code: def _pad_kv_cache_view(
      t: torch.Tensor, len: int, device: torch.device, pos: int = 2
  ) -> torch.Tensor:
      cur_size = list(t.size())
      if cur_size[pos] < len:
          zeros = get_zero_tensor(len, cur_size, device, pos)
          padded_view = torch.cat((zeros, t), dim=pos)
          return padded_view
      elif cur_size[pos] > len:
          padded_view = t.narrow(pos, cur_size[pos] - len, len)
          return padded_view
      else:
          return t
  
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 2 for calling a global function with example usage: _pad_kv_cache_view(t, len, device, pos) and returns: padded_view, padded_view, t