code: def repeat_kv(
      hidden_states: torch.Tensor, n_rep: int
  ) -> torch.Tensor:
      """
      This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep).
      The hidden states go from (batch, num_key_value_heads, seqlen, head_dim)
      to (batch, num_attention_heads, seqlen, head_dim)
      """
      batch, num_key_value_heads, slen, head_dim = hidden_states.shape
      if n_rep == 1:
          return hidden_states
      hidden_states = hidden_states[:, :, None, :, :].expand(
          batch, num_key_value_heads, n_rep, slen, head_dim
      )
      return hidden_states.reshape(
          batch, num_key_value_heads * n_rep, slen, head_dim
      )
  
file_path: BigDL/python/llm/src/bigdl/llm/transformers/models/mistral.py
imports: import math
  from typing import Optional, Tuple
  import torch
  from torch import nn
  import torch.nn.functional
  from bigdl.llm.utils.common import invalidInputError
  from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
  from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb, apply_rotary_pos_emb_no_cache_xpu
  from bigdl.llm.transformers.models.utils import is_enough_kv_cache_room_4_31, is_enough_kv_cache_room_4_36
  from bigdl.llm.transformers.low_bit_linear import SYM_INT4, FP8E5
  from bigdl.llm.transformers.models.utils import use_flash_attention
label_desc: Use image node_id 1 for calling a global function with example usage: repeat_kv(hidden_states, n_rep) and returns: hidden_states, hidden_states