code: def forward(self, hidden_states):
      gate_up_states = self.gate_up_proj(hidden_states)
      gate_up_states = gate_up_states.view(
          -1, 2, self.intermediate_size
      )
      return self.down_proj(
          self.act(gate_up_states[:, 0]) * gate_up_states[:, 1]
      )
  
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 LlamaMLP obj's underlying member method code with example usage: obj.forward(hidden_states) and returns: self