rustorch 0.6.29

Production-ready PyTorch-compatible deep learning library in Rust with special mathematical functions (gamma, Bessel, error functions), statistical distributions, Fourier transforms (FFT/RFFT), matrix decomposition (SVD/QR/LU/eigenvalue), automatic differentiation, neural networks, computer vision transforms, complete GPU acceleration (CUDA/Metal/OpenCL), SIMD optimizations, parallel processing, WebAssembly browser support, comprehensive distributed learning support, and performance validation
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# Clippy Refactoring Progress

## Status: 269 total warnings (23 fixed)
Current focus: High-impact, easy-to-fix warnings first

## Priority Fixes (High Impact, Easy)

### 1. manual_div_ceil (20 warnings) - PRIORITY 1
**Pattern**: `(a + b - 1) / b` → `a.div_ceil(b)`
**Impact**: GPU kernel performance patterns
**Status**: READY TO FIX

### 2. new_without_default (18 warnings) - PRIORITY 2  
**Pattern**: Add `#[derive(Default)]` or `impl Default`
**Impact**: API completeness, ergonomics
**Status**: READY TO FIX

### 3. too_many_arguments (12 warnings) - PRIORITY 3
**Pattern**: Functions with >7 arguments need parameter structs
**Impact**: API design, maintainability  
**Status**: NEEDS CAREFUL ANALYSIS

### 4. needless_borrows_for_generic_args (7 warnings) - PRIORITY 4
**Pattern**: Remove `&` from format strings and error messages
**Impact**: Performance micro-optimization
**Status**: READY TO FIX

## Other Common Patterns (5+ warnings)
- manual_slice_size_calculation
- unwrap_or_default  
- needless_borrow
- needless_range_loop
- derivable_impls

## Strategy
1. Fix div_ceil patterns (GPU kernels)
2. Add Default implementations 
3. Fix borrows and conversions
4. Analyze too_many_arguments for API improvements
5. Handle remaining one-off patterns

## Progress Tracking
- [x] manual_div_ceil: 20/20 fixed ✅
- [ ] new_without_default: 4/18 fixed (14 remaining)  
- [ ] needless_borrows: 0/7 fixed
- [ ] too_many_arguments: 0/12 analyzed