code: def __init__(
      self,
      vocab_size=256008,
      max_position_embeddings=2048,
      d_model=1024,
      ffn_dim=4096,
      num_layers=24,
      attention_heads=16,
      activation_function="gelu",
      dropout=0.1,
      attention_dropout=0.1,
      activation_dropout=0.0,
      layerdrop=0.0,
      init_std=0.02,
      scale_embedding=True,
      use_cache=True,
      decoder_start_token_id=2,
      pad_token_id=1,
      bos_token_id=0,
      eos_token_id=2,
      **kwargs,
  ):
      self.vocab_size = vocab_size
      self.max_position_embeddings = max_position_embeddings
      self.d_model = d_model
      self.ffn_dim = ffn_dim
      self.num_layers = num_layers
      self.attention_heads = attention_heads
      self.activation_function = activation_function
      self.dropout = dropout
      self.attention_dropout = attention_dropout
      self.activation_dropout = activation_dropout
      self.layerdrop = layerdrop
      self.init_std = init_std
      self.scale_embedding = (
          scale_embedding  # scale factor will be sqrt(d_model) if True
      )
      self.use_cache = use_cache
  
      super().__init__(
          pad_token_id=pad_token_id,
          bos_token_id=bos_token_id,
          eos_token_id=eos_token_id,
          decoder_start_token_id=decoder_start_token_id,
          **kwargs,
      )
  
imports: from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new XGLMConfig object from inherited base classes: PretrainedConfig with example: obj = XGLMConfig(vocab_size, max_position_embeddings, d_model, ffn_dim, num_layers, attention_heads, activation_function, dropout, attention_dropout, activation_dropout, layerdrop, init_std, scale_embedding, use_cache, decoder_start_token_id, pad_token_id, bos_token_id, eos_token_id)