code: def __init__(
      self,
      vocab_size=50432,
      hidden_size=6144,
      num_hidden_layers=44,
      num_attention_heads=64,
      intermediate_size=24576,
      hidden_act="gelu",
      rotary_pct=0.25,
      rotary_emb_base=10000,
      max_position_embeddings=2048,
      initializer_range=0.02,
      layer_norm_eps=1e-5,
      use_cache=True,
      bos_token_id=0,
      eos_token_id=2,
      tie_word_embeddings=False,
      use_parallel_residual=True,
      **kwargs,
  ):
      super().__init__(
          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.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.rotary_pct = rotary_pct
      self.rotary_emb_base = rotary_emb_base
      self.initializer_range = initializer_range
      self.layer_norm_eps = layer_norm_eps
      self.use_cache = use_cache
      self.tie_word_embeddings = tie_word_embeddings
      self.use_parallel_residual = use_parallel_residual
  
imports: from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new GPTNeoXConfig object from inherited base classes: PretrainedConfig with example: obj = GPTNeoXConfig(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, rotary_pct, rotary_emb_base, max_position_embeddings, initializer_range, layer_norm_eps, use_cache, bos_token_id, eos_token_id, tie_word_embeddings, use_parallel_residual)