fix: SupportSTDP.stdp_update keyword typo and DSRunner deprecation warning

[chaoming0625]

Fixes the two latent runtime bugs flagged during the package-wide mypy pass (#863).

1. brainpy/mixin.py — onn_post keyword typo

The base SupportSTDP.stdp_update placeholder declared the keyword onn_post, while every concrete override (brainpy/dnn/linear.py) and every caller (brainpy/dyn/projections/plasticity.py, tests) uses on_post. The placeholder body only raise NotImplementedError, so this was a contract/signature defect rather than a wrong-result bug, but it was the source of the STDP [override] friction. Signature aligned to on_post.

The five overrides keep their targeted # type: ignore[override] (the base remains a permissive *args/**kwargs placeholder vs. their explicit params, so the LSP mismatch stands), but their comments no longer reference the now-fixed typo.

2. brainpy/runners.py:455 — raise warnings.warn(...)

DSRunner.predict did raise warnings.warn(...) for the deprecated inputs_are_batching argument. warnings.warn returns None, so raise None produced TypeError: exceptions must derive from BaseException instead of a warning. Now emits the UserWarning and continues, as intended.

Validation

• python -m mypy brainpy/ → Success: no issues found in 432 source files.
• New runtime check: predict(..., inputs_are_batching=True) now warns and continues (previously raised TypeError).
• Base SupportSTDP.stdp_update now accepts on_post= and still raises NotImplementedError.
• STDP suites (boost_linear_test.py, mixin_coverage_test.py, test_dense_stdp_update_promotes_weight): 91 passed.

Summary by Sourcery

Align STDP mixin and linear layer signatures and fix DSRunner deprecation handling so it emits a warning instead of raising an error.

Bug Fixes:

• Correct the SupportSTDP.stdp_update keyword name from onn_post to on_post to match concrete implementations and callers.
• Fix DSRunner.predict to use warnings.warn for the deprecated inputs_are_batching argument instead of incorrectly raising its return value.

[sourcery-ai]

Reviewer's guide (collapsed on small PRs)

Reviewer's Guide

Aligns the SupportSTDP.stdp_update interface with its concrete implementations by fixing a keyword typo, and corrects DSRunner.predict’s deprecation handling to emit a warning instead of raising a TypeError.

Sequence diagram for DSRunner.predict deprecation warning flow

sequenceDiagram
actor User
participant DSRunner
participant warnings
User->>DSRunner: predict(inputs, inputs_are_batching)
alt inputs_are_batching is not None
  DSRunner->>warnings: warn(message, UserWarning)
end
DSRunner-->>User: prediction_result

Loading

File-Level Changes

Change	Details	Files
Align SupportSTDP.stdp_update signature keyword with all concrete overrides and callers.	Rename the placeholder parameter from onn_post to on_post in SupportSTDP.stdp_update while keeping *args/**kwargs and raising NotImplementedError. Leave override methods in linear STDP modules unchanged in behavior but retain their explicit signatures and targeted type: ignore[override] annotations. Update inline comments on stdp_update overrides to remove references to the old onn_post typo while still explaining the override vs. permissive base signature mismatch.	brainpy/mixin.py brainpy/dnn/linear.py
Fix DSRunner.predict deprecation handling so inputs_are_batching triggers a warning and does not raise.	Replace raise warnings.warn(...) with a direct warnings.warn(...) call to avoid raising None. Keep the deprecation message content and UserWarning type the same, aside from trimming trailing whitespace in the message string.	brainpy/runners.py

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[sourcery-ai]

Hey - I've found 1 issue, and left some high level feedback:

• Consider aligning the SupportSTDP.stdp_update signature with the concrete implementations (dropping *args/**kwargs and using explicit parameters) so the override relationship is LSP-compliant and the # type: ignore[override] hints can eventually be removed.
• In DSRunner.predict, you may want to set an explicit stacklevel on warnings.warn so that the deprecation warning points at the caller rather than the internal runner implementation.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- Consider aligning the `SupportSTDP.stdp_update` signature with the concrete implementations (dropping `*args/**kwargs` and using explicit parameters) so the override relationship is LSP-compliant and the `# type: ignore[override]` hints can eventually be removed.
- In `DSRunner.predict`, you may want to set an explicit `stacklevel` on `warnings.warn` so that the deprecation warning points at the caller rather than the internal runner implementation.

## Individual Comments

### Comment 1
<location path="brainpy/runners.py" line_range="455-460" />
<code_context>

         if inputs_are_batching is not None:
-            raise warnings.warn(  # type: ignore[misc]  # latent bug: warnings.warn returns None; preserved to avoid behavior change
+            warnings.warn(
                 f'''
-        `inputs_are_batching` is no longer supported. 
+        `inputs_are_batching` is no longer supported.
         The target mode of {self.target.mode} has already indicated the input should be batching.
         ''',
                 UserWarning
</code_context>
<issue_to_address>
**suggestion:** Refine the warning to give a clearer location and potentially a more specific warning type.

With the misuse of `raise warnings.warn(...)` corrected, consider setting an appropriate `stacklevel` (e.g., `stacklevel=2`) so the warning points to the caller instead of the runner internals. Since this concerns a deprecated argument, using `DeprecationWarning` or a dedicated warning subclass instead of `UserWarning` would better express the deprecation intent.
</issue_to_address>

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[sourcery-ai]

suggestion: Refine the warning to give a clearer location and potentially a more specific warning type.

With the misuse of raise warnings.warn(...) corrected, consider setting an appropriate stacklevel (e.g., stacklevel=2) so the warning points to the caller instead of the runner internals. Since this concerns a deprecated argument, using DeprecationWarning or a dedicated warning subclass instead of UserWarning would better express the deprecation intent.