Harden PyTorch random scalar input validation

[FlorianPfaff]

Summary

• accept zero-dimensional NumPy integer arrays and scalar PyTorch integer tensors as size dimensions in PyTorch random samplers
• reject zero-dimensional boolean and floating-point array/tensor sizes with the backend's standard size validation error
• validate random.choice(..., p=...) before casting probabilities so complex probabilities are rejected instead of silently truncated
• add PyTorch backend regression coverage for scalar array/tensor size inputs and complex choice probabilities

Testing

• Not run as a full repository pytest suite here; the repository could not be cloned in this environment due DNS resolution for github.com.
• Performed standalone validation of the changed helper paths for accepted/rejected scalar sizes and complex choice probabilities.

[github-actions]

✅MegaLinter analysis: Success

Descriptor	Linter	Files	Fixed	Errors	Warnings	Elapsed time
✅ COPYPASTE	jscpd	yes		no	no	47.07s
✅ JSON	prettier	7	0	0	0	0.8s
✅ JSON	v8r	7		0	0	4.48s
✅ MARKDOWN	markdownlint	68	0	0	0	1.37s
✅ MARKDOWN	markdown-table-formatter	68	0	0	0	0.72s
✅ PYTHON	black	1051	51	0	0	45.26s
✅ PYTHON	isort	1051	73	0	0	3.53s
✅ REPOSITORY	checkov	yes		no	no	40.87s
✅ REPOSITORY	gitleaks	yes		no	no	7.33s
✅ REPOSITORY	git_diff	yes		no	no	0.17s
✅ REPOSITORY	secretlint	yes		no	no	23.59s
✅ REPOSITORY	syft	yes		no	no	4.02s
✅ REPOSITORY	trivy-sbom	yes		no	no	4.7s
✅ REPOSITORY	trufflehog	yes		no	no	19.55s
✅ YAML	prettier	11	0	0	0	0.57s
✅ YAML	v8r	11		0	0	8.32s
✅ YAML	yamllint	11		0	0	0.36s

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