class_docstr: This is the configuration class to store the configuration of a [`SiglipVisionModel`]. It is used to instantiate a
  Siglip vision encoder according to the specified arguments, defining the model architecture. Instantiating a
  configuration with the defaults will yield a similar configuration to that of the vision encoder of the Siglip
  [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      hidden_size (`int`, *optional*, defaults to 768):
          Dimensionality of the encoder layers and the pooler layer.
      intermediate_size (`int`, *optional*, defaults to 3072):
          Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
      num_hidden_layers (`int`, *optional*, defaults to 12):
          Number of hidden layers in the Transformer encoder.
      num_attention_heads (`int`, *optional*, defaults to 12):
          Number of attention heads for each attention layer in the Transformer encoder.
      num_channels (`int`, *optional*, defaults to 3):
          Number of channels in the input images.
      image_size (`int`, *optional*, defaults to 224):
          The size (resolution) of each image.
      patch_size (`int`, *optional*, defaults to 16):
          The size (resolution) of each patch.
      hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
          The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
          `"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
      layer_norm_eps (`float`, *optional*, defaults to 1e-06):
          The epsilon used by the layer normalization layers.
      attention_dropout (`float`, *optional*, defaults to 0.0):
          The dropout ratio for the attention probabilities.
  
  Example:
  
  ```python
  >>> from transformers import SiglipVisionConfig, SiglipVisionModel
  
  >>> # Initializing a SiglipVisionConfig with google/siglip-base-patch16-224 style configuration
  >>> configuration = SiglipVisionConfig()
  
  >>> # Initializing a SiglipVisionModel (with random weights) from the google/siglip-base-patch16-224 style configuration
  >>> model = SiglipVisionModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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
      )
  
      # get the vision config dict if we are loading from SiglipConfig
      if config_dict.get("model_type") == "siglip":
          config_dict = config_dict["vision_config"]
  
      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)
  
file_path: transformers/src/transformers/models/siglip/configuration_siglip.py
imports: import os
  from typing import Union
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 2 for calling the SiglipVisionConfig obj's underlying member method code with example usage: obj.from_pretrained(cls, pretrained_model_name_or_path) and returns: cls