What is recommended way to convert into fp16 from fp32

Description

I have a modernbert model in fp32 and I want to convert it into fp16 while keeping embedding layer and classifier layer in fp32. What are the recommended way to do it?
It seems to be layerOutputTypes function but I could not find Python API and it is gone in 10.12?

tensorrt.BuilderFlag.FP16 is said to be Deprecated in TensorRT 10.12. https://docs.nvidia.com/deeplearning/tensorrt/latest/_static/python-api/infer/Core/BuilderConfig.html#tensorrt.BuilderFlag
What is the alternative? It said strong typing superseded. Do you have example?

• I am in 10.13, actually it still works.

Thanks so much!

Environment

TensorRT Version:

NVIDIA GPU: L40S

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CUDA Version:

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Python Version (if applicable):

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Relevant Files

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Steps To Reproduce

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Have you tried the latest release?:

Attach the captured .json and .bin files from TensorRT's API Capture tool if you're on an x86_64 Unix system

Can this model run on other frameworks? For example run ONNX model with ONNXRuntime (polygraphy run <model.onnx> --onnxrt):

Start by reviewing the TensorRT Python API around layerOutputTypes, BuilderFlag.FP16, and strong typing, using the linked BuilderConfig documentation as the entry point. Clarify how TensorRT 10.13 handles FP32-to-FP16 conversion while retaining FP32 embedding and classifier layers; done means a documented recommendation and runnable example.