class_docstr: [`AlignConfig`] is the configuration class to store the configuration of a [`AlignModel`]. It is used to
  instantiate a ALIGN 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 ALIGN
  [kakaobrain/align-base](https://huggingface.co/kakaobrain/align-base) 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 [`AlignTextConfig`].
      vision_config (`dict`, *optional*):
          Dictionary of configuration options used to initialize [`AlignVisionConfig`].
      projection_dim (`int`, *optional*, defaults to 640):
          Dimentionality of text and vision projection layers.
      temperature_init_value (`float`, *optional*, defaults to 1.0):
          The inital value of the *temperature* paramter. Default is used as per the original ALIGN implementation.
      initializer_range (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      kwargs (*optional*):
          Dictionary of keyword arguments.
  
  Example:
  
  ```python
  >>> from transformers import AlignConfig, AlignModel
  
  >>> # Initializing a AlignConfig with kakaobrain/align-base style configuration
  >>> configuration = AlignConfig()
  
  >>> # Initializing a AlignModel (with random weights) from the kakaobrain/align-base style configuration
  >>> model = AlignModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  
  >>> # We can also initialize a AlignConfig from a AlignTextConfig and a AlignVisionConfig
  >>> from transformers import AlignTextConfig, AlignVisionConfig
  
  >>> # Initializing ALIGN Text and Vision configurations
  >>> config_text = AlignTextConfig()
  >>> config_vision = AlignVisionConfig()
  
  >>> config = AlignConfig.from_text_vision_configs(config_text, config_vision)
  ```
docstr: Instantiate a [`AlignConfig`] (or a derived class) from align text model configuration and align vision model
  configuration.
  
  Returns:
      [`AlignConfig`]: An instance of a configuration object
docstr_tok: ["Instantiate","a","[","`","AlignConfig","`","]","(","or","a","derived","class",")","from","align","text","model","configuration","and","align","vision","model","configuration",".","Returns",":","[","`","AlignConfig","`","]",":","An","instance","of","a","configuration","object"]
code: def from_text_vision_configs(
      cls,
      text_config: AlignTextConfig,
      vision_config: AlignVisionConfig,
      **kwargs,
  ):
      r"""
      Instantiate a [`AlignConfig`] (or a derived class) from align text model configuration and align vision model
      configuration.
  
      Returns:
          [`AlignConfig`]: An instance of a configuration object
      """
  
      return cls(
          text_config=text_config.to_dict(),
          vision_config=vision_config.to_dict(),
          **kwargs,
      )
  
file_path: transformers/src/transformers/models/align/configuration_align.py
imports: import os
  from typing import TYPE_CHECKING, List, Union
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 2 for calling the AlignConfig obj's underlying member method code with example usage: obj.from_text_vision_configs(cls, text_config, vision_config) and returns: cls