class_docstr: A mixin class for all pre-trained classes to inherit.
      
class_docstr_tok: ["A","mixin","class","for","all","pre-trained","classes","to","inherit","."]
docstr: Initialize model parameters from weights stored in the pre-trained
  checkpoint.
  
  Args:
      pretrained_model_name (str): Name of the pre-trained model.
      cache_dir (str): Path to the cache directory.
      scope_name: Variable scope.
      **kwargs: Additional arguments for specific models.
docstr_tok: ["Initialize","model","parameters","from","weights","stored","in","the","pre-trained","checkpoint",".","Args",":","pretrained_model_name","(","str",")",":","Name","of","the","pre-trained","model",".","cache_dir","(","str",")",":","Path","to","the","cache","directory",".","scope_name",":","Variable","scope",".","*","*","kwargs",":","Additional","arguments","for","specific","models","."]
code: def _init_from_checkpoint(
      self, pretrained_model_name, cache_dir, scope_name, **kwargs
  ):
      r"""Initialize model parameters from weights stored in the pre-trained
      checkpoint.
  
      Args:
          pretrained_model_name (str): Name of the pre-trained model.
          cache_dir (str): Path to the cache directory.
          scope_name: Variable scope.
          **kwargs: Additional arguments for specific models.
      """
      raise NotImplementedError
  
imports: import os
  import sys
  from abc import ABCMeta, abstractmethod
  from pathlib import Path
  from texar.tf.data.data_utils import maybe_download
  from texar.tf.hyperparams import HParams
  from texar.tf.module_base import ModuleBase
label_desc: Use image node_id 9 for calling the PretrainedMixin obj's underlying member method code with example usage: obj._init_from_checkpoint(pretrained_model_name, cache_dir, scope_name) without return types