code: def __init__(
      self,
      vocab_size=50272,
      hidden_size=768,
      num_hidden_layers=12,
      ffn_dim=3072,
      max_position_embeddings=2048,
      do_layer_norm_before=True,
      _remove_final_layer_norm=False,
      word_embed_proj_dim=None,
      dropout=0.1,
      attention_dropout=0.0,
      num_attention_heads=12,
      activation_function="relu",
      layerdrop=0.0,
      init_std=0.02,
      use_cache=True,
      pad_token_id=1,
      bos_token_id=2,
      eos_token_id=2,
      enable_bias=True,
      layer_norm_elementwise_affine=True,
      **kwargs,
  ):
      super().__init__(
          pad_token_id=pad_token_id,
          bos_token_id=bos_token_id,
          eos_token_id=eos_token_id,
          **kwargs,
      )
      self.vocab_size = vocab_size
      self.max_position_embeddings = max_position_embeddings
      self.num_attention_heads = num_attention_heads
      self.word_embed_proj_dim = (
          word_embed_proj_dim
          if word_embed_proj_dim is not None
          else hidden_size
      )
      self.ffn_dim = ffn_dim
      self.hidden_size = hidden_size
      self.num_hidden_layers = num_hidden_layers
      self.dropout = dropout
      self.attention_dropout = attention_dropout
      self.activation_function = activation_function
      self.init_std = init_std
      self.layerdrop = layerdrop
      self.use_cache = use_cache
      self.do_layer_norm_before = do_layer_norm_before
      # We keep these variables at `True` for backward compatibility.
      self.enable_bias = enable_bias
      self.layer_norm_elementwise_affine = layer_norm_elementwise_affine
  
      # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
      # with checkpoints that have been fine-tuned before transformers v4.20.1
      # see https://github.com/facebookresearch/metaseq/pull/164
      self._remove_final_layer_norm = _remove_final_layer_norm
  
imports: from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new OPTConfig object from inherited base classes: PretrainedConfig with example: obj = OPTConfig(vocab_size, hidden_size, num_hidden_layers, ffn_dim, max_position_embeddings, do_layer_norm_before, _remove_final_layer_norm, word_embed_proj_dim, dropout, attention_dropout, num_attention_heads, activation_function, layerdrop, init_std, use_cache, pad_token_id, bos_token_id, eos_token_id, enable_bias, layer_norm_elementwise_affine)