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,
      lm_head_indices: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      hidden_states = self.transformer(
          input_ids,
          position_ids,
          cu_seqlen_prefill,
          kv_cache,
          block_tables,
          slots,
          input_lengths,
          max_s,
      )
      if lm_head_indices is not None:
          hidden_states = hidden_states[lm_head_indices]
      logits = self.lm_head(hidden_states)
      return logits
  
imports: import torch
  import torch.distributed
  from torch import nn
  from transformers.activations import ACT2FN
  from typing import Optional, List, Tuple
  from text_generation_server.utils import paged_attention, flash_attn
  from text_generation_server.utils.layers import TensorParallelRowLinear, TensorParallelColumnLinear, TensorParallelHead, TensorParallelEmbedding, FastLayerNorm, get_linear
label_desc: Use image node_id 2 for calling the FlashSantacoderForCausalLM 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, lm_head_indices) and returns: logits