code: def _get_transcriber_streaming_input(self):
      input_config = self._get_input_config()
      batch_size = input_config["batch_size"]
      segment_length = input_config["segment_length"]
      input_dim = input_config["input_dim"]
      right_context_length = input_config["right_context_length"]
  
      input = torch.rand(
          batch_size, segment_length + right_context_length, input_dim
      ).to(device=self.device, dtype=self.dtype)
      lengths = torch.randint(
          1, segment_length + right_context_length + 1, (batch_size,)
      ).to(device=self.device, dtype=torch.int32)
      return input, lengths
  
imports: import torch
  from torchaudio.models import emformer_rnnt_model
  from torchaudio_unittest.common_utils import TestBaseMixin, torch_script
label_desc: Use image node_id 5 for calling the RNNTTestImpl obj's underlying member method code with example usage: obj._get_transcriber_streaming_input() and returns: input,  lengths