code: def _get_transcriber_input(self):
      input_config = self._get_input_config()
      batch_size = input_config["batch_size"]
      max_input_length = input_config["max_input_length"]
      input_dim = input_config["input_dim"]
  
      input = torch.rand(batch_size, max_input_length, input_dim).to(
          device=self.device, dtype=self.dtype
      )
      lengths = torch.full((batch_size,), max_input_length).to(
          device=self.device, dtype=torch.int32
      )
      return input, 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 4 for calling the ConformerRNNTTestImpl obj's underlying member method code with example usage: obj._get_transcriber_input() and returns: input,  lengths