docstr: Loads the model from the given file.
  :param model: the model file
  :return: the loaded text classifier model
docstr_tok: ["Loads","the","model","from","the","given","file",".",":","param","model",":","the","model","file",":","return",":","the","loaded","text","classifier","model"]
code: def load(cls, model):
      """
      Loads the model from the given file.
      :param model: the model file
      :return: the loaded text classifier model
      """
      model_file = cls._fetch_model(str(model))
  
      with warnings.catch_warnings():
          warnings.filterwarnings("ignore")
          # load_big_file is a workaround by https://github.com/highway11git to load models on some Mac/Windows setups
          # see https://github.com/zalandoresearch/flair/issues/351
          f = file_utils.load_big_file(str(model_file))
          state = torch.load(f, map_location="cpu")
  
      model = cls._init_model_with_state_dict(state)
  
      model.eval()
      model.to(device)
  
      return model
  
file_path: underthesea/underthesea/modules/nn.py
imports: import warnings
  from abc import abstractmethod
  import torch.nn
  from underthesea import file_utils
  from underthesea.utils.util_deep_learning import device
label_desc: Use image node_id 2 for calling the Model obj's underlying member method code with example usage: obj.load(cls, model) and returns: model