class_docstr: A wrapper class around a (`transformers.PreTrainedModel`) to be compatible with the
  (`~transformers.PreTrained`) class in order to keep some attributes and methods of the
  (`~transformers.PreTrainedModel`) class.
  
  Attributes:
      pretrained_model: (`transformers.PreTrainedModel`)
          The model to be wrapped.
      parent_class: (`transformers.PreTrainedModel`)
          The parent class of the model to be wrapped.
      supported_args: (`list`)
          The list of arguments that are supported by the wrapper class.
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docstr: Post initialization method. This method is called after the model is
  instantiated and loaded from a checkpoint. It can be used to perform
  additional operations such as loading the state_dict.
docstr_tok: ["Post","initialization","method",".","This","method","is","called","after","the","model","is","instantiated","and","loaded","from","a","checkpoint",".","It","can","be","used","to","perform","additional","operations","such","as","loading","the","state_dict","."]
code: def post_init(self, *args, **kwargs):
      r"""
      Post initialization method. This method is called after the model is
      instantiated and loaded from a checkpoint. It can be used to perform
      additional operations such as loading the state_dict.
      """
      raise NotImplementedError
  
imports: import json
  import logging
  import os
  from copy import deepcopy
  import torch
  import torch.nn
  from accelerate import PartialState
  from huggingface_hub import hf_hub_download
  from huggingface_hub.utils import EntryNotFoundError, HFValidationError, LocalEntryNotFoundError, RepositoryNotFoundError
  from safetensors.torch import load_file
  from transformers import PreTrainedModel
  from ..import_utils import is_npu_available, is_peft_available, is_transformers_greater_than, is_xpu_available
label_desc: Use image node_id 10 for calling the PreTrainedModelWrapper obj's underlying member method code with example usage: obj.post_init() without return types