code: def _get_joiner_input(self):
      input_config = self._get_input_config()
      batch_size = input_config["batch_size"]
      joiner_max_input_length = input_config["joiner_max_input_length"]
      max_target_length = input_config["max_target_length"]
      input_dim = input_config["encoding_dim"]
  
      utterance_encodings = torch.rand(
          batch_size, joiner_max_input_length, input_dim
      ).to(device=self.device, dtype=self.dtype)
      utterance_lengths = torch.randint(
          0, joiner_max_input_length + 1, (batch_size,)
      ).to(device=self.device, dtype=torch.int32)
      target_encodings = torch.rand(
          batch_size, max_target_length, input_dim
      ).to(device=self.device, dtype=self.dtype)
      target_lengths = torch.randint(
          0, max_target_length + 1, (batch_size,)
      ).to(device=self.device, dtype=torch.int32)
  
      return (
          utterance_encodings,
          utterance_lengths,
          target_encodings,
          target_lengths,
      )
  
file_path: audio/test/torchaudio_unittest/prototype/rnnt_test_impl.py
imports: import torch
  from torchaudio.prototype.models import conformer_rnnt_model
  from torchaudio_unittest.common_utils import TestBaseMixin, torch_script
label_desc: Use image node_id 6 for calling the ConformerRNNTTestImpl obj's underlying member method code with example usage: obj._get_joiner_input() and returns: utterance_encodings,  utterance_lengths,  target_encodings,  target_lengths