code: def forward(
      self,
      hidden_states,
      cos,
      sin,
      cu_seqlen_prefill,
      kv_cache,
      block_tables,
      slots,
      input_lengths,
      max_s,
  ):
      qkv = self.query_key_value(hidden_states)
      qkv = qkv.view(-1, 3, self.num_heads, self.head_size)
  
      # Inplace rotary
      self.rotary_emb(qkv[:, 0], qkv[:, 1], cos, sin)
  
      paged_attention.reshape_and_cache(
          qkv[:, 1], qkv[:, 2], kv_cache[0], kv_cache[1], slots
      )
  
      # output tensor
      attn_output = torch.empty_like(qkv[:, 0])
  
      # Prefill
      if cu_seqlen_prefill is not None:
          # flash attention
          flash_attn.attention(
              qkv[:, 0],
              qkv[:, 1],
              qkv[:, 2],
              attn_output,
              cu_seqlen_prefill,
              max_s,
              self.softmax_scale,
          )
      # Decode
      else:
          paged_attention.attention(
              attn_output,
              qkv[:, 0],
              kv_cache[0],
              kv_cache[1],
              self.kv_head_mapping,
              self.softmax_scale,
              block_tables,
              input_lengths,
              max_s,
          )
  
      return self.dense(
          attn_output.view(-1, self.num_heads * self.head_size)
      )
  
imports: import torch
  import torch.distributed
  from torch import nn
  from transformers.activations import ACT2FN
  from transformers.modeling_utils import PreTrainedModel
  from transformers.models.gpt_neox import GPTNeoXConfig
  from typing import Optional, List, Tuple
  from text_generation_server.utils import paged_attention, flash_attn
  from text_generation_server.utils.flash_attn import attention
  from text_generation_server.utils.layers import TensorParallelRowLinear, TensorParallelColumnLinear, TensorParallelEmbedding, TensorParallelHead, FastLayerNorm, PositionRotaryEmbedding, get_linear
label_desc: Use image node_id 2 for calling the FlashNeoxAttention obj's underlying member method code with example usage: obj.forward(hidden_states, cos, sin, cu_seqlen_prefill, kv_cache, block_tables, slots, input_lengths, max_s) and returns: self