code: def forward(
      self,
      input_ids: torch.Tensor,
      position_ids: torch.Tensor,
      cu_seqlen_prefill: Optional[torch.Tensor],
      kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
      block_tables: torch.Tensor,
      slots: torch.Tensor,
      input_lengths: torch.Tensor,
      max_s: int,
  ) -> torch.Tensor:
      hidden_states = self.embed_in(input_ids)
  
      # Get rotary cos and sin for this forward
      # Avoid to index in each layer
      cos, sin = self.layers[0].attention.rotary_emb.get_cos_sin(
          position_ids, max_s, hidden_states.dtype
      )
  
      residual = None
      for i, layer in enumerate(self.layers):
          hidden_states, residual = layer(
              hidden_states,
              residual,
              cos,
              sin,
              cu_seqlen_prefill,
              kv_cache[i],
              block_tables,
              slots,
              input_lengths,
              max_s,
          )
  
      hidden_states, _ = self.final_layer_norm(hidden_states, residual)
  
      return hidden_states
  
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 FlashGPTNeoXModel obj's underlying member method code with example usage: obj.forward(input_ids, position_ids, cu_seqlen_prefill, kv_cache, block_tables, slots, input_lengths, max_s) and returns: hidden_states