code: def __init__(
      self,
      num_layers=28,
      padded_vocab_size=65024,
      hidden_size=4096,
      ffn_hidden_size=13696,
      kv_channels=128,
      num_attention_heads=32,
      seq_length=2048,
      hidden_dropout=0.0,
      attention_dropout=0.0,
      layernorm_epsilon=1e-5,
      rmsnorm=True,
      apply_residual_connection_post_layernorm=False,
      post_layer_norm=True,
      add_bias_linear=False,
      add_qkv_bias=False,
      bias_dropout_fusion=True,
      multi_query_attention=False,
      multi_query_group_num=1,
      apply_query_key_layer_scaling=True,
      attention_softmax_in_fp32=True,
      fp32_residual_connection=False,
      quantization_bit=0,
      pre_seq_len=None,
      prefix_projection=False,
      **kwargs,
  ):
      self.num_layers = num_layers
      self.vocab_size = padded_vocab_size
      self.padded_vocab_size = padded_vocab_size
      self.hidden_size = hidden_size
      self.ffn_hidden_size = ffn_hidden_size
      self.kv_channels = kv_channels
      self.num_attention_heads = num_attention_heads
      self.seq_length = seq_length
      self.hidden_dropout = hidden_dropout
      self.attention_dropout = attention_dropout
      self.layernorm_epsilon = layernorm_epsilon
      self.rmsnorm = rmsnorm
      self.apply_residual_connection_post_layernorm = (
          apply_residual_connection_post_layernorm
      )
      self.post_layer_norm = post_layer_norm
      self.add_bias_linear = add_bias_linear
      self.add_qkv_bias = add_qkv_bias
      self.bias_dropout_fusion = bias_dropout_fusion
      self.multi_query_attention = multi_query_attention
      self.multi_query_group_num = multi_query_group_num
      self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
      self.attention_softmax_in_fp32 = attention_softmax_in_fp32
      self.fp32_residual_connection = fp32_residual_connection
      self.quantization_bit = quantization_bit
      self.pre_seq_len = pre_seq_len
      self.prefix_projection = prefix_projection
      super().__init__(**kwargs)
  
label_desc: Use image node_id 1 to create a new ChatGLMConfig object from inherited base classes: PretrainedConfig with example: obj = ChatGLMConfig(num_layers, padded_vocab_size, hidden_size, ffn_hidden_size, kv_channels, num_attention_heads, seq_length, hidden_dropout, attention_dropout, layernorm_epsilon, rmsnorm, apply_residual_connection_post_layernorm, post_layer_norm, add_bias_linear, add_qkv_bias, bias_dropout_fusion, multi_query_attention, multi_query_group_num, apply_query_key_layer_scaling, attention_softmax_in_fp32, fp32_residual_connection, quantization_bit, pre_seq_len, prefix_projection)