code: def test_emformer_join(self):
      class EmformerRnntJoinerExample(torch.nn.Module):
          """
          This is a wrapper for validating joiner 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,
              source_encodings,
              source_lengths,
              target_encodings,
              target_lengths,
          ):
              return self.rnnt.join(
                  source_encodings,
                  source_lengths,
                  target_encodings,
                  target_lengths,
              )
  
      model = EmformerRnntJoinerExample()
      model.eval()
  
      self.run_compare_torch(
          [(1, 128, 1024), (128,), (1, 128, 1024), (128,)],
          model,
          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 10 for calling the TestExecutorch obj's underlying member method code with example usage: obj.test_emformer_join() and returns: self