"//tensorflow/compiler/mlir/lite/quantization/lite:quantize_model",
        "//tensorflow/compiler/mlir/quantization/common/quantization_lib:quantization_config",
        "//tensorflow/compiler/mlir/lite/sparsity:sparsify_model",
        "//tensorflow/compiler/mlir/quantization/stablehlo/python:pywrap_quantization_lib_impl",

2)  Reference [TensorFlow Lite Model Colab](https://colab.research.google.com/gist/ymodak/0dfeb28255e189c5c48d9093f296e9a8/tensorflow-lite-debugger-colab.ipynb): Demonstrate how to convert your TF model to a TF Lite model (with quantization, if used) and run TFLite Inference (if possible).

```
(You can paste links or attach files by dragging & dropping them below)
- Provide links to your updated versions of the above two colab notebooks.

        `tf.math.zeta`.
    *   New endpoints in `tf.quantization` namespace:
        `tf.quantization.dequantize`,
        `tf.quantization.fake_quant_with_min_max_args`,
        `tf.quantization.fake_quant_with_min_max_args_gradient`,
        `tf.quantization.fake_quant_with_min_max_vars`,
        `tf.quantization.fake_quant_with_min_max_vars_gradient`,
        `tf.quantization.fake_quant_with_min_max_vars_per_channel`,

# CROSS-COMPILE ARM64 PYCPP
build:cross_compile_linux_arm64_pycpp_test --config=linux_arm64_pycpp_test
# Tests that fail only when cross-compiled
build:cross_compile_linux_arm64_pycpp_test -//tensorflow/compiler/mlir/quantization/stablehlo:convert_tf_quant_to_mhlo_int_test
# MACOS ARM64 PYCPP