class_docstr: This is the configuration class to store the configuration of a [`LukeModel`]. It is used to instantiate a LUKE
  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 LUKE
  [studio-ousia/luke-base](https://huggingface.co/studio-ousia/luke-base) 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 LUKE model. Defines the number of different tokens that can be represented by the
          `inputs_ids` passed when calling [`LukeModel`].
      entity_vocab_size (`int`, *optional*, defaults to 500000):
          Entity vocabulary size of the LUKE model. Defines the number of different entities that can be represented
          by the `entity_ids` passed when calling [`LukeModel`].
      hidden_size (`int`, *optional*, defaults to 768):
          Dimensionality of the encoder layers and the pooler layer.
      entity_emb_size (`int`, *optional*, defaults to 256):
          The number of dimensions of the entity embedding.
      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):
          Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
      hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
          The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
          `"relu"`, `"silu"` and `"gelu_new"` are supported.
      hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
          The dropout probability 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 [`LukeModel`].
      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.
      use_entity_aware_attention (`bool`, defaults to `True`):
          Whether or not the model should use the entity-aware self-attention mechanism proposed in [LUKE: Deep
          Contextualized Entity Representations with Entity-aware Self-attention (Yamada et
          al.)](https://arxiv.org/abs/2010.01057).
      classifier_dropout (`float`, *optional*):
          The dropout ratio for the classification head.
  
  Examples:
  
  ```python
  >>> from transformers import LukeConfig, LukeModel
  
  >>> # Initializing a LUKE configuration
  >>> configuration = LukeConfig()
  
  >>> # Initializing a model from the configuration
  >>> model = LukeModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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docstr: Constructs LukeConfig.
docstr_tok: ["Constructs","LukeConfig","."]
code: def __init__(
      self,
      vocab_size=50267,
      entity_vocab_size=500000,
      hidden_size=768,
      entity_emb_size=256,
      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,
      use_entity_aware_attention=True,
      classifier_dropout=None,
      pad_token_id=1,
      bos_token_id=0,
      eos_token_id=2,
      **kwargs,
  ):
      """Constructs LukeConfig."""
      super().__init__(
          pad_token_id=pad_token_id,
          bos_token_id=bos_token_id,
          eos_token_id=eos_token_id,
          **kwargs,
      )
  
      self.vocab_size = vocab_size
      self.entity_vocab_size = entity_vocab_size
      self.hidden_size = hidden_size
      self.entity_emb_size = entity_emb_size
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
      self.hidden_act = hidden_act
      self.intermediate_size = intermediate_size
      self.hidden_dropout_prob = hidden_dropout_prob
      self.attention_probs_dropout_prob = attention_probs_dropout_prob
      self.max_position_embeddings = max_position_embeddings
      self.type_vocab_size = type_vocab_size
      self.initializer_range = initializer_range
      self.layer_norm_eps = layer_norm_eps
      self.use_entity_aware_attention = use_entity_aware_attention
      self.classifier_dropout = classifier_dropout
  
imports: from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new LukeConfig object from inherited base classes: PretrainedConfig with example: obj = LukeConfig(vocab_size, entity_vocab_size, hidden_size, entity_emb_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, use_entity_aware_attention, classifier_dropout, pad_token_id, bos_token_id, eos_token_id)