code: def forward(
      self, hidden_states: Optional[Tuple[torch.FloatTensor]]
  ) -> torch.FloatTensor:
      hidden_states = self.c_fc(hidden_states)
      hidden_states = self.act(hidden_states)
      hidden_states = self.c_proj(hidden_states)
      hidden_states = self.dropout(hidden_states)
      return hidden_states
  
imports: import math
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_attn_mask_utils import AttentionMaskConverter
  from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast, TokenClassifierOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10, logging
  from .configuration_gpt_bigcode import GPTBigCodeConfig
label_desc: Use image node_id 2 for calling the GPTBigCodeMLP obj's underlying member method code with example usage: obj.forward(hidden_states) and returns: hidden_states