code: def export_onnx(args, asr_model):
      if args.onnx:
          if args.asr_model.endswith("nemo"):
              onnx_name = args.asr_model.replace(".nemo", ".onnx")
          else:
              onnx_name = args.asr_model
          logging.info(f"Export to {onnx_name}")
          quant_nn.TensorQuantizer.use_fb_fake_quant = True
          asr_model.export(onnx_name, onnx_opset_version=13)
          quant_nn.TensorQuantizer.use_fb_fake_quant = False
  
file_path: NeMo/examples/asr/quantization/speech_to_text_quant_infer.py
imports: import collections
  from argparse import ArgumentParser
  from pprint import pprint
  import torch
  from omegaconf import open_dict
  from nemo.collections.asr.metrics.wer import WER, word_error_rate
  from nemo.collections.asr.models import EncDecCTCModel
  from nemo.collections.asr.parts.submodules.ctc_decoding import CTCDecoding, CTCDecodingConfig
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling a global function with example usage: export_onnx(args, asr_model) without return types