code: def forward(
      self,
      hidden_states,
      residual,
      cos,
      sin,
      cu_seqlen_prefill,
      kv_cache,
      block_tables,
      slots,
      input_lengths,
      max_s,
      prefill_cache_indices,
  ):
      normed_hidden_states, res = self.input_layernorm(
          hidden_states, residual
      )
  
      # Self Attention
      attn_output = self.self_attn(
          normed_hidden_states,
          cos,
          sin,
          cu_seqlen_prefill,
          kv_cache,
          block_tables,
          slots,
          input_lengths,
          max_s,
          prefill_cache_indices,
      )
  
      # faster post attention rms norm
      normed_attn_res_output, attn_res = self.post_attention_layernorm(
          attn_output, res
      )
  
      mlp_output = self.mlp(normed_attn_res_output)
  
      return mlp_output, attn_res
  
imports: import torch
  import torch.distributed
  from torch import nn
  from transformers.activations import ACT2FN
  from transformers.configuration_utils import PretrainedConfig
  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, TensorParallelEmbedding, PositionRotaryEmbedding, TensorParallelHead, get_linear, FastRMSNorm
label_desc: Use image node_id 2 for calling the MistralLayer obj's underlying member method code with example usage: obj.forward(hidden_states, residual, cos, sin, cu_seqlen_prefill, kv_cache, block_tables, slots, input_lengths, max_s, prefill_cache_indices) and returns: mlp_output,  attn_res