code: def conformer_rnnt_bpe_model():
      model = EncDecRNNTBPEModel.from_pretrained(
          model_name="stt_en_conformer_transducer_small"
      )
      model.set_trainer(Trainer(devices=1, accelerator="cpu"))
      model = model.eval()
      return model
  
imports: import json
  import math
  import tempfile
  from pathlib import Path
  import numpy
  import pytest
  from omegaconf import OmegaConf
  from pytorch_lightning import Trainer
  from nemo.collections.asr.models import ASRModel, EncDecCTCModelBPE, EncDecRNNTBPEModel
  from nemo.collections.asr.parts.submodules.ctc_decoding import CTCDecodingConfig
  from nemo.collections.asr.parts.submodules.ctc_greedy_decoding import GreedyCTCInferConfig
  from nemo.collections.asr.parts.submodules.rnnt_decoding import RNNTDecodingConfig
  from nemo.collections.asr.parts.submodules.rnnt_greedy_decoding import GreedyBatchedRNNTInferConfig
  from nemo.collections.asr.parts.utils.asr_confidence_benchmarking_utils import run_confidence_benchmark
  from nemo.collections.asr.parts.utils.asr_confidence_utils import ConfidenceConfig
label_desc: Use image node_id 2 for calling a global function with example usage: conformer_rnnt_bpe_model() and returns: model