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,
      project_dim=512,
      pooler_fn="average",
      learn_encoder=False,
      model_type="bert",
      **kwargs,
  ):
      super().__init__(
          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,
          **kwargs,
      )
      self.project_dim = project_dim
      self.pooler_fn = pooler_fn
      self.learn_encoder = learn_encoder
  
file_path: stable-diffusion-webui/modules/xlmr.py
imports: from transformers import BertPreTrainedModel, BertConfig
  import torch.nn
  import torch
  from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig
  from transformers import XLMRobertaModel, XLMRobertaTokenizer
  from typing import Optional
label_desc: Use image node_id 1 to create a new BertSeriesConfig object from inherited base classes: BertConfig with example: obj = BertSeriesConfig(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, project_dim, pooler_fn, learn_encoder, model_type)