2.2.1 Summary This PR delivers a broad flatbuffer_direct upgrade focused on three goals: feature parity with the TF-converter path where practical, safer dtype/shape handling for dynamic graphs, stronger final-stage graph optimization for layout-heavy models. It includes the two commits in fix-sgsch: d20dba8 Optimize redundant transpose chains in flatbuffer_direct e7641fa Update flatbuffer_direct converters, metadata handling, and tests Key Improvements 1) FP16 accumulation metadata parity between TF-converter and flatbuffer_direct Added propagation of enable_accumulation_type_float16 into flatbuffer-direct export calls. Added model-level metadata plumbing to serialize reduced_precision_support in direct-written TFLite flatbuffers. Metadata now correctly reflects: fp16accfp16 when -eatfp16 is enabled, fp16accfp32 otherwise. This addresses mismatch cases where *_float16.tflite metadata stayed fp16accfp32 despite -eatfp16. 2) New/expanded builtin op coverage Added CumProd builtin lowering path (build_cumprod_op) using a TFLite decomposition: RANGE, LESS/LESS_EQUAL, RESHAPE, TILE, FILL, SELECT_V2, REDUCE_PROD, optional REVERSE_V2. Added Unique builtin lowering path (build_unique_op) with validation constraints. Added CastLike builtin lowering path (build_castlike_op). Added Upsample dispatch entry to reuse Resize lowering/validation path. Added UNIQUE options serialization support in model writer (UniqueOptions). 3) Scatter/Index robustness upgrades ScatterND: support numeric updates dtype (with casting to data dtype as needed), normalize negative indices to ONNX semantics before scatter, improve dynamic-shape compatibility for shape/signature handling. ScatterElements: relaxed/expanded rank handling for indices/updates, runtime shape handling for dynamic updates, improved coordinate assembly for mismatched rank scenarios. 4) Dtype harmonization and 64-bit normalization improvements Added consistent INT64 -> INT32 and UINT64 -> UINT32 normalization in critical paths. Improved binary op dtype selection for mixed integer/float inputs and comparison ops. Added unary/reduce dtype harmonization helpers to prevent runtime mismatches after upstream dtype normalization. Improved Where/control-flow mux dtype normalization for signed/unsigned integer outputs. Added constant-input CAST folding and redundant INT64->INT32 CAST chain cleanup in post-lowering optimization. 5) Dynamic shape and reshape stability fixes Reworked dynamic reshape resolution behavior: preserves runtime-driven newShape=[] intent when required, uses safer template sanitization for 0 / multiple -1 cases, avoids over-resolving shapes that should remain runtime-dynamic, better treatment of onnxRawNewShape with existing concrete newShape. Improved replacement logic for EXPAND_DIMS / SQUEEZE to RESHAPE: dynamic cases are preserved where static rewrite would be unsafe, dynamic squeeze rewrite path uses runtime shape plumbing when valid. Improved static-shape reconciliation and signature sanitation for edge dynamic-lineage cases. 6) Layout/transpose optimization expansion Added and integrated multiple strict/final-stage optimization passes in lower_from_onnx2tf.py, including: transpose-split mixed pre-concat consolidation, channel-shuffle reshape/transpose rewrite to gather, transpose-gather-transpose axis-remap cleanup, post-transpose InstanceNorm/bias/add chain simplification, affine chain folding (mul/add/mul forms), split-conv-concat bridge rewrite to single post adapter, concat/mul/add/transpose(+add/mean/reshape-tail) bridge reductions, additional terminal/final-pass reapplication for late-introduced patterns. These reduce redundant transposes/casts and stabilize final graph topology after late rewrites. 7) GridSample and pooling support expansion GridSample: expanded support to rank-5 border padding path (padding_mode in {zeros, border}), refined indexing/clipping behavior and flattened indexing paths for border/zero modes. MaxPool1D: expanded kernel/stride handling constraints (positive kernel/stride with more explicit indices-mode checks). 8) Accuracy evaluation and error-report runtime resilience Accuracy evaluator now supports safer dynamic-dimension defaults and layout-aware dynamic dimension inference. Added runtime TFLite input resize helper usage in evaluator and direct-op error report path. Prevents shape-signature incompatibility issues during validation/inference when model inputs are dynamic. 9) Control-flow handling improvements Extended supported If NMS-guard subpattern variant. Improved generic If branch mux dtype normalization. Strengthened Loop state output remapping/casting logic for dtype/shape consistency. 10) Documentation and release updates Updated README builtin coverage/constraints table to reflect current direct-lowering capabilities. Clarified that accumulation-type option applies to both tf_converter and flatbuffer_direct paths. Version bumped: onnx2tf package version 2.2.0 -> 2.2.1 Docker tags in README updated accordingly. Validation Executed full direct-builder regression: pytest -q tests/test_tflite_builder_direct.py Result: 554 passed, 1 warning, 0 failures. Also added/updated extensive tests for: CumProd builtin dispatch, Unique builtin lowering, ScatterND negative-index normalization and dtype casting, Upsample opset9 / Pad opset9 compatibility, dynamic reshape/signature preservation, transpose/layout optimization rewrites, integer dtype normalization in control-flow and elementwise paths, GridSample rank-5 border validation. What's Changed flatbuffer_direct: add CumProd/Unique/CastLike, fp16 metadata parity, and shape/dtype optimizations by @PINTO0309 in https://github.com/PINTO0309/onnx2tf/pull/898 Full Changelog: https://github.com/PINTO0309/onnx2tf/compare/2.2.0...2.2.1

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