code: def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
      """torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
      bs, slen, n_kv_heads, head_dim = x.shape
      if n_rep == 1:
          return x
      return (
          x[:, :, :, None, :]
          .expand(bs, slen, n_kv_heads, n_rep, head_dim)
          .reshape(bs, slen, n_kv_heads * n_rep, head_dim)
      )
  
imports: import math
  from dataclasses import dataclass
  from typing import Optional, Tuple
  import fairscale.nn.model_parallel.initialize
  import torch
  import torch.nn.functional
  from fairscale.nn.model_parallel.layers import ColumnParallelLinear, ParallelEmbedding, RowParallelLinear
  from torch import nn
label_desc: Use image node_id 4 for calling a global function with example usage: repeat_kv(x, n_rep) and returns: x, x