code: def forward(self, hidden_states):
      forwarded_states = self.layer_norm(hidden_states)
      forwarded_states = self.DenseReluDense(forwarded_states)
      hidden_states = self.bottleneck_layer_forward(
          hidden_states=self.dropout(forwarded_states),
          residual_input=hidden_states,
          layer_norm=None,
      )
      return hidden_states
  
imports: import torch
  from torch import nn
  from torch.utils.checkpoint import checkpoint
  from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions
  from transformers.models.t5.modeling_t5 import T5Attention, T5LayerCrossAttention, T5LayerFF, T5LayerSelfAttention, T5Stack
  from transformers.utils import logging
  from ...composition import adjust_tensors_for_parallel, match_attn_matrices_for_parallel
  from .mixin_t5 import T5AttentionAdaptersMixin, T5CrossAttentionLayerAdaptersMixin, T5FFLayerAdaptersMixin, T5SelfAttentionLayerAdaptersMixin, T5StackAdaptersMixin
label_desc: Use image node_id 1 for calling the T5LayerFFWithAdapters obj's underlying member method code with example usage: obj.forward(hidden_states) and returns: hidden_states