Fix raw PyTorch broadcast_arrays under NumPy backend

[FlorianPfaff]

Summary

• Patch raw pyrecest._backend.pytorch.broadcast_arrays from backend-support initialization so it accepts NumPy-style array-like inputs even when the public backend is not PyTorch.
• Keep the active public PyTorch backend synchronized when PYRECEST_BACKEND=pytorch.
• Add a subprocess regression for PYRECEST_BACKEND=numpy followed by direct raw PyTorch broadcast_arrays calls with list/scalar inputs.

Bug fixed

The raw PyTorch backend currently exposes torch.broadcast_tensors directly as broadcast_arrays. When the process selects the NumPy public backend and user code imports pyrecest._backend.pytorch directly, calls such as raw_backend.broadcast_arrays([1, 2], raw_backend.array([[3], [4]])) still reach Torch-native input validation and reject the list input. The patch normalizes all operands through the raw PyTorch backend array constructor before calling torch.broadcast_tensors.

Testing

• Not run locally in this connector session; added focused regression coverage in tests/backend_support/test_raw_pytorch_broadcast_arrays_default_backend.py.

[github-actions]

✅MegaLinter analysis: Success

Descriptor	Linter	Files	Fixed	Errors	Warnings	Elapsed time
✅ COPYPASTE	jscpd	yes		no	no	62.98s
✅ JSON	prettier	7	0	0	0	0.98s
✅ JSON	v8r	7		0	0	5.06s
✅ MARKDOWN	markdownlint	68	0	0	0	1.53s
✅ MARKDOWN	markdown-table-formatter	68	0	0	0	0.84s
✅ PYTHON	black	1132	4	0	0	57.14s
✅ PYTHON	isort	1132	4	0	0	2.8s
✅ REPOSITORY	checkov	yes		no	no	43.19s
✅ REPOSITORY	gitleaks	yes		no	no	10.44s
✅ REPOSITORY	git_diff	yes		no	no	0.14s
✅ REPOSITORY	secretlint	yes		no	no	43.15s
✅ REPOSITORY	syft	yes		no	no	6.03s
✅ REPOSITORY	trivy-sbom	yes		no	no	5.69s
✅ REPOSITORY	trufflehog	yes		no	no	15.26s
✅ YAML	prettier	11	0	0	0	0.66s
✅ YAML	v8r	11		0	0	10.82s
✅ YAML	yamllint	11		0	0	0.47s

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