code: def get_model_encoder_output(data_dir, model_name):
      # Import inside function to avoid issues with dependencies
      import librosa
  
      audio_filepath = os.path.join(
          data_dir, "asr", "test", "an4", "wav", "cen3-fjlp-b.wav"
      )
  
      with torch.no_grad():
          model = ASRModel.from_pretrained(
              model_name, map_location="cpu"
          )  # type: ASRModel
          model.preprocessor.featurizer.dither = 0.0
          model.preprocessor.featurizer.pad_to = 0
  
          audio, sr = librosa.load(
              path=audio_filepath, sr=16000, mono=True
          )
  
          input_signal = torch.tensor(
              audio, dtype=torch.float32
          ).unsqueeze(0)
          input_signal_length = torch.tensor(
              [len(audio)], dtype=torch.int32
          )
  
          encoded, encoded_len = model(
              input_signal=input_signal,
              input_signal_length=input_signal_length,
          )
  
      return model, encoded, encoded_len
  
imports: import os
  from functools import lru_cache
  import pytest
  import torch
  from omegaconf import DictConfig
  from nemo.collections.asr.models import ASRModel
  from nemo.collections.asr.modules import RNNTDecoder, RNNTJoint
  from nemo.collections.asr.parts.mixins import mixins
  from nemo.collections.asr.parts.submodules import rnnt_beam_decoding
  from nemo.collections.asr.parts.submodules import rnnt_greedy_decoding
  from nemo.collections.asr.parts.submodules.rnnt_decoding import RNNTBPEDecoding, RNNTDecoding, RNNTDecodingConfig
  from nemo.collections.asr.parts.utils import rnnt_utils
  from nemo.core.utils import numba_utils
  from nemo.core.utils.numba_utils import __NUMBA_MINIMUM_VERSION__
label_desc: Use image node_id 5 for calling a global function with example usage: get_model_encoder_output(data_dir, model_name) and returns: model,  encoded,  encoded_len