code: def get_model_paths(pretrained_model, model_path, quantized):
      model_path.mkdir(parents=True, exist_ok=True)
  
      # gets only the filename
      pretrained_model_name = Path(pretrained_model).stem
  
      if not quantized:
          encoder_path = model_path.joinpath(
              f"{pretrained_model_name}-encoder.onnx"
          )
          decoder_path = model_path.joinpath(
              f"{pretrained_model_name}-decoder.onnx"
          )
          init_decoder_path = model_path.joinpath(
              f"{pretrained_model_name}-init-decoder.onnx"
          )
      else:
          encoder_path = model_path.joinpath(
              f"{pretrained_model_name}-encoder-quantized.onnx"
          )
          decoder_path = model_path.joinpath(
              f"{pretrained_model_name}-decoder-quantized.onnx"
          )
          init_decoder_path = model_path.joinpath(
              f"{pretrained_model_name}-init-decoder-quantized.onnx"
          )
  
      return encoder_path, decoder_path, init_decoder_path
  
file_path: fastT5/fastT5/onnx_exporter.py
imports: from .huggingface_utils import get_auth_token
  from .onnx_models_structure import T5Encoder, DecoderWithLMhead, DecoderWithLMheadInitial
  from transformers import AutoConfig, T5ForConditionalGeneration, MT5ForConditionalGeneration
  import torch
  import functools
  import operator
  from progress.bar import Bar
  from pathlib import Path
  import os
label_desc: Use image node_id 4 for calling a global function with example usage: get_model_paths(pretrained_model, model_path, quantized) and returns: encoder_path,  decoder_path,  init_decoder_path