code: def forward(
      self,
      x: torch.Tensor,
      start_pos: int,
      freqs_cis: torch.Tensor,
      mask: Optional[torch.Tensor],
  ):
      bsz, seqlen, _ = x.shape
      xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
  
      xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
      xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
      xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
  
      self.cache_k = self.cache_k.to(xq)
      self.cache_v = self.cache_v.to(xq)
  
      self.cache_k[:bsz, start_pos : start_pos + seqlen] = xk
      self.cache_v[:bsz, start_pos : start_pos + seqlen] = xv
  
      keys = self.cache_k[:bsz, : start_pos + seqlen]
      values = self.cache_v[:bsz, : start_pos + seqlen]
  
      xq = apply_rotary_emb(xq, freqs_cis=freqs_cis[-seqlen:])
      keys = apply_rotary_emb(keys, freqs_cis=freqs_cis)
  
      # repeat k/v heads if n_kv_heads < n_heads
      keys = repeat_kv(
          keys, self.n_rep
      )  # (bs, seqlen, n_local_heads, head_dim)
      values = repeat_kv(
          values, self.n_rep
      )  # (bs, seqlen, n_local_heads, head_dim)
      # calling PT SDPA to enable using Flash Attention 2 and Xformer memory efficient kernels.
  
      output = torch.nn.functional.scaled_dot_product_attention(
          xq.transpose(1, 2),
          keys.transpose(1, 2),
          values.transpose(1, 2),
          attn_mask=mask,
          dropout_p=0.0,
          is_causal=False,
      )
  
      output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1)
      return self.wo(output)
  
imports: import json
  import logging
  import os
  import sys
  import time
  from dataclasses import asdict, dataclass
  from pathlib import Path
  from typing import Optional, Tuple, Union
  import torch
  import torch.distributed
  import torch.distributed.checkpoint
  import torch.nn.functional
  from checkpoint_converter import build_distributed_state_dict_from_consolidated
  from llama2_tokenizer import Tokenizer
  from torch import nn
  from torch.distributed._tensor import DeviceMesh, DTensor
  from torch.distributed._tensor.placement_types import Replicate
  from torch.distributed.fsdp import FullyShardedDataParallel
  from torch.distributed.tensor.parallel import ColwiseParallel, RowwiseParallel, parallelize_module
label_desc: Use image node_id 4 for calling the Attention obj's underlying member method code with example usage: obj.forward(x, start_pos, freqs_cis, mask) and returns: self