code: def load_from_checkpoint(path, map_location=None):
      old = True
      with ZipFile(path) as model_zip:
          filesMatch = [
              file
              for file in model_zip.namelist()
              if file.endswith("/.info")
          ]
          file = filesMatch[0] if filesMatch else None
          if file:
              old = False
              data_from_model = Data.load(
                  model_zip.read(file).decode("utf-8")
              )
          model_zip.close()
      if old:
          model = HubertTokenizer()
      else:
          model = HubertTokenizer(
              data_from_model.hidden_size,
              data_from_model.input_size,
              data_from_model.output_size,
              data_from_model.version,
          )
      model.load_state_dict(torch.load(path, map_location=map_location))
      if map_location:
          model = model.to(map_location)
      return model
  
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 7 for calling the HubertTokenizer obj's underlying member method code with example usage: obj.load_from_checkpoint(path, map_location) and returns: model