Tools to convert ONNX files (NCHW) to TensorFlow format (NHWC)

Summary

• prefer the identity permutation for NHWC-preserved InstanceNormalization inputs when candidate permutation errors are effectively tied
• require non-identity permutations to beat identity by more than the existing 1e-2 strict-mode acceptance threshold before selecting them
• keep the original minimum-error behavior for non-NHWC inputs and clear non-identity wins

Refs #930

Review notes

The issue attachment was downloadable and the reported command path was exercised locally. In this scratch environment, that full model still fails later in a downstream Conv shape mismatch, so this PR focuses on the InstanceNormalization permutation tie-break described in the issue rather than claiming full model conversion success.

Checks

• python3 -m py_compile onnx2tf/ops/InstanceNormalization.py
• git diff --check
• lightweight tie-break assertion script for near-tie, clear-win, and non-NHWC cases

Local smoke context

A synthetic InstanceNormalization conversion completed in the side-run scratch environment before review. A follow-up tf_converter smoke after review reached TensorFlow/TFLite converter logging but hung in this local environment, so it was terminated without changing the repo.

What's Changed

• Prefer layout-preserving tie-break for InstanceNormalization by @aaronday-dev in https://github.com/PINTO0309/onnx2tf/pull/931

New Contributors

• @aaronday-dev made their first contribution in https://github.com/PINTO0309/onnx2tf/pull/931

Full Changelog: https://github.com/PINTO0309/onnx2tf/compare/2.4.0...2.4.1