Fix PyTorch mean integer backend inputs

[FlorianPfaff]

Summary

• Promote non-floating/non-complex PyTorch backend mean inputs to the configured default floating dtype before reduction.
• Preserve explicit dtype handling and NumPy-style axis/keepdims semantics.
• Add a forced-PyTorch regression test for integer Python-list inputs.

Rationale

The public backend facade generally accepts NumPy-style array-like inputs. For the PyTorch backend, backend.mean([[1, 2, 3], [4, 5, 6]], axis=0, keepdims=True) previously converted the list to an integer tensor and forwarded it to torch.mean, which rejects integer tensors. NumPy returns a floating result for the same input.

Validation

• Added tests/test_backend_mean_contract.py::test_pytorch_mean_accepts_integer_array_like_inputs.
• Full local pytest could not be run from this execution environment; repository CI should run the focused regression and full matrix.

[github-actions]

✅MegaLinter analysis: Success

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✅ PYTHON	isort	764	0	0	0	2.23s
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✅ REPOSITORY	gitleaks	yes		no	no	17.3s
✅ REPOSITORY	git_diff	yes		no	no	0.11s
✅ REPOSITORY	secretlint	yes		no	no	22.26s
✅ REPOSITORY	syft	yes		no	no	4.89s
✅ REPOSITORY	trivy-sbom	yes		no	no	5.72s
✅ REPOSITORY	trufflehog	yes		no	no	22.16s
✅ YAML	prettier	11	0	0	0	0.63s
✅ YAML	v8r	11		0	0	10.46s
✅ YAML	yamllint	11		0	0	0.49s

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