Fix static shape inference in pt.linalg.kron and add regression test

[ayulockedin]

Description

This PR resolves the issue where pt.linalg.kron destroys static shape information, returning (None, None) even when input shapes are fully known.

The previous implementation relied on a vector-wise symbolic multiplication of shapes:
out_shape = tuple(a.shape * b.shape)

This forced the underlying Reshape Op to treat the entire shape vector as a single symbolic entity, which prevented the ShapeFeature from constant-folding individual dimensions into their static values.

The Fix

I implemented element-wise symbolic multiplication for the output shape:
[a.shape[i] * b.shape[i] for i in range(a.ndim)]

This provides the shape inference engine with enough granularity to resolve static constants (e.g., 4 * 3 = 12) at compile time while maintaining the symbolic integrity required for downstream operations like clone_replace.

Related Issue

• Closes pt.linalg.kron destroys static shape information #1867
• Related to #

Checklist

• Checked that the pre-commit linting/style checks pass
• Included tests that prove the fix is effective or that the new feature works
• Added necessary documentation (docstrings and/or example notebooks)
• If you are a pro: each commit corresponds to a relevant logical change

Type of change

• New feature / enhancement
• Bug fix
• Documentation
• Maintenance
• Other (please specify):

[ayulockedin]

@jessegrabowski can you have a look at this when u have a moment thx

[jessegrabowski]

Solution looks good. Just needs some cleanup.

[jessegrabowski]

out is a numpy array. You need to test the shape of symbolic kron(x, y), not the numerical output of the compiled function.

[ayulockedin]

Good catch. I was asserting against the compiled numerical output instead of the symbolic graph. I've updated the test to instantiate explicitly static tensors and assert against kron(x_static, y_static).type.shape to ensure the static shape information is properly preserved by the shape inference engine.