code: def forward(self, hidden_states):
      hidden_states = self.fc_in(hidden_states)
      hidden_states = self.act(hidden_states)
      hidden_states = self.fc_out(hidden_states)
      hidden_states = self.dropout(hidden_states)
      return hidden_states
  
imports: from typing import Tuple
  import numpy
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from transformers.activations import ACT2FN
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from transformers.modeling_utils import PreTrainedModel
  from transformers.utils import logging
  from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
  from .configuration_codegen import CodeGenConfig
label_desc: Use image node_id 2 for calling the CodeGenMLP obj's underlying member method code with example usage: obj.forward(hidden_states) and returns: hidden_states