Fix non-vectorized Dirac transforms across backends

[FlorianPfaff]

Summary

• Map non-vectorized Dirac transforms over complete Dirac particles instead of delegating to backend apply_along_axis.
• Remove the fragile apply_along_axis(..., axis=1, ...) dependency from AbstractDiracDistribution.apply_function.
• Add default-backend and optional PyTorch-backend regression coverage.

Bug fixed

AbstractDiracDistribution.apply_function(..., function_is_vectorized=False) intended to call the provided function once per Dirac particle. The previous implementation used backend.apply_along_axis(f, 1, dist.d), which is backend-sensitive and, under the PyTorch backend, iterates over coordinate columns rather than particle rows and stacks the result along the wrong axis. This can silently produce invalid transformed particles.

Validation

• Compared branch against current main: 2 commits ahead, 0 behind.
• Added focused regression coverage in tests/distributions/test_abstract_dirac_distribution.py.
• Full local pytest was not run because the execution sandbox cannot resolve github.com; repository CI should run the focused tests and full matrix.

[github-actions]

✅MegaLinter analysis: Success

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✅ PYTHON	isort	766	0	0	0	2.39s
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✅ REPOSITORY	git_diff	yes		no	no	0.05s
✅ REPOSITORY	secretlint	yes		no	no	22.65s
✅ REPOSITORY	syft	yes		no	no	3.72s
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✅ REPOSITORY	trufflehog	yes		no	no	23.84s
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