class_docstr: This is the configuration class to store the configuration of a [`NatModel`]. It is used to instantiate a Nat 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 Nat
  [shi-labs/nat-mini-in1k-224](https://huggingface.co/shi-labs/nat-mini-in1k-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:
      patch_size (`int`, *optional*, defaults to 4):
          The size (resolution) of each patch. NOTE: Only patch size of 4 is supported at the moment.
      num_channels (`int`, *optional*, defaults to 3):
          The number of input channels.
      embed_dim (`int`, *optional*, defaults to 64):
          Dimensionality of patch embedding.
      depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 5]`):
          Number of layers in each level of the encoder.
      num_heads (`List[int]`, *optional*, defaults to `[2, 4, 8, 16]`):
          Number of attention heads in each layer of the Transformer encoder.
      kernel_size (`int`, *optional*, defaults to 7):
          Neighborhood Attention kernel size.
      mlp_ratio (`float`, *optional*, defaults to 3.0):
          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.
      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.
      layer_scale_init_value (`float`, *optional*, defaults to 0.0):
          The initial value for the layer scale. Disabled if <=0.
      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 NatConfig, NatModel
  
  >>> # Initializing a Nat shi-labs/nat-mini-in1k-224 style configuration
  >>> configuration = NatConfig()
  
  >>> # Initializing a model (with random weights) from the shi-labs/nat-mini-in1k-224 style configuration
  >>> model = NatModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
code: def __init__(
      self,
      patch_size=4,
      num_channels=3,
      embed_dim=64,
      depths=[3, 4, 6, 5],
      num_heads=[2, 4, 8, 16],
      kernel_size=7,
      mlp_ratio=3.0,
      qkv_bias=True,
      hidden_dropout_prob=0.0,
      attention_probs_dropout_prob=0.0,
      drop_path_rate=0.1,
      hidden_act="gelu",
      initializer_range=0.02,
      layer_norm_eps=1e-5,
      layer_scale_init_value=0.0,
      out_features=None,
      out_indices=None,
      **kwargs,
  ):
      super().__init__(**kwargs)
  
      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.kernel_size = kernel_size
      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.layer_norm_eps = layer_norm_eps
      self.initializer_range = initializer_range
      # we set the hidden_size attribute in order to make Nat 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))
      self.layer_scale_init_value = layer_scale_init_value
      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,
      )
  
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 NatConfig object from inherited base classes: BackboneConfigMixin, PretrainedConfig with example: obj = NatConfig(patch_size, num_channels, embed_dim, depths, num_heads, kernel_size, mlp_ratio, qkv_bias, hidden_dropout_prob, attention_probs_dropout_prob, drop_path_rate, hidden_act, initializer_range, layer_norm_eps, layer_scale_init_value, out_features, out_indices)