code: def __init__(self, config):
      super().__init__(config)
      self.model = LlamaModel(config)
  
      self.lm_head = nn.Linear(
          config.hidden_size, config.vocab_size, bias=False
      )
  
      # Initialize weights and apply final processing
      self.post_init()
  
imports: import math
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.activations import ACT2FN
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
  from transformers.modeling_utils import PreTrainedModel
  from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from transformers.models.llama.configuration_llama import LlamaConfig
label_desc: Use image node_id 1 to create a new LlamaForCausalLM object from inherited base classes: LlamaPreTrainedModel with example: obj = LlamaForCausalLM(config)