Implement fallback for unimplemented Numba RandomVariables (#1245)

[SwarnimShekhar]

Description

This PR updates the perform method in pytensor/tensor/random/op.py to gracefully handle unimplemented Numba RandomVariables by falling back to object mode. The changes include:

Fallback Mechanism:
When a RandomVariable is not implemented in Numba, the code now falls back instead of raising a low-level error.

Improved Size Validation:
A helper function _get_value (using get_underlying_scalar_constant_value) is introduced to evaluate symbolic constants in the size parameter. The validation now uses broadcasting rules (via zip_longest) to check that the provided size is compatible with the expected batch dimensions. If not, a ValueError is raised with the message:
"Size length is incompatible with batched dimensions."

Known Issues:
Two tests—test_RandomVariable_bcast_specify_shape and parts of test_RandomVariable_bcast—still fail due to output shape inference (e.g., the first dimension remains None instead of being concrete). This appears to be a separate type inference issue and is not directly related to the fallback mechanism. I plan to address this in a subsequent PR.

Related Issue

• Closes #
• 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):

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📚 Documentation preview 📚: https://pytensor--1269.org.readthedocs.build/en/1269/