code: def test_inference(pretrained_model, audio_text_pair_example_english):
      model, _ = pretrained_model
      audio, audio_len, text_raw = audio_text_pair_example_english
  
      # Generate mel-spectrogram
      spec, spec_len = model.preprocessor(
          input_signal=audio, length=audio_len
      )
  
      # Process text
      text_normalized = model.normalizer.normalize(
          text_raw, punct_post_process=True
      )
      text_tokens = model.tokenizer(text_normalized)
      text = (
          torch.tensor(text_tokens, device=spec.device)
          .unsqueeze(0)
          .long()
      )
      text_len = (
          torch.tensor(len(text_tokens), device=spec.device)
          .unsqueeze(0)
          .long()
      )
  
      # Run the Aligner
      _, _ = model(
          spec=spec, spec_len=spec_len, text=text, text_len=text_len
      )
  
imports: import pytest
  import torch
  from nemo.collections.tts.models import AlignerModel
label_desc: Use image node_id 2 for calling a global function with example usage: test_inference(pretrained_model, audio_text_pair_example_english) without return types