code: def __init__(
      self,
      vocab_size=130528,
      hidden_size=4096,
      num_layers=28,
      num_attention_heads=32,
      layernorm_epsilon=1e-5,
      use_cache=True,
      bos_token_id=130004,
      eos_token_id=130005,
      mask_token_id=130000,
      gmask_token_id=130001,
      pad_token_id=3,
      max_sequence_length=2048,
      inner_hidden_size=16384,
      position_encoding_2d=True,
      quantization_bit=0,
      pre_seq_len=None,
      prefix_projection=False,
      **kwargs,
  ):
      self.num_layers = num_layers
      self.vocab_size = vocab_size
      self.hidden_size = hidden_size
      self.num_attention_heads = num_attention_heads
      self.max_sequence_length = max_sequence_length
      self.layernorm_epsilon = layernorm_epsilon
      self.inner_hidden_size = inner_hidden_size
      self.use_cache = use_cache
      self.bos_token_id = bos_token_id
      self.eos_token_id = eos_token_id
      self.pad_token_id = pad_token_id
      self.mask_token_id = mask_token_id
      self.gmask_token_id = gmask_token_id
      self.position_encoding_2d = position_encoding_2d
      self.quantization_bit = quantization_bit
      self.pre_seq_len = pre_seq_len
      self.prefix_projection = prefix_projection
  
      super().__init__(
          pad_token_id=pad_token_id,
          bos_token_id=bos_token_id,
          eos_token_id=eos_token_id,
          **kwargs,
      )
  
imports: from transformers.configuration_utils import PretrainedConfig
  from transformers.utils import logging
label_desc: Use image node_id 1 to create a new ChatGLMConfig object from inherited base classes: PretrainedConfig with example: obj = ChatGLMConfig(vocab_size, hidden_size, num_layers, num_attention_heads, layernorm_epsilon, use_cache, bos_token_id, eos_token_id, mask_token_id, gmask_token_id, pad_token_id, max_sequence_length, inner_hidden_size, position_encoding_2d, quantization_bit, pre_seq_len, prefix_projection)