code: def __init__(
      self,
      config,
      device: Optional[str] = None,
      max_model_len: Optional[int] = None,
  ):
      super().__init__()
      self.config = config
      if device is None:
          self.device = torch.device(
              "cuda" if torch.cuda.is_available() else "cpu"
          )
      else:
          self.device = torch.device(device)
          if device == "xpu":
              try:
                  import intel_extension_for_pytorch as ipex
              except ImportError:
                  print(
                      "Intel Extension for PyTorch is not installed, \
                          but is required for xpu inference."
                  )
  
      self.max_seq_limit = max_model_len
      self.last_kv_cache = None
      self.last_seq_ids = None
  
imports: import torch
  from torch import nn
  from typing import Optional, Tuple, List, Type, Dict
  from transformers import LlamaConfig
  from bigdl.llm.vllm.sequence import SequenceOutputs, SequenceGroupMetadata
  from bigdl.llm.transformers.models.utils import extend_kv_cache
  from bigdl.llm.vllm.logger import init_logger
label_desc: Use image node_id 1 to create a new BigDLModelForCausalLM object from inherited base classes: nn with example: obj = BigDLModelForCausalLM(config, device, max_model_len)