[FIX] Fix PyKeops tensor operations and ensure contiguous arrays

[Leguark]

Fix PyKeops tensor operations and improve performance

This PR addresses several issues with tensor operations, particularly when using PyKeops:

1. Add contiguous array check and conversion for PyKeops tensors
2. Fix matrix multiplication in symbolic evaluator by correcting axis parameters
3. Remove commented-out code in fancy_triangulation
4. Add a comment about CUDA device check slowing down code
5. Ensure tensors are contiguous before passing to LazyTensor

These changes improve the reliability of tensor operations across different backends and fix potential issues with non-contiguous arrays that could cause PyKeops to fail.

[Leguark]

• [FIX] Reuse weights calculation instead of raising error on hash mismatch #27
• [ENH] Refactor dual contouring with parallel and sequential triangulation modules #26
• [ENH] Refactor dual contouring code with improved documentation and modular functions #25
• [REFACTOR] Use center grid for dual contouring instead of corners grid #24
• [FIX] Use timestamp for cache key instead of input parameters #23
• [FIX] Fix PyKeops tensor operations and ensure contiguous arrays #22 👈 (View in Graphite)
• [ENH] Add PyTorch backend support for dual contouring and octree operations #21
• [!!BUG] Chunking with faults were creating rogue voxels | GEN-13299 #20
• [ENH] Add transform support to meshes_to_subsurface method #19
• [FIX] Separate pykeops_enabled flag from use_pykeops for better control #18
• [ENH] Improve CG solver and add weights caching for better performance #17
• main

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[Leguark]

Merge activity

• Sep 24, 2:24 PM UTC: A user started a stack merge that includes this pull request via Graphite.
• Sep 24, 2:24 PM UTC: @Leguark merged this pull request with Graphite.