class_docstr: This is the configuration class to store the configuration of a [`Swinv2Model`]. It is used to instantiate a Swin
  Transformer v2 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 Swin Transformer v2
  [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256)
  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.
      num_channels (`int`, *optional*, defaults to 3):
          The number of input channels.
      embed_dim (`int`, *optional*, defaults to 96):
          Dimensionality of patch embedding.
      depths (`list(int)`, *optional*, defaults to `[2, 2, 6, 2]`):
          Depth of each layer in the Transformer encoder.
      num_heads (`list(int)`, *optional*, defaults to `[3, 6, 12, 24]`):
          Number of attention heads in each layer of the Transformer encoder.
      window_size (`int`, *optional*, defaults to 7):
          Size of windows.
      pretrained_window_sizes (`list(int)`, *optional*, defaults to `[0, 0, 0, 0]`):
          Size of windows during pretraining.
      mlp_ratio (`float`, *optional*, defaults to 4.0):
          Ratio of MLP hidden dimensionality to embedding dimensionality.
      qkv_bias (`bool`, *optional*, defaults to `True`):
          Whether or not a learnable bias should be added to the queries, keys and values.
      hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
          The dropout probability for all fully connected layers in the embeddings and encoder.
      attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
          The dropout ratio for the attention probabilities.
      drop_path_rate (`float`, *optional*, defaults to 0.1):
          Stochastic depth rate.
      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.
      use_absolute_embeddings (`bool`, *optional*, defaults to `False`):
          Whether or not to add absolute position embeddings to the patch embeddings.
      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.
      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.
  
  Example:
  
  ```python
  >>> from transformers import Swinv2Config, Swinv2Model
  
  >>> # Initializing a Swinv2 microsoft/swinv2-tiny-patch4-window8-256 style configuration
  >>> configuration = Swinv2Config()
  
  >>> # Initializing a model (with random weights) from the microsoft/swinv2-tiny-patch4-window8-256 style configuration
  >>> model = Swinv2Model(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
code: def __init__(
      self,
      image_size=224,
      patch_size=4,
      num_channels=3,
      embed_dim=96,
      depths=[2, 2, 6, 2],
      num_heads=[3, 6, 12, 24],
      window_size=7,
      pretrained_window_sizes=[0, 0, 0, 0],
      mlp_ratio=4.0,
      qkv_bias=True,
      hidden_dropout_prob=0.0,
      attention_probs_dropout_prob=0.0,
      drop_path_rate=0.1,
      hidden_act="gelu",
      use_absolute_embeddings=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.depths = depths
      self.num_layers = len(depths)
      self.num_heads = num_heads
      self.window_size = window_size
      self.pretrained_window_sizes = pretrained_window_sizes
      self.mlp_ratio = mlp_ratio
      self.qkv_bias = qkv_bias
      self.hidden_dropout_prob = hidden_dropout_prob
      self.attention_probs_dropout_prob = attention_probs_dropout_prob
      self.drop_path_rate = drop_path_rate
      self.hidden_act = hidden_act
      self.use_absolute_embeddings = use_absolute_embeddings
      self.layer_norm_eps = layer_norm_eps
      self.initializer_range = initializer_range
      self.encoder_stride = encoder_stride
      self.stage_names = ["stem"] + [
          f"stage{idx}" for idx in range(1, len(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,
      )
      # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))
  
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 Swinv2Config object from inherited base classes: BackboneConfigMixin, PretrainedConfig with example: obj = Swinv2Config(image_size, patch_size, num_channels, embed_dim, depths, num_heads, window_size, pretrained_window_sizes, mlp_ratio, qkv_bias, hidden_dropout_prob, attention_probs_dropout_prob, drop_path_rate, hidden_act, use_absolute_embeddings, initializer_range, layer_norm_eps, encoder_stride, out_features, out_indices)