code: def forward(self, hidden_states):
      hidden_gelu = self.act(self.wi_0(hidden_states))
      hidden_linear = self.wi_1(hidden_states)
      hidden_states = hidden_gelu * hidden_linear
      hidden_states = self.dropout(hidden_states)
  
      hidden_states = hidden_states.to(dtype=self.wo_cast[0])
      hidden_states = self.wo(hidden_states)
      # XXX: Recasting is already done within the layer norm.
      # Casting back to float16 here modifies results
      # hidden_states = hidden_states.to(dtype=self.wo_cast[1])
      return hidden_states
  
imports: import copy
  import math
  import warnings
  from typing import Optional, Tuple, Union
  from loguru import logger
  import torch
  import torch.distributed
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from transformers.activations import ACT2FN
  from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput
  from transformers.modeling_utils import PreTrainedModel
  from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
  from transformers.utils import is_torch_fx_proxy
  from transformers import T5Config
  from text_generation_server.utils.layers import TensorParallelColumnLinear, TensorParallelEmbedding, TensorParallelRowLinear, TensorParallelHead
label_desc: Use image node_id 2 for calling the T5DenseGatedActDense obj's underlying member method code with example usage: obj.forward(hidden_states) and returns: hidden_states