docstr: Synthesize speech with the given input text.
  
  Args:
      text (str): Input text.
      config (BarkConfig): Config with inference parameters.
      speaker_id (str): One of the available speaker names. If `random`, it generates a random speaker.
      speaker_wav (str): Path to the speaker audio file for cloning a new voice. It is cloned and saved in
          `voice_dirs` with the name `speaker_id`. Defaults to None.
      voice_dirs (List[str]): List of paths that host reference audio files for speakers. Defaults to None.
      **kwargs: Model specific inference settings used by `generate_audio()` and `TTS.tts.layers.bark.inference_funcs.generate_text_semantic().
  
  Returns:
      A dictionary of the output values with `wav` as output waveform, `deterministic_seed` as seed used at inference,
      `text_input` as text token IDs after tokenizer, `voice_samples` as samples used for cloning, `conditioning_latents`
      as latents used at inference.
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code: def synthesize(
      self, text, config, speaker_id="random", voice_dirs=None, **kwargs
  ):  # pylint: disable=unused-argument
      """Synthesize speech with the given input text.
  
      Args:
          text (str): Input text.
          config (BarkConfig): Config with inference parameters.
          speaker_id (str): One of the available speaker names. If `random`, it generates a random speaker.
          speaker_wav (str): Path to the speaker audio file for cloning a new voice. It is cloned and saved in
              `voice_dirs` with the name `speaker_id`. Defaults to None.
          voice_dirs (List[str]): List of paths that host reference audio files for speakers. Defaults to None.
          **kwargs: Model specific inference settings used by `generate_audio()` and `TTS.tts.layers.bark.inference_funcs.generate_text_semantic().
  
      Returns:
          A dictionary of the output values with `wav` as output waveform, `deterministic_seed` as seed used at inference,
          `text_input` as text token IDs after tokenizer, `voice_samples` as samples used for cloning, `conditioning_latents`
          as latents used at inference.
  
      """
      speaker_id = "random" if speaker_id is None else speaker_id
      voice_dirs = self._set_voice_dirs(voice_dirs)
      history_prompt = load_voice(self, speaker_id, voice_dirs)
      outputs = self.generate_audio(
          text, history_prompt=history_prompt, **kwargs
      )
      return_dict = {
          "wav": outputs[0],
          "text_inputs": text,
      }
  
      return return_dict
  
imports: import os
  from dataclasses import dataclass
  from typing import Optional
  import numpy
  from coqpit import Coqpit
  from encodec import EncodecModel
  from transformers import BertTokenizer
  from TTS.tts.layers.bark.inference_funcs import codec_decode, generate_coarse, generate_fine, generate_text_semantic, generate_voice, load_voice
  from TTS.tts.layers.bark.load_model import load_model
  from TTS.tts.layers.bark.model import GPT
  from TTS.tts.layers.bark.model_fine import FineGPT
  from TTS.tts.models.base_tts import BaseTTS
label_desc: Use image node_id 10 for calling the Bark obj's underlying member method code with example usage: obj.synthesize(text, config, speaker_id, voice_dirs) and returns: return_dict