code: def forward(
      self,
      hidden_states,
      key_value_states,
      attention_mask=None,
      position_bias=None,
      layer_head_mask=None,
      past_key_value=None,
      use_cache=False,
      query_length=None,
      output_attentions=False,
  ):
      normed_hidden_states = self.layer_norm(hidden_states)
      attention_output = self.EncDecAttention(
          normed_hidden_states,
          mask=attention_mask,
          key_value_states=key_value_states,
          position_bias=position_bias,
          layer_head_mask=layer_head_mask,
          past_key_value=past_key_value,
          use_cache=use_cache,
          query_length=query_length,
          output_attentions=output_attentions,
      )
      layer_output = self.bottleneck_layer_forward(
          hidden_states=self.dropout(attention_output[0]),
          residual_input=hidden_states,
          layer_norm=None,
      )
      outputs = (layer_output,) + attention_output[
          1:
      ]  # add attentions if we output them
      return outputs
  
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 T5LayerCrossAttentionWithAdapters obj's underlying member method code with example usage: obj.forward(hidden_states, key_value_states, attention_mask, position_bias, layer_head_mask, past_key_value, use_cache, query_length, output_attentions) and returns: outputs