code: def prepare_kv_cache_selective_batching(
      self,
      cur_seq_ids: List[int],
      seq_group_meta_data_lists: List[SequenceGroupMetadata],
      kv_cache: Dict,
      num_layers: int,
      kv_cache_size_1: int,
  ):
      # Return bigdl_kv_cache in the format of Tuple(List[Tuple(torch.Tensor)])
      bigdl_kv_cache = []
      for i in range(num_layers):
          # Construct a list of tuple(tensor)
          temp_cache = []
          for seq_id in cur_seq_ids:
              key = kv_cache[i][0][seq_id]
              value = kv_cache[i][1][seq_id]
              temp_cache.append((key, value))
          bigdl_kv_cache.append(temp_cache)
      return bigdl_kv_cache
  
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 6 for calling the BigDLModelForCausalLM obj's underlying member method code with example usage: obj.prepare_kv_cache_selective_batching(cur_seq_ids, seq_group_meta_data_lists, kv_cache, num_layers, kv_cache_size_1) and returns: bigdl_kv_cache