class_docstr: This is the configuration class to store the configuration of a [`VitDetModel`]. It is used to instantiate an
  VitDet 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 VitDet
  [google/vitdet-base-patch16-224](https://huggingface.co/google/vitdet-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.
      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.
      mlp_ratio (`int`, *optional*, defaults to 4):
          Ratio of mlp hidden dim to embedding dim.
      hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
          The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
          `"relu"`, `"selu"` and `"gelu_new"` are supported.
      dropout_prob (`float`, *optional*, defaults to 0.0):
          The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
      initializer_range (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      layer_norm_eps (`float`, *optional*, defaults to 1e-06):
          The epsilon used by the layer normalization layers.
      image_size (`int`, *optional*, defaults to 224):
          The size (resolution) of each image.
      pretrain_image_size (`int`, *optional*, defaults to 224):
          The size (resolution) of each image during pretraining.
      patch_size (`int`, *optional*, defaults to 16):
          The size (resolution) of each patch.
      num_channels (`int`, *optional*, defaults to 3):
          The number of input channels.
      qkv_bias (`bool`, *optional*, defaults to `True`):
          Whether to add a bias to the queries, keys and values.
      drop_path_rate (`float`, *optional*, defaults to 0.0):
          Stochastic depth rate.
      window_block_indices (`List[int]`, *optional*, defaults to `[]`):
          List of indices of blocks that should have window attention instead of regular global self-attention.
      residual_block_indices (`List[int]`, *optional*, defaults to `[]`):
          List of indices of blocks that should have an extra residual block after the MLP.
      use_absolute_position_embeddings (`bool`, *optional*, defaults to `True`):
          Whether to add absolute position embeddings to the patch embeddings.
      use_relative_position_embeddings (`bool`, *optional*, defaults to `False`):
          Whether to add relative position embeddings to the attention maps.
      window_size (`int`, *optional*, defaults to 0):
          The size of the attention window.
      out_features (`List[str]`, *optional*):
          If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
          (depending on how many stages the model has). If unset and `out_indices` is set, will default to the
          corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the
          same order as defined in the `stage_names` attribute.
      out_indices (`List[int]`, *optional*):
          If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
          many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
          If unset and `out_features` is unset, will default to the last stage. Must be in the
          same order as defined in the `stage_names` attribute.
  
  Example:
  
  ```python
  >>> from transformers import VitDetConfig, VitDetModel
  
  >>> # Initializing a VitDet configuration
  >>> configuration = VitDetConfig()
  
  >>> # Initializing a model (with random weights) from the configuration
  >>> model = VitDetModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
code: def __init__(
      self,
      hidden_size=768,
      num_hidden_layers=12,
      num_attention_heads=12,
      mlp_ratio=4,
      hidden_act="gelu",
      dropout_prob=0.0,
      initializer_range=0.02,
      layer_norm_eps=1e-6,
      image_size=224,
      pretrain_image_size=224,
      patch_size=16,
      num_channels=3,
      qkv_bias=True,
      drop_path_rate=0.0,
      window_block_indices=[],
      residual_block_indices=[],
      use_absolute_position_embeddings=True,
      use_relative_position_embeddings=False,
      window_size=0,
      out_features=None,
      out_indices=None,
      **kwargs,
  ):
      super().__init__(**kwargs)
  
      self.hidden_size = hidden_size
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
      self.mlp_ratio = mlp_ratio
      self.hidden_act = hidden_act
      self.dropout_prob = dropout_prob
      self.initializer_range = initializer_range
      self.layer_norm_eps = layer_norm_eps
      self.image_size = image_size
      self.pretrain_image_size = pretrain_image_size
      self.patch_size = patch_size
      self.num_channels = num_channels
      self.qkv_bias = qkv_bias
      self.drop_path_rate = drop_path_rate
      self.window_block_indices = window_block_indices
      self.residual_block_indices = residual_block_indices
      self.use_absolute_position_embeddings = (
          use_absolute_position_embeddings
      )
      self.use_relative_position_embeddings = (
          use_relative_position_embeddings
      )
      self.window_size = window_size
  
      self.stage_names = ["stem"] + [
          f"stage{idx}" for idx in range(1, self.num_hidden_layers + 1)
      ]
      (
          self._out_features,
          self._out_indices,
      ) = get_aligned_output_features_output_indices(
          out_features=out_features,
          out_indices=out_indices,
          stage_names=self.stage_names,
      )
  
imports: from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
label_desc: Use image node_id 1 to create a new VitDetConfig object from inherited base classes: BackboneConfigMixin, PretrainedConfig with example: obj = VitDetConfig(hidden_size, num_hidden_layers, num_attention_heads, mlp_ratio, hidden_act, dropout_prob, initializer_range, layer_norm_eps, image_size, pretrain_image_size, patch_size, num_channels, qkv_bias, drop_path_rate, window_block_indices, residual_block_indices, use_absolute_position_embeddings, use_relative_position_embeddings, window_size, out_features, out_indices)