code: def repeat_kv(
      self, hidden_states: torch.Tensor, n_rep: int
  ) -> torch.Tensor:
      # TODO: This seems inefficient. It should be possible to broadcast in the attention matmul to avoid building
      # temporary K/V tensors like this
  
      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
      )
  
imports: import sys
  import torch
  from torch import nn
  import torch.nn.functional
  from safetensors import safe_open
  import cuda_ext
  import json
  import math
  import gc
  from enum import Enum
label_desc: Use image node_id 2 for calling the ExLlamaAttention obj's underlying member method code with example usage: obj.repeat_kv(hidden_states, n_rep) and returns: hidden_states, hidden_states