code: def forward(self, hidden_states, cache, buffer, lora):
      # Self-attention
  
      if self.config.fused_attn and _rows(hidden_states) == 1:
          self.self_attn.fused(
              hidden_states, cache, buffer, self.input_layernorm, lora
          )
  
      else:
          residual = hidden_states
          hidden_states = self.input_layernorm.forward(
              hidden_states, buffer
          )
          hidden_states = self.self_attn.forward(
              hidden_states, cache, buffer, lora
          )
          hidden_states = residual + hidden_states
  
      # MLP
  
      if (
          self.config.fused_mlp_thd > 0
          and _rows(hidden_states) <= self.config.fused_mlp_thd
      ):
          self.mlp.fused(
              hidden_states, buffer, self.post_attention_layernorm, lora
          )
  
      else:
          residual = hidden_states
          hidden_states = self.post_attention_layernorm.forward(
              hidden_states, buffer
          )
          hidden_states = self.mlp.forward(hidden_states, buffer, lora)
          hidden_states = residual + hidden_states
  
      return hidden_states
  
imports: import sys
  import torch
  from torch import nn
  import torch.nn.functional
  from safetensors import safe_open
  import cuda_ext
  import json
  import math
  import gc
  from enum import Enum
label_desc: Use image node_id 2 for calling the ExLlamaDecoderLayer obj's underlying member method code with example usage: obj.forward(hidden_states, cache, buffer, lora) and returns: hidden_states