23]

Summary

• add a helper to derive the policy tensor width from shared memory resources
• use the derived width when building fallback policy logits on the server and client paths

Testing

• python - <<'PY' ...

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https://chatgpt.com/codex/tasks/task_e_68d1bc54bd888323b9feb57ff68200d9

[Copilot]

Pull Request Overview

This PR ensures that inference fallback logic respects dynamic policy tensor sizes rather than using hardcoded dimensions. The changes add a helper function to dynamically derive policy width from shared memory resources and update both server and client paths to use this derived width when creating fallback policy logits.

• Added _get_policy_width_from_resource() helper function to extract policy tensor dimensions
• Updated server fallback logic to derive policy width from worker resources
• Updated client fallback logic to use dynamic policy width instead of hardcoded 4672

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[Copilot]

The function returns 0 for invalid width values, but later code uses this as a tensor dimension. A zero-width tensor would cause runtime errors. Consider returning a sensible default width or raising an exception to fail fast.

[Copilot]

When policy_width is 0 (from the helper function), this creates a tensor with shape (batch_size, 0) which will likely cause errors in downstream code expecting valid policy dimensions.

[Copilot]

Similar to the server path, creating a zero-width policy tensor when policy_width is 0 will cause runtime errors. The fallback should ensure a valid tensor dimension.

Suggested change

	)
	)
	policy_width = max(1, policy_width)