Add CUDA memory management APIs

[alinpahontu2912]

Fixes
Add the following torch.cuda APIs:

• empty_cache() - Release unoccupied cached memory (add empty_cache function #1521)
• memory_allocated() - Current GPU memory occupied by tensors
• max_memory_allocated() - Peak GPU memory occupied by tensors
• reset_peak_memory_stats() - Reset peak memory tracking
• memory_reserved() - Current GPU memory managed by caching allocator
• max_memory_reserved() - Peak GPU memory managed by caching allocator
• mem_get_info() - Free and total memory on device
• set_device() - Set current CUDA device
• current_device() - Get current CUDA device index

These APIs are commonly used in PyTorch workflows for memory management and debugging, and are needed by TorchSharpExamples users.

Native implementations use c10::cuda::CUDACachingAllocator with #if defined(USE_CUDA) guards for CPU-only build compatibility.

Includes unit tests for all new APIs.

[Copilot]

Pull request overview

This PR adds 9 new CUDA memory management APIs to TorchSharp, addressing issue #1521. These APIs provide essential functionality for monitoring and managing GPU memory usage, which is commonly needed in PyTorch workflows for debugging and optimization.

Changes:

• Adds CUDA memory management APIs: empty_cache, memory_allocated, max_memory_allocated, reset_peak_memory_stats, memory_reserved, max_memory_reserved, mem_get_info, set_device, and current_device
• Implements native C++ bindings with USE_CUDA guards for CPU-only build compatibility
• Includes comprehensive unit tests for all new APIs
• Bumps version from 0.106.0 to 0.106.1

Reviewed changes

Copilot reviewed 7 out of 7 changed files in this pull request and generated 2 comments.

Show a summary per file

File	Description
src/TorchSharp/Torch.cs	Adds 9 new public CUDA memory management methods with XML documentation
src/TorchSharp/PInvoke/LibTorchSharp.THSTorchCuda.cs	Adds P/Invoke declarations for the new native methods
src/Native/LibTorchSharp/THSTorch.h	Declares native function signatures for CUDA memory APIs
src/Native/LibTorchSharp/THSTorch.cpp	Implements native functions using c10::cuda APIs with USE_CUDA guards and CPU-only stubs
test/TorchSharpTest/TestTorchSharp.cs	Adds comprehensive unit tests for all 9 new APIs
build/BranchInfo.props	Bumps patch version from 0.106.0 to 0.106.1
RELEASENOTES.md	Documents the new APIs in release notes

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[Copilot]

The documentation refers to "PyTorch" but this is TorchSharp. Update this to say "doesn't increase the amount of GPU memory available for TorchSharp" to accurately reflect the library being documented.

Suggested change

	/// empty_cache() doesn't increase the amount of GPU memory available for PyTorch.
	/// empty_cache() doesn't increase the amount of GPU memory available for TorchSharp.

[Copilot]

Consider saving the original device at the beginning of the test and restoring it at the end to avoid potential side effects on other tests. While the tests are marked as Sequential and most explicitly set devices, it's good practice to restore the original state. You can use a try-finally block or wrap it in a using statement pattern to ensure cleanup.

Suggested change

	// Set to device 0 (always valid if CUDA is available)
	cuda.set_device(0);
	Assert.Equal(0, cuda.current_device());
	// Set to device 0 (always valid if CUDA is available) and restore original device afterwards
	try {
	cuda.set_device(0);
	Assert.Equal(0, cuda.current_device());
	}
	finally {
	cuda.set_device(device);
	}