code: def test_emformer_predict(self):
      class EmformerRnntPredictorExample(torch.nn.Module):
          """
          This is a wrapper for validating predictor for the Emformer RNN-T architecture.
          It does not reflect the actual usage such as beam search, but rather an example for the export workflow.
          """
  
          def __init__(self) -> None:
              super().__init__()
              bundle = (
                  torchaudio.pipelines.EMFORMER_RNNT_BASE_LIBRISPEECH
              )
              decoder = bundle.get_decoder()
              self.rnnt = decoder.model
  
          def forward(self, targets, target_lengths):
              return self.rnnt.predict(targets, target_lengths, None)
  
      model = EmformerRnntPredictorExample()
      model.eval()
  
      self.run_compare_torch(
          [
              torch.zeros([1, 128], dtype=int),
              torch.tensor([128], dtype=int),
          ],
          model,
          input_as_shape=False,
          frontend=TorchFrontend.EDGEIR,
          backend=("mlprogram", "fp16"),
      )
  
imports: import pytest
  from coremltools._deps import _HAS_EXECUTORCH, _HAS_TORCH_VISION
  import torch
  import torchvision
  import torchaudio
  import torchsr
  import timm
  import transformers
  from .testing_utils import TorchBaseTest, TorchFrontend
label_desc: Use image node_id 9 for calling the TestExecutorch obj's underlying member method code with example usage: obj.test_emformer_predict() and returns: self