Validate NumPy multinomial sample counts

[FlorianPfaff]

Summary

• validate NumPy backend random.multinomial(..., n=...) before dispatching to NumPy
• reject boolean, non-scalar, fractional, text, and negative sample counts with explicit errors
• add regression coverage while preserving integer-like scalar counts

Bug fixed

The NumPy backend already validated pvals, but passed n directly to numpy.random.multinomial. Because Python booleans are integer-like, calls such as random.multinomial(True, [1.0]) were accepted as one draw instead of being rejected consistently with the JAX/PyTorch backend contract.

Testing

• python -m py_compile /mnt/data/random.py /mnt/data/test_numpy_random_backend.py
• isolated validator check confirming boolean/fractional/text/non-scalar n inputs are rejected and an integer NumPy scalar is accepted

Full repository pytest was not run because this environment cannot clone the GitHub repository.

[github-actions]

✅MegaLinter analysis: Success

Descriptor	Linter	Files	Fixed	Errors	Warnings	Elapsed time
✅ COPYPASTE	jscpd	yes		no	no	69.47s
✅ JSON	prettier	7	0	0	0	0.95s
✅ JSON	v8r	7		0	0	6.53s
✅ MARKDOWN	markdownlint	68	0	0	0	1.72s
✅ MARKDOWN	markdown-table-formatter	68	0	0	0	0.8s
✅ PYTHON	black	1045	48	0	0	66.78s
✅ PYTHON	isort	1045	69	0	0	5.85s
✅ REPOSITORY	checkov	yes		no	no	48.39s
✅ REPOSITORY	gitleaks	yes		no	no	8.17s
✅ REPOSITORY	git_diff	yes		no	no	0.24s
✅ REPOSITORY	secretlint	yes		no	no	31.23s
✅ REPOSITORY	syft	yes		no	no	3.09s
✅ REPOSITORY	trivy-sbom	yes		no	no	11.85s
✅ REPOSITORY	trufflehog	yes		no	no	21.13s
✅ YAML	prettier	11	0	0	0	0.79s
✅ YAML	v8r	11		0	0	12.22s
✅ YAML	yamllint	11		0	0	0.55s

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