code: def __init__(
      self,
      hidden_size=1024,
      input_size=768,
      output_size=10000,
      version=0,
  ):
      super().__init__()
      next_size = input_size
      if version == 0:
          self.lstm = nn.LSTM(
              input_size, hidden_size, 2, batch_first=True
          )
          next_size = hidden_size
      if version == 1:
          self.lstm = nn.LSTM(
              input_size, hidden_size, 2, batch_first=True
          )
          self.intermediate = nn.Linear(hidden_size, 4096)
          next_size = 4096
  
      self.fc = nn.Linear(next_size, output_size)
      self.softmax = nn.LogSoftmax(dim=1)
      self.optimizer: optim.Optimizer = None
      self.lossfunc = nn.CrossEntropyLoss()
      self.input_size = input_size
      self.hidden_size = hidden_size
      self.output_size = output_size
      self.version = version
  
file_path: TTS/TTS/tts/layers/bark/hubert/tokenizer.py
imports: import json
  import os.path
  from zipfile import ZipFile
  import numpy
  import torch
  from torch import nn, optim
label_desc: Use image node_id 1 to create a new HubertTokenizer object from inherited base classes: nn with example: obj = HubertTokenizer(hidden_size, input_size, output_size, version)