class_docstr: Abstract base class for all downstream task models
  Every new type of model must implement these methods.
  
  Source: FlairNLP
class_docstr_tok: ["Abstract","base","class","for","all","downstream","task","models","Every","new","type","of","model","must","implement","these","methods",".","Source",":","FlairNLP"]
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: Union[str, Path]):
      """
      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/models/model.py
imports: import warnings
  from abc import abstractmethod
  from pathlib import Path
  from typing import Union
  import torch.nn
  from underthesea import file_utils
  from underthesea.utils import device
label_desc: Use image node_id 5 for calling the Model obj's underlying member method code with example usage: obj.load(cls, model) and returns: model