Fix scalar Gaussian log-pdf backend shape

[FlorianPfaff]

Summary

• preserve scalar output shape for one-dimensional GaussianDistribution.ln_pdf scalar inputs after PyTorch/JAX event-dimension reshaping
• keep one-dimensional array inputs as a batch of scalar evaluation points
• add a backend-portable regression covering scalar ln_pdf, scalar pdf, and one-element batch ln_pdf across NumPy, PyTorch, and JAX

Bug fixed

For a one-dimensional Gaussian, scalar inputs are valid evaluation points. The NumPy/SciPy path returns a scalar log-density, but the PyTorch and JAX paths reshape scalar inputs to (-1, 1) for their multivariate-normal APIs and previously returned the resulting length-1 batch unchanged. The fix records scalar input shape before reshaping and reshapes the returned log-density back to ().

Validation

• python -m py_compile src/pyrecest/distributions/nonperiodic/gaussian_distribution.py tests/backend_support/test_gaussian_scalar_logpdf_contract.py
• Checked the underlying NumPy/PyTorch/JAX scalar-shape behavior in an isolated local snippet.
• Full repository tests were not run locally because this environment could not clone/resolve github.com; CI should run the backend-portable regression.

[github-actions]

✅MegaLinter analysis: Success

Descriptor	Linter	Files	Fixed	Errors	Warnings	Elapsed time
✅ COPYPASTE	jscpd	yes		no	no	53.64s
✅ JSON	prettier	7	0	0	0	0.7s
✅ JSON	v8r	7		0	0	4.0s
✅ MARKDOWN	markdownlint	68	0	0	0	1.34s
✅ MARKDOWN	markdown-table-formatter	68	0	0	0	0.65s
✅ PYTHON	black	1175	26	0	0	45.89s
✅ PYTHON	isort	1175	44	0	0	3.07s
✅ REPOSITORY	checkov	yes		no	no	37.59s
✅ REPOSITORY	gitleaks	yes		no	no	9.23s
✅ REPOSITORY	git_diff	yes		no	no	0.22s
✅ REPOSITORY	secretlint	yes		no	no	26.81s
✅ REPOSITORY	syft	yes		no	no	3.5s
✅ REPOSITORY	trivy-sbom	yes		no	no	9.31s
✅ REPOSITORY	trufflehog	yes		no	no	15.24s
✅ YAML	prettier	11	0	0	0	0.56s
✅ YAML	v8r	11		0	0	9.22s
✅ YAML	yamllint	11		0	0	0.37s

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