code: def test_conversion_additional_tensor(self):
      speech_generator = pipeline(
          task="text-to-audio",
          model="suno/bark-small",
          framework="pt",
          device=torch_device,
      )
      processor = AutoProcessor.from_pretrained("suno/bark-small")
  
      forward_params = {
          "do_sample": True,
          "semantic_max_new_tokens": 100,
      }
  
      # atm, must do to stay coherent with BarkProcessor
      preprocess_params = {
          "max_length": 256,
          "add_special_tokens": False,
          "return_attention_mask": True,
          "return_token_type_ids": False,
          "padding": "max_length",
      }
      outputs = speech_generator(
          "This is a test",
          forward_params=forward_params,
          preprocess_params=preprocess_params,
      )
  
      temp_inp = processor(
          "hey, how are you?", voice_preset="v2/en_speaker_5"
      )
      history_prompt = temp_inp["history_prompt"]
      forward_params["history_prompt"] = history_prompt
  
      # history_prompt is a torch.Tensor passed as a forward_param
      # if generation is successful, it means that it was passed to the right device
      outputs = speech_generator(
          "This is a test",
          forward_params=forward_params,
          preprocess_params=preprocess_params,
      )
      self.assertEqual(
          {"audio": ANY(np.ndarray), "sampling_rate": 24000},
          outputs,
      )
  
file_path: transformers/tests/pipelines/test_pipelines_text_to_audio.py
imports: import unittest
  import numpy
  from transformers import MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING, AutoProcessor, TextToAudioPipeline, pipeline
  from transformers.testing_utils import is_pipeline_test, require_torch, require_torch_accelerator, require_torch_or_tf, slow, torch_device
  from transformers.trainer_utils import set_seed
  from .test_pipelines_common import ANY
label_desc: Use image node_id 3 for calling the TextToAudioPipelineTests obj's underlying member method code with example usage: obj.test_conversion_additional_tensor() without return types