class_docstr: This is the configuration class to store the configuration of [`VitMatteForImageMatting`]. It is used to
  instantiate a ViTMatte model according to the specified arguments, defining the model architecture. Instantiating a
  configuration with the defaults will yield a similar configuration to that of the ViTMatte
  [hustvl/vitmatte-small-composition-1k](https://huggingface.co/hustvl/vitmatte-small-composition-1k) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      backbone_config (`PretrainedConfig` or `dict`, *optional*, defaults to `VitDetConfig()`):
          The configuration of the backbone model.
      hidden_size (`int`, *optional*, defaults to 384):
          The number of input channels of the decoder.
      batch_norm_eps (`float`, *optional*, defaults to 1e-05):
          The epsilon used by the batch norm layers.
      initializer_range (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      convstream_hidden_sizes (`List[int]`, *optional*, defaults to `[48, 96, 192]`):
          The output channels of the ConvStream module.
      fusion_hidden_sizes (`List[int]`, *optional*, defaults to `[256, 128, 64, 32]`):
          The output channels of the Fusion blocks.
  
  Example:
  
  ```python
  >>> from transformers import VitMatteConfig, VitMatteForImageMatting
  
  >>> # Initializing a ViTMatte hustvl/vitmatte-small-composition-1k style configuration
  >>> configuration = VitMatteConfig()
  
  >>> # Initializing a model (with random weights) from the hustvl/vitmatte-small-composition-1k style configuration
  >>> model = VitMatteForImageMatting(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
docstr: Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns:
      `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
code: def to_dict(self):
      """
      Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns:
          `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
      """
      output = copy.deepcopy(self.__dict__)
      output["backbone_config"] = self.backbone_config.to_dict()
      output["model_type"] = self.__class__.model_type
      return output
  
file_path: transformers/src/transformers/models/vitmatte/configuration_vitmatte.py
imports: import copy
  from typing import List
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto.configuration_auto import CONFIG_MAPPING
label_desc: Use image node_id 2 for calling the VitMatteConfig obj's underlying member method code with example usage: obj.to_dict() and returns: output