class_docstr: This is the configuration class to store the configuration of a [`LayoutLMv2Model`]. It is used to instantiate an
  LayoutLMv2 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 LayoutLMv2
  [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      vocab_size (`int`, *optional*, defaults to 30522):
          Vocabulary size of the LayoutLMv2 model. Defines the number of different tokens that can be represented by
          the `inputs_ids` passed when calling [`LayoutLMv2Model`] or [`TFLayoutLMv2Model`].
      hidden_size (`int`, *optional*, defaults to 768):
          Dimension 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.
      intermediate_size (`int`, *optional*, defaults to 3072):
          Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
      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.
      hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
          The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
      attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
          The dropout ratio for the attention probabilities.
      max_position_embeddings (`int`, *optional*, defaults to 512):
          The maximum sequence length that this model might ever be used with. Typically set this to something large
          just in case (e.g., 512 or 1024 or 2048).
      type_vocab_size (`int`, *optional*, defaults to 2):
          The vocabulary size of the `token_type_ids` passed when calling [`LayoutLMv2Model`] or
          [`TFLayoutLMv2Model`].
      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-12):
          The epsilon used by the layer normalization layers.
      max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
          The maximum value that the 2D position embedding might ever be used with. Typically set this to something
          large just in case (e.g., 1024).
      max_rel_pos (`int`, *optional*, defaults to 128):
          The maximum number of relative positions to be used in the self-attention mechanism.
      rel_pos_bins (`int`, *optional*, defaults to 32):
          The number of relative position bins to be used in the self-attention mechanism.
      fast_qkv (`bool`, *optional*, defaults to `True`):
          Whether or not to use a single matrix for the queries, keys, values in the self-attention layers.
      max_rel_2d_pos (`int`, *optional*, defaults to 256):
          The maximum number of relative 2D positions in the self-attention mechanism.
      rel_2d_pos_bins (`int`, *optional*, defaults to 64):
          The number of 2D relative position bins in the self-attention mechanism.
      image_feature_pool_shape (`List[int]`, *optional*, defaults to [7, 7, 256]):
          The shape of the average-pooled feature map.
      coordinate_size (`int`, *optional*, defaults to 128):
          Dimension of the coordinate embeddings.
      shape_size (`int`, *optional*, defaults to 128):
          Dimension of the width and height embeddings.
      has_relative_attention_bias (`bool`, *optional*, defaults to `True`):
          Whether or not to use a relative attention bias in the self-attention mechanism.
      has_spatial_attention_bias (`bool`, *optional*, defaults to `True`):
          Whether or not to use a spatial attention bias in the self-attention mechanism.
      has_visual_segment_embedding (`bool`, *optional*, defaults to `False`):
          Whether or not to add visual segment embeddings.
      detectron2_config_args (`dict`, *optional*):
          Dictionary containing the configuration arguments of the Detectron2 visual backbone. Refer to [this
          file](https://github.com/microsoft/unilm/blob/master/layoutlmft/layoutlmft/models/layoutlmv2/detectron2_config.py)
          for details regarding default values.
  
  Example:
  
  ```python
  >>> from transformers import LayoutLMv2Config, LayoutLMv2Model
  
  >>> # Initializing a LayoutLMv2 microsoft/layoutlmv2-base-uncased style configuration
  >>> configuration = LayoutLMv2Config()
  
  >>> # Initializing a model (with random weights) from the microsoft/layoutlmv2-base-uncased style configuration
  >>> model = LayoutLMv2Model(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
code: def __init__(
      self,
      vocab_size=30522,
      hidden_size=768,
      num_hidden_layers=12,
      num_attention_heads=12,
      intermediate_size=3072,
      hidden_act="gelu",
      hidden_dropout_prob=0.1,
      attention_probs_dropout_prob=0.1,
      max_position_embeddings=512,
      type_vocab_size=2,
      initializer_range=0.02,
      layer_norm_eps=1e-12,
      pad_token_id=0,
      max_2d_position_embeddings=1024,
      max_rel_pos=128,
      rel_pos_bins=32,
      fast_qkv=True,
      max_rel_2d_pos=256,
      rel_2d_pos_bins=64,
      convert_sync_batchnorm=True,
      image_feature_pool_shape=[7, 7, 256],
      coordinate_size=128,
      shape_size=128,
      has_relative_attention_bias=True,
      has_spatial_attention_bias=True,
      has_visual_segment_embedding=False,
      detectron2_config_args=None,
      **kwargs,
  ):
      super().__init__(
          vocab_size=vocab_size,
          hidden_size=hidden_size,
          num_hidden_layers=num_hidden_layers,
          num_attention_heads=num_attention_heads,
          intermediate_size=intermediate_size,
          hidden_act=hidden_act,
          hidden_dropout_prob=hidden_dropout_prob,
          attention_probs_dropout_prob=attention_probs_dropout_prob,
          max_position_embeddings=max_position_embeddings,
          type_vocab_size=type_vocab_size,
          initializer_range=initializer_range,
          layer_norm_eps=layer_norm_eps,
          pad_token_id=pad_token_id,
          **kwargs,
      )
      self.max_2d_position_embeddings = max_2d_position_embeddings
      self.max_rel_pos = max_rel_pos
      self.rel_pos_bins = rel_pos_bins
      self.fast_qkv = fast_qkv
      self.max_rel_2d_pos = max_rel_2d_pos
      self.rel_2d_pos_bins = rel_2d_pos_bins
      self.convert_sync_batchnorm = convert_sync_batchnorm
      self.image_feature_pool_shape = image_feature_pool_shape
      self.coordinate_size = coordinate_size
      self.shape_size = shape_size
      self.has_relative_attention_bias = has_relative_attention_bias
      self.has_spatial_attention_bias = has_spatial_attention_bias
      self.has_visual_segment_embedding = has_visual_segment_embedding
      self.detectron2_config_args = (
          detectron2_config_args
          if detectron2_config_args is not None
          else self.get_default_detectron2_config()
      )
  
imports: from ...configuration_utils import PretrainedConfig
  from ...utils import is_detectron2_available, logging
label_desc: Use image node_id 1 to create a new LayoutLMv2Config object from inherited base classes: PretrainedConfig with example: obj = LayoutLMv2Config(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, pad_token_id, max_2d_position_embeddings, max_rel_pos, rel_pos_bins, fast_qkv, max_rel_2d_pos, rel_2d_pos_bins, convert_sync_batchnorm, image_feature_pool_shape, coordinate_size, shape_size, has_relative_attention_bias, has_spatial_attention_bias, has_visual_segment_embedding, detectron2_config_args)