code: def __init__(
      self,
      vocab_size=50280,
      d_model=2560,
      n_layer=64,
      rms_norm=True,
      residual_in_fp32=True,
      fused_add_norm=True,
      pad_vocab_size_multiple=8,
      pad_token_id=50277,
      bos_token_id=0,
      eos_token_id=0,
      tie_word_embeddings=False,
      **kwargs,
  ):
      self.vocab_size = vocab_size
      self.d_model = d_model
      self.n_layer = n_layer
      self.rms_norm = rms_norm
      self.residual_in_fp32 = residual_in_fp32
      self.fused_add_norm = fused_add_norm
      self.pad_vocab_size_multiple = pad_vocab_size_multiple
      super().__init__(
          pad_token_id=pad_token_id,
          bos_token_id=bos_token_id,
          eos_token_id=eos_token_id,
          tie_word_embeddings=tie_word_embeddings,
          **kwargs,
      )
  
imports: from transformers import PretrainedConfig
label_desc: Use image node_id 1 to create a new MambaConfig object from inherited base classes: PretrainedConfig with example: obj = MambaConfig(vocab_size, d_model, n_layer, rms_norm, residual_in_fp32, fused_add_norm, pad_vocab_size_multiple, pad_token_id, bos_token_id, eos_token_id, tie_word_embeddings)