class_docstr: This is the configuration class to store the configuration of a [`~ChatGLMModel`].
  It is used to instantiate an ChatGLM model according to the specified arguments, defining the model
  architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
  the ChatGLM-6B [THUDM/ChatGLM-6B](https://huggingface.co/THUDM/chatglm-6b) architecture.
  
  Configuration objects inherit from  [`PretrainedConfig`] and can be used
  to control the model outputs. Read the documentation from  [`PretrainedConfig`]
  for more information.
  
  
  Args:
      vocab_size (`int`, *optional*, defaults to 150528):
          Vocabulary size of the ChatGLM-6B model. Defines the number of different tokens that can be represented by the
          `inputs_ids` passed when calling [`~ChatGLMModel`] or
          [`~TFChatGLMModel`].
      hidden_size (`int`, *optional*, defaults to 4096):
          Dimension of the encoder layers and the pooler layer.
      num_hidden_layers (`int`, *optional*, defaults to 28):
          Number of hidden layers in the Transformer encoder.
      num_attention_heads (`int`, *optional*, defaults to 32):
          Number of attention heads for each attention layer in the Transformer encoder.
      inner_hidden_size (`int`, *optional*, defaults to 16384):
          Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
      max_sequence_length (`int`, *optional*, defaults to 512):
          The maximum sequence length that this model might ever be used with.
          Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
      layernorm_epsilon (`float`, *optional*, defaults to 1e-5):
          The epsilon used by the layer normalization layers.
      use_cache (`bool`, *optional*, defaults to `True`):
          Whether the model should return the last key/values attentions (not used by all models).
      Example:
  
  ```python
  >>> from configuration_chatglm import ChatGLMConfig
  >>> from modeling_chatglm import ChatGLMModel
  
  >>> # Initializing a ChatGLM-6B THUDM/ChatGLM-6B style configuration
  >>> configuration = ChatGLMConfig()
  
  >>> # Initializing a model from the THUDM/ChatGLM-6B style configuration
  >>> model = ChatGLMModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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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)