code: def run_eval_pipeline(args):
      decoder = EMFORMER_RNNT_BASE_TEDLIUM3.get_decoder()
      token_processor = (
          EMFORMER_RNNT_BASE_TEDLIUM3.get_token_processor()
      )
      feature_extractor = (
          EMFORMER_RNNT_BASE_TEDLIUM3.get_feature_extractor()
      )
  
      if args.use_cuda:
          feature_extractor = feature_extractor.to(device="cuda").eval()
          decoder = decoder.to(device="cuda")
      _eval_subset(
          args.tedlium_path,
          "dev",
          feature_extractor,
          decoder,
          token_processor,
          args.use_cuda,
      )
      _eval_subset(
          args.tedlium_path,
          "test",
          feature_extractor,
          decoder,
          token_processor,
          args.use_cuda,
      )
  
file_path: audio/examples/asr/emformer_rnnt/tedlium3/eval_pipeline.py
imports: import logging
  import pathlib
  from argparse import ArgumentParser, RawTextHelpFormatter
  import torch
  import torchaudio
  from torchaudio.prototype.pipelines import EMFORMER_RNNT_BASE_TEDLIUM3
label_desc: Use image node_id 3 for calling a global function with example usage: run_eval_pipeline(args) without return types