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,
      use_cache=True,
      pad_token_id=0,
      position_embedding_type="absolute",
      classifier_dropout=None,
      enable_pronunciation=True,
      enable_shape=True,
      pronunciation_embed_dim=768,
      pronunciation_vocab_size=910,
      shape_embed_dim=512,
      shape_vocab_size=24858,
      concat_input=True,
      **kwargs,
  ):
      self.vocab_size = vocab_size
      self.max_position_embeddings = max_position_embeddings
      self.hidden_size = hidden_size
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
      self.intermediate_size = intermediate_size
      self.hidden_act = hidden_act
      self.hidden_dropout_prob = hidden_dropout_prob
      self.attention_probs_dropout_prob = attention_probs_dropout_prob
      self.initializer_range = initializer_range
      self.type_vocab_size = type_vocab_size
      self.layer_norm_eps = layer_norm_eps
      self.use_cache = use_cache
      self.enable_pronunciation = enable_pronunciation
      self.enable_shape = enable_shape
      self.pronunciation_embed_dim = pronunciation_embed_dim
      self.pronunciation_vocab_size = pronunciation_vocab_size
      self.shape_embed_dim = shape_embed_dim
      self.shape_vocab_size = shape_vocab_size
      self.concat_input = concat_input
      self.position_embedding_type = position_embedding_type
      self.classifier_dropout = classifier_dropout
      super().__init__(pad_token_id=pad_token_id, **kwargs)
  
imports: from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new RoCBertConfig object from inherited base classes: PretrainedConfig with example: obj = RoCBertConfig(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, use_cache, pad_token_id, position_embedding_type, classifier_dropout, enable_pronunciation, enable_shape, pronunciation_embed_dim, pronunciation_vocab_size, shape_embed_dim, shape_vocab_size, concat_input)