class_docstr: A class replicating the `~transformers.BertConfig` with additional parameters for pruning/masking configuration.
class_docstr_tok: ["A","class","replicating","the","`","~transformers.BertConfig","`","with","additional","parameters","for","pruning\/masking","configuration","."]
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,
      pruning_method="topK",
      mask_init="constant",
      mask_scale=0.0,
      **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.pruning_method = pruning_method
      self.mask_init = mask_init
      self.mask_scale = mask_scale
  
imports: import logging
  from transformers.configuration_utils import PretrainedConfig
label_desc: Use image node_id 1 to create a new MaskedBertConfig object from inherited base classes: PretrainedConfig with example: obj = MaskedBertConfig(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, pruning_method, mask_init, mask_scale)