class_docstr: Class to store the configuration of the BertAbs model.
  
  Arguments:
      vocab_size: int
          Number of tokens in the vocabulary.
      max_pos: int
          The maximum sequence length that this model will be used with.
      enc_layer: int
          The numner of hidden layers in the Transformer encoder.
      enc_hidden_size: int
          The size of the encoder's layers.
      enc_heads: int
          The number of attention heads for each attention layer in the encoder.
      enc_ff_size: int
          The size of the encoder's feed-forward layers.
      enc_dropout: int
          The dropout probability for all fully connected layers in the
          embeddings, layers, pooler and also the attention probabilities in
          the encoder.
      dec_layer: int
          The numner of hidden layers in the decoder.
      dec_hidden_size: int
          The size of the decoder's layers.
      dec_heads: int
          The number of attention heads for each attention layer in the decoder.
      dec_ff_size: int
          The size of the decoder's feed-forward layers.
      dec_dropout: int
          The dropout probability for all fully connected layers in the
          embeddings, layers, pooler and also the attention probabilities in
          the decoder.
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code: def __init__(
      self,
      vocab_size=30522,
      max_pos=512,
      enc_layers=6,
      enc_hidden_size=512,
      enc_heads=8,
      enc_ff_size=512,
      enc_dropout=0.2,
      dec_layers=6,
      dec_hidden_size=768,
      dec_heads=8,
      dec_ff_size=2048,
      dec_dropout=0.2,
      **kwargs,
  ):
      super().__init__(**kwargs)
  
      self.vocab_size = vocab_size
      self.max_pos = max_pos
  
      self.enc_layers = enc_layers
      self.enc_hidden_size = enc_hidden_size
      self.enc_heads = enc_heads
      self.enc_ff_size = enc_ff_size
      self.enc_dropout = enc_dropout
  
      self.dec_layers = dec_layers
      self.dec_hidden_size = dec_hidden_size
      self.dec_heads = dec_heads
      self.dec_ff_size = dec_ff_size
      self.dec_dropout = dec_dropout
  
imports: import logging
  from transformers import PretrainedConfig
label_desc: Use image node_id 1 to create a new BertAbsConfig object from inherited base classes: PretrainedConfig with example: obj = BertAbsConfig(vocab_size, max_pos, enc_layers, enc_hidden_size, enc_heads, enc_ff_size, enc_dropout, dec_layers, dec_hidden_size, dec_heads, dec_ff_size, dec_dropout)