code: def generate(
      self, data: List[str], batch_size: int = 1, use_gpu: bool = False
  ):
      assert (
          self._speaker_embedding is not None
      ), f"Set speaker embedding before voice cloning."
  
      paddle.set_device("gpu") if use_gpu else paddle.set_device("cpu")
      batches = self._batchify(data, batch_size)
  
      results = []
      for batch in batches:
          phones, tones, speaker_embeddings = map(
              paddle.to_tensor, batch
          )
          waveforms = self(phones, tones, speaker_embeddings).numpy()
          results.extend(list(waveforms))
  
      files = []
      for idx, waveform in enumerate(results):
          output_wav = os.path.join(self.output_dir, f"{idx+1}.wav")
          sf.write(output_wav, waveform, samplerate=self.sample_rate)
          files.append(output_wav)
  
      return files
  
imports: import importlib
  import os
  from typing import List
  import numpy
  import paddle
  import paddle.nn
  from paddlehub.env import MODULE_HOME
  from paddlehub.module.module import moduleinfo
  from paddlehub.utils.log import logger
  from paddlenlp.data import Pad
  from parakeet.models import ConditionalWaveFlow, Tacotron2
  from parakeet.models.lstm_speaker_encoder import LSTMSpeakerEncoder
  import soundfile
  from .audio_processor import SpeakerVerificationPreprocessor
  from .chinese_g2p import convert_sentence
  from .preprocess_transcription import voc_phones, voc_tones, phone_pad_token, tone_pad_token
label_desc: Use image node_id 7 for calling the VoiceCloner obj's underlying member method code with example usage: obj.generate(data, batch_size, use_gpu) and returns: files