class_docstr: This is the configuration class to store the configuration of a [`FocalNetModel`]. It is used to instantiate a
  FocalNet 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 FocalNet
  [microsoft/focalnet-tiny](https://huggingface.co/microsoft/focalnet-tiny) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      image_size (`int`, *optional*, defaults to 224):
          The size (resolution) of each image.
      patch_size (`int`, *optional*, defaults to 4):
          The size (resolution) of each patch in the embeddings layer.
      num_channels (`int`, *optional*, defaults to 3):
          The number of input channels.
      embed_dim (`int`, *optional*, defaults to 96):
          Dimensionality of patch embedding.
      use_conv_embed (`bool`, *optional*, defaults to `False`):
          Whether to use convolutional embedding. The authors noted that using convolutional embedding usually
          improve the performance, but it's not used by default.
      hidden_sizes (`List[int]`, *optional*, defaults to `[192, 384, 768, 768]`):
          Dimensionality (hidden size) at each stage.
      depths (`list(int)`, *optional*, defaults to `[2, 2, 6, 2]`):
          Depth (number of layers) of each stage in the encoder.
      focal_levels (`list(int)`, *optional*, defaults to `[2, 2, 2, 2]`):
          Number of focal levels in each layer of the respective stages in the encoder.
      focal_windows (`list(int)`, *optional*, defaults to `[3, 3, 3, 3]`):
          Focal window size in each layer of the respective stages in the encoder.
      hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
          The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`,
          `"selu"` and `"gelu_new"` are supported.
      mlp_ratio (`float`, *optional*, defaults to 4.0):
          Ratio of MLP hidden dimensionality to embedding dimensionality.
      hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
          The dropout probability for all fully connected layers in the embeddings and encoder.
      drop_path_rate (`float`, *optional*, defaults to 0.1):
          Stochastic depth rate.
      use_layerscale (`bool`, *optional*, defaults to `False`):
          Whether to use layer scale in the encoder.
      layerscale_value (`float`, *optional*, defaults to 0.0001):
          The initial value of the layer scale.
      use_post_layernorm (`bool`, *optional*, defaults to `False`):
          Whether to use post layer normalization in the encoder.
      use_post_layernorm_in_modulation (`bool`, *optional*, defaults to `False`):
          Whether to use post layer normalization in the modulation layer.
      normalize_modulator (`bool`, *optional*, defaults to `False`):
          Whether to normalize the modulator.
      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-05):
          The epsilon used by the layer normalization layers.
      encoder_stride (`int`, *optional*, defaults to 32):
          Factor to increase the spatial resolution by in the decoder head for masked image modeling.
      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 FocalNetConfig, FocalNetModel
  
  >>> # Initializing a FocalNet microsoft/focalnet-tiny style configuration
  >>> configuration = FocalNetConfig()
  
  >>> # Initializing a model (with random weights) from the microsoft/focalnet-tiny style configuration
  >>> model = FocalNetModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
code: def __init__(
      self,
      image_size=224,
      patch_size=4,
      num_channels=3,
      embed_dim=96,
      use_conv_embed=False,
      hidden_sizes=[192, 384, 768, 768],
      depths=[2, 2, 6, 2],
      focal_levels=[2, 2, 2, 2],
      focal_windows=[3, 3, 3, 3],
      hidden_act="gelu",
      mlp_ratio=4.0,
      hidden_dropout_prob=0.0,
      drop_path_rate=0.1,
      use_layerscale=False,
      layerscale_value=1e-4,
      use_post_layernorm=False,
      use_post_layernorm_in_modulation=False,
      normalize_modulator=False,
      initializer_range=0.02,
      layer_norm_eps=1e-5,
      encoder_stride=32,
      out_features=None,
      out_indices=None,
      **kwargs,
  ):
      super().__init__(**kwargs)
  
      self.image_size = image_size
      self.patch_size = patch_size
      self.num_channels = num_channels
      self.embed_dim = embed_dim
      self.use_conv_embed = use_conv_embed
      self.hidden_sizes = hidden_sizes
      self.depths = depths
      self.focal_levels = focal_levels
      self.focal_windows = focal_windows
      self.hidden_act = hidden_act
      self.mlp_ratio = mlp_ratio
      self.hidden_dropout_prob = hidden_dropout_prob
      self.drop_path_rate = drop_path_rate
      self.use_layerscale = use_layerscale
      self.layerscale_value = layerscale_value
      self.use_post_layernorm = use_post_layernorm
      self.use_post_layernorm_in_modulation = (
          use_post_layernorm_in_modulation
      )
      self.normalize_modulator = normalize_modulator
      self.initializer_range = initializer_range
      self.layer_norm_eps = layer_norm_eps
      self.encoder_stride = encoder_stride
      self.stage_names = ["stem"] + [
          f"stage{idx}" for idx in range(1, len(self.depths) + 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 FocalNetConfig object from inherited base classes: BackboneConfigMixin, PretrainedConfig with example: obj = FocalNetConfig(image_size, patch_size, num_channels, embed_dim, use_conv_embed, hidden_sizes, depths, focal_levels, focal_windows, hidden_act, mlp_ratio, hidden_dropout_prob, drop_path_rate, use_layerscale, layerscale_value, use_post_layernorm, use_post_layernorm_in_modulation, normalize_modulator, initializer_range, layer_norm_eps, encoder_stride, out_features, out_indices)