code: def forward(
      self,
      hidden_states,
      residual,
      cu_seqlen_prefill,
      kv_cache,
      block_tables,
      slots,
      input_lengths,
      max_s,
  ):
      hidden_states, residual = self.ln_1(hidden_states, residual)
      hidden_states = self.attn(
          hidden_states,
          cu_seqlen_prefill,
          kv_cache,
          block_tables,
          slots,
          input_lengths,
          max_s,
      )
  
      hidden_states, residual = self.ln_2(hidden_states, residual)
  
      mlp_output = self.mlp(hidden_states)
  
      return mlp_output, residual
  
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 Block obj's underlying member method code with example usage: obj.forward(hidden_states, residual, cu_seqlen_prefill, kv_cache, block_tables, slots, input_lengths, max_s) and returns: mlp_output,  residual