## Type-Safe Tensor Operations and Package Distribution

### Introduction:
The 'tensor_ops' Python package provides a type-safe interface for basic tensor operations using PyTorch, aimed at improving maintainability, testability, and robustness. This package includes unit tests and documentation, ensuring it is well-documented and easy to use.

### Dependencies:
- Python 3.8+
- PyTorch

### Installation
```sh
pip install torch
```

### Usage Example:
```python
from tensor_ops import add_tensors

# Initialize tensors with proper typing for better type safety
a = torch.tensor([1, 2])
b = torch.tensor([3, 4])

result = add_tensors(a, b)
print(result)  # Output: tensor([4, 6])
```

### Running Tests
```sh
pytest

--- ATTEMPT: initial (code=0) ---
--- STDOUT ---
--- RUNTIME PROFILE ---
Device policy: gpu_preferred
Torch: 2.11.0+rocm7.1
Accelerator backend: rocm
Torch CUDA build: None
Torch HIP build: 7.1.52802
CUDA available: True
CUDA device count: 1
CUDA device[0]: AMD Radeon 890M Graphics
Accelerator memory total: 73728.0 MB
Accelerator memory used: 16880.2 MB
Recommended autocast dtype: bf16
Recommended DataLoader pin_memory: True
Recommended DataLoader num_workers: 12
Recommended starting batch size: 64
Recommended CPU threads: 24
/dev/kfd present: True

VRAM_USAGE: 126.41MB
TOKENS_PER_SEC: 9876

VERIFIED: Benchmark passed all checks.

--- STDERR ---


--- HUMAN SUMMARY (LAYMAN) ---
Result: The test completed successfully.
Benchmark script conclusion: VERIFIED: Benchmark passed all checks.