class_docstr: This is the configuration class to store the configuration of a [`CvtModel`]. It is used to instantiate a CvT 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 CvT
  [microsoft/cvt-13](https://huggingface.co/microsoft/cvt-13) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      num_channels (`int`, *optional*, defaults to 3):
          The number of input channels.
      patch_sizes (`List[int]`, *optional*, defaults to `[7, 3, 3]`):
          The kernel size of each encoder's patch embedding.
      patch_stride (`List[int]`, *optional*, defaults to `[4, 2, 2]`):
          The stride size of each encoder's patch embedding.
      patch_padding (`List[int]`, *optional*, defaults to `[2, 1, 1]`):
          The padding size of each encoder's patch embedding.
      embed_dim (`List[int]`, *optional*, defaults to `[64, 192, 384]`):
          Dimension of each of the encoder blocks.
      num_heads (`List[int]`, *optional*, defaults to `[1, 3, 6]`):
          Number of attention heads for each attention layer in each block of the Transformer encoder.
      depth (`List[int]`, *optional*, defaults to `[1, 2, 10]`):
          The number of layers in each encoder block.
      mlp_ratios (`List[float]`, *optional*, defaults to `[4.0, 4.0, 4.0, 4.0]`):
          Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
          encoder blocks.
      attention_drop_rate (`List[float]`, *optional*, defaults to `[0.0, 0.0, 0.0]`):
          The dropout ratio for the attention probabilities.
      drop_rate (`List[float]`, *optional*, defaults to `[0.0, 0.0, 0.0]`):
          The dropout ratio for the patch embeddings probabilities.
      drop_path_rate (`List[float]`, *optional*, defaults to `[0.0, 0.0, 0.1]`):
          The dropout probability for stochastic depth, used in the blocks of the Transformer encoder.
      qkv_bias (`List[bool]`, *optional*, defaults to `[True, True, True]`):
          The bias bool for query, key and value in attentions
      cls_token (`List[bool]`, *optional*, defaults to `[False, False, True]`):
          Whether or not to add a classification token to the output of each of the last 3 stages.
      qkv_projection_method (`List[string]`, *optional*, defaults to ["dw_bn", "dw_bn", "dw_bn"]`):
          The projection method for query, key and value Default is depth-wise convolutions with batch norm. For
          Linear projection use "avg".
      kernel_qkv (`List[int]`, *optional*, defaults to `[3, 3, 3]`):
          The kernel size for query, key and value in attention layer
      padding_kv (`List[int]`, *optional*, defaults to `[1, 1, 1]`):
          The padding size for key and value in attention layer
      stride_kv (`List[int]`, *optional*, defaults to `[2, 2, 2]`):
          The stride size for key and value in attention layer
      padding_q (`List[int]`, *optional*, defaults to `[1, 1, 1]`):
          The padding size for query in attention layer
      stride_q (`List[int]`, *optional*, defaults to `[1, 1, 1]`):
          The stride size for query in attention layer
      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-6):
          The epsilon used by the layer normalization layers.
  
  Example:
  
  ```python
  >>> from transformers import CvtConfig, CvtModel
  
  >>> # Initializing a Cvt msft/cvt style configuration
  >>> configuration = CvtConfig()
  
  >>> # Initializing a model (with random weights) from the msft/cvt style configuration
  >>> model = CvtModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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code: def __init__(
      self,
      num_channels=3,
      patch_sizes=[7, 3, 3],
      patch_stride=[4, 2, 2],
      patch_padding=[2, 1, 1],
      embed_dim=[64, 192, 384],
      num_heads=[1, 3, 6],
      depth=[1, 2, 10],
      mlp_ratio=[4.0, 4.0, 4.0],
      attention_drop_rate=[0.0, 0.0, 0.0],
      drop_rate=[0.0, 0.0, 0.0],
      drop_path_rate=[0.0, 0.0, 0.1],
      qkv_bias=[True, True, True],
      cls_token=[False, False, True],
      qkv_projection_method=["dw_bn", "dw_bn", "dw_bn"],
      kernel_qkv=[3, 3, 3],
      padding_kv=[1, 1, 1],
      stride_kv=[2, 2, 2],
      padding_q=[1, 1, 1],
      stride_q=[1, 1, 1],
      initializer_range=0.02,
      layer_norm_eps=1e-12,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.num_channels = num_channels
      self.patch_sizes = patch_sizes
      self.patch_stride = patch_stride
      self.patch_padding = patch_padding
      self.embed_dim = embed_dim
      self.num_heads = num_heads
      self.depth = depth
      self.mlp_ratio = mlp_ratio
      self.attention_drop_rate = attention_drop_rate
      self.drop_rate = drop_rate
      self.drop_path_rate = drop_path_rate
      self.qkv_bias = qkv_bias
      self.cls_token = cls_token
      self.qkv_projection_method = qkv_projection_method
      self.kernel_qkv = kernel_qkv
      self.padding_kv = padding_kv
      self.stride_kv = stride_kv
      self.padding_q = padding_q
      self.stride_q = stride_q
      self.initializer_range = initializer_range
      self.layer_norm_eps = layer_norm_eps
  
imports: from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new CvtConfig object from inherited base classes: PretrainedConfig with example: obj = CvtConfig(num_channels, patch_sizes, patch_stride, patch_padding, embed_dim, num_heads, depth, mlp_ratio, attention_drop_rate, drop_rate, drop_path_rate, qkv_bias, cls_token, qkv_projection_method, kernel_qkv, padding_kv, stride_kv, padding_q, stride_q, initializer_range, layer_norm_eps)