class_docstr: [`OwlViTConfig`] is the configuration class to store the configuration of an [`OwlViTModel`]. It is used to
  instantiate an OWL-ViT model according to the specified arguments, defining the text model and vision model
  configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the OWL-ViT
  [google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      text_config (`dict`, *optional*):
          Dictionary of configuration options used to initialize [`OwlViTTextConfig`].
      vision_config (`dict`, *optional*):
          Dictionary of configuration options used to initialize [`OwlViTVisionConfig`].
      projection_dim (`int`, *optional*, defaults to 512):
          Dimensionality of text and vision projection layers.
      logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
          The inital value of the *logit_scale* parameter. Default is used as per the original OWL-ViT
          implementation.
      return_dict (`bool`, *optional*, defaults to `True`):
          Whether or not the model should return a dictionary. If `False`, returns a tuple.
      kwargs (*optional*):
          Dictionary of keyword arguments.
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code: def from_pretrained(
      cls,
      pretrained_model_name_or_path: Union[str, os.PathLike],
      **kwargs,
  ) -> "PretrainedConfig":
      cls._set_token_in_kwargs(kwargs)
  
      config_dict, kwargs = cls.get_config_dict(
          pretrained_model_name_or_path, **kwargs
      )
  
      if (
          "model_type" in config_dict
          and hasattr(cls, "model_type")
          and config_dict["model_type"] != cls.model_type
      ):
          logger.warning(
              f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
              f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
          )
  
      return cls.from_dict(config_dict, **kwargs)
  
imports: import os
  from collections import OrderedDict
  from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
label_desc: Use image node_id 2 for calling the OwlViTConfig obj's underlying member method code with example usage: obj.from_pretrained(cls, pretrained_model_name_or_path) and returns: cls