class_docstr: This is the configuration class to store the configuration of a [`LayoutLMModel`]. It is used to instantiate a
  LayoutLM 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 LayoutLM
  [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) architecture.
  
  Configuration objects inherit from [`BertConfig`] and can be used to control the model outputs. Read the
  documentation from [`BertConfig`] for more information.
  
  
  Args:
      vocab_size (`int`, *optional*, defaults to 30522):
          Vocabulary size of the LayoutLM model. Defines the different tokens that can be represented by the
          *inputs_ids* passed to the forward method of [`LayoutLMModel`].
      hidden_size (`int`, *optional*, defaults to 768):
          Dimensionality 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):
          Dimensionality 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"`, `"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 into [`LayoutLMModel`].
      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 used. Typically set this to something large
          just in case (e.g., 1024).
  
  Examples:
  
  ```python
  >>> from transformers import LayoutLMConfig, LayoutLMModel
  
  >>> # Initializing a LayoutLM configuration
  >>> configuration = LayoutLMConfig()
  
  >>> # Initializing a model (with random weights) from the configuration
  >>> model = LayoutLMModel(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,
      position_embedding_type="absolute",
      use_cache=True,
      classifier_dropout=None,
      max_2d_position_embeddings=1024,
      **kwargs,
  ):
      super().__init__(pad_token_id=pad_token_id, **kwargs)
      self.vocab_size = vocab_size
      self.hidden_size = hidden_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.position_embedding_type = position_embedding_type
      self.use_cache = use_cache
      self.classifier_dropout = classifier_dropout
      self.max_2d_position_embeddings = max_2d_position_embeddings
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/layoutlm/configuration_layoutlm.py
imports: from collections import OrderedDict
  from typing import Any, List, Mapping, Optional
  from transformers import PretrainedConfig, PreTrainedTokenizer, TensorType
  from ...None import is_torch_available
  from ...onnx import OnnxConfig, PatchingSpec
  from ...utils import logging
label_desc: Use image node_id 1 to create a new LayoutLMConfig object from inherited base classes: PretrainedConfig with example: obj = LayoutLMConfig(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, position_embedding_type, use_cache, classifier_dropout, max_2d_position_embeddings)