code: def load_model(
      model_name_or_path: Optional[Union[str, os.PathLike]],
      model_class: Type[PreTrainedModel],
      *model_args: Any,
      **kwargs: Any,
  ) -> PreTrainedModel:
      """
      Loads a pretrained model with adapters from the given path or url.
  
      Parameters:
          model_name_or_path (`str` or `os.PathLike`, *optional*):
              Parameter identical to PreTrainedModel.from_pretrained
          model_class (`PreTrainedModel` or `AutoModel`):
              The model class to load (e.g. EncoderDecoderModel and EncoderDecoderAdapterModel both work)
          model_args (sequence of positional arguments, *optional*):
              All remaining positional arguments will be passed to the underlying model's `__init__` method.
          kwargs (remaining dictionary of keyword arguments, *optional*):
              Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
              `output_attentions=True`).
      Returns:
          `PreTrainedModel`: The model with adapters loaded from the given path or url.
      """
  
      old_init = model_class.__init__
  
      def new_init(self, config, *args, **kwargs):
          old_init(self, config, *args, **kwargs)
          init(self)
  
      # wrap model after it is initialized but before the weights are loaded
      model_class.__init__ = new_init
      model = model_class.from_pretrained(
          model_name_or_path, *model_args, **kwargs
      )
  
      # restore original __init__ function for when other models of the same type are created
      model_class.__init__ = old_init
  
      return model
  
file_path: adapters/src/adapters/wrappers/model.py
imports: import importlib
  import os
  from typing import Any, Optional, Type, Union
  from torch import nn
  from transformers import PreTrainedModel
  from transformers.models.auto.auto_factory import getattribute_from_module
  from transformers.models.auto.configuration_auto import model_type_to_module_name
  from ..configuration import ModelAdaptersConfig
  from ..model_mixin import EmbeddingAdaptersWrapperMixin, ModelAdaptersMixin, ModelUsingSubmodelsAdaptersMixin, ModelWithHeadsAdaptersMixin
  from ..models import MODEL_MIXIN_MAPPING
  from .configuration import init_adapters_config
label_desc: Use image node_id 4 for calling a global function with example usage: load_model(model_name_or_path, model_class) and returns: model