code: def __init__(
      self,
      d_model: int,
      n_layer: int,
      vocab_size: int,
      initializer_cfg=None,
      pad_vocab_size_multiple: int = 1,
      device=None,
      dtype=None,
      **backbone_kwargs,
  ) -> None:
      factory_kwargs = {"device": device, "dtype": dtype}
      super().__init__()
      if vocab_size % pad_vocab_size_multiple != 0:
          vocab_size += pad_vocab_size_multiple - (
              vocab_size % pad_vocab_size_multiple
          )
      self.config = MambaConfig(
          vocab_size=vocab_size,
          d_model=d_model,
          n_layer=n_layer,
          pad_vocab_size_multiple=pad_vocab_size_multiple,
      )
      self.backbone = MixerModel(
          d_model=d_model,
          n_layer=n_layer,
          vocab_size=vocab_size,
          initializer_cfg=initializer_cfg,
          **backbone_kwargs,
          **factory_kwargs,
      )
      self.lm_head = nn.Linear(
          d_model, vocab_size, bias=False, **factory_kwargs
      )
  
      # Initialize weights and apply final processing
      self.apply(
          partial(
              _init_weights,
              n_layer=n_layer,
              **(
                  initializer_cfg if initializer_cfg is not None else {}
              ),
          )
      )
      self.tie_weights()
  
file_path: axolotl/src/axolotl/models/mamba/modeling_mamba.py
imports: import os
  from collections import namedtuple
  from functools import partial
  from typing import Optional, Union
  import torch
  from mamba_ssm.models.mixer_seq_simple import MixerModel, _init_weights
  from mamba_ssm.utils.generation import GenerationMixin
  from mamba_ssm.utils.hf import load_config_hf, load_state_dict_hf
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from axolotl.models.mamba.configuration_mamba import MambaConfig
label_desc: Use image node_id 1 to create a new MambaLMHeadModel object from inherited base classes: nn, GenerationMixin with example: obj = MambaLMHeadModel(d_model, n_layer, vocab_size, initializer_cfg, pad_vocab_size_multiple, device, dtype)