This release include the new v0.4.0 and v0.3.[56] (never actually released) versions.

v0.4.0: More ops and fused ops; Added support for model Florence-2; `onnx` package refactored.

Package onnx: split implementation into internal/onnxgomlx/..., onnx is now just a public API.
- API change: it's a small change but onnx.Model is now an interface (it was a pointer to an object)
  and the constructors reside in onnx/parser.
Mod operator: Supports both fmod=1 (C-style, sign follows dividend) and fmod=0 (Python-style, sign follows divisor) with broadcasting and dtype promotion
onnxImplicitFloatPromotion for float-only ops (Sqrt, Exp, etc.).
Concat dtype alignment: When dtype promotion is enabled, all Concat operands are cast to the first operand's dtype, preserving Int64 for shape/index tensors.
isVariableConstant loosening: Float variables with "const" in the name are now accepted as materializable constants (needed when Concat dtype promotion casts Float32 constants to Int64).
Sub-graph name shadowing fix: convertSubGraph now saves and restores parent entries in nodeOutputToNode / variableNameToValue instead of unconditionally deleting them on cleanup.
convertIf() rework: Uses GoMLX's native If with closures instead of the Where-based approach.
Added support for Florence2 model;
Updated GoMLX dependency to v0.27.0.

v0.3.5: New ops for various models support (Gemma, Snowflake,CLAP, etc); Many fixes and improvements.

Added quantized fusion patterns for dense layers, QKV projections, and scaled dot-product attention (SDPA). (by @ajroetker)
Fixed isZeroInitializer to handle tensors with zero-sized dimensions (e.g., [batchSize, 0]).
Add ONNX operators for Gemma and Snowflake Arctic models: ReduceL2, SimplifiedLayerNormalization (RMSNorm),
RotaryEmbedding, MultiHeadAttention.
Simplified optional DType auto-promotion.
Added Resize operation for "CLAP" model support.
New Einsum op support (2 operands).
Various bug fixes across multiple ops and sub-graph handling.
Added ReduceMax, ReduceMin, ReduceSum, ReduceProd, and NonZero ONNX operations.
Optional auto-promotion of dtypes in case of mismatches: this is an error
in ONNX specification, but some Pytorch models do this. See Model.AllowDTypePromotion().
Added support for reading variables from external data files (due to proto 2/4 Gb size-limit)
Several fixes (see logs)