code: def forward(
      self,
      input_ids=None,
      attention_mask=None,
      encoder_hidden_states=None,
      encoder_attention_mask=None,
      inputs_embeds=None,
      head_mask=None,
      cross_attn_head_mask=None,
      past_key_values=None,
      use_cache=None,
      output_attentions=None,
      output_hidden_states=None,
      return_dict=None,
  ):
      # Model parallel
      if self.model_parallel:
          torch.cuda.set_device(self.first_device)
          self.embed_tokens = self.embed_tokens.to(self.first_device)
      use_cache = (
          use_cache if use_cache is not None else self.config.use_cache
      )
      output_attentions = (
          output_attentions
          if output_attentions is not None
          else self.config.output_attentions
      )
      output_hidden_states = (
          output_hidden_states
          if output_hidden_states is not None
          else self.config.output_hidden_states
      )
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
      if self.is_decoder and encoder_hidden_states is not None:
          (
              input_ids,
              encoder_attention_mask,
          ) = adjust_tensors_for_parallel(
              encoder_hidden_states, input_ids, encoder_attention_mask
          )
  
      if input_ids is not None and inputs_embeds is not None:
          err_msg_prefix = "decoder_" if self.is_decoder else ""
          raise ValueError(
              f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time"
          )
      elif input_ids is not None:
          input_shape = input_ids.size()
          input_ids = input_ids.view(-1, input_shape[-1])
      elif inputs_embeds is not None:
          input_shape = inputs_embeds.size()[:-1]
      else:
          err_msg_prefix = "decoder_" if self.is_decoder else ""
          raise ValueError(
              f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds"
          )
  
      if inputs_embeds is None:
          if self.embed_tokens is None:
              raise ValueError(
                  "You have to initialize the model with valid token embeddings"
              )
          inputs_embeds = self.embed_tokens(input_ids)
  
      batch_size, seq_length = input_shape
  
      # required mask seq length can be calculated via length of past
      mask_seq_length = (
          past_key_values[0][0].shape[2] + seq_length
          if past_key_values is not None
          else seq_length
      )
  
      if use_cache is True:
          if not self.is_decoder:
              raise ValueError(
                  f"`use_cache` can only be set to `True` if {self} is used as a decoder"
              )
  
      if attention_mask is None:
          attention_mask = torch.ones(
              batch_size, mask_seq_length, device=inputs_embeds.device
          )
      if (
          self.is_decoder
          and encoder_attention_mask is None
          and encoder_hidden_states is not None
      ):
          encoder_seq_length = encoder_hidden_states.shape[1]
          encoder_attention_mask = torch.ones(
              batch_size,
              encoder_seq_length,
              device=inputs_embeds.device,
              dtype=torch.long,
          )
  
      # initialize past_key_values with `None` if past does not exist
      if past_key_values is None:
          past_key_values = [None] * len(self.block)
  
      # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
      # ourselves in which case we just need to make it broadcastable to all heads.
      extended_attention_mask = self.get_extended_attention_mask(
          attention_mask, input_shape
      )
  
      # If a 2D or 3D attention mask is provided for the cross-attention
      # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
      if self.is_decoder and encoder_hidden_states is not None:
          (
              encoder_batch_size,
              encoder_sequence_length,
              _,
          ) = encoder_hidden_states.size()
          encoder_hidden_shape = (
              encoder_batch_size,
              encoder_sequence_length,
          )
          if encoder_attention_mask is None:
              encoder_attention_mask = torch.ones(
                  encoder_hidden_shape, device=inputs_embeds.device
              )
          encoder_extended_attention_mask = self.invert_attention_mask(
              encoder_attention_mask
          )
      else:
          encoder_extended_attention_mask = None
  
      if self.gradient_checkpointing and self.training:
          if use_cache:
              logger.warning_once(
                  "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
              )
              use_cache = False
  
      # Prepare head mask if needed
      head_mask = self.get_head_mask(head_mask, self.config.num_layers)
      cross_attn_head_mask = self.get_head_mask(
          cross_attn_head_mask, self.config.num_layers
      )
      present_key_value_states = () if use_cache else None
      all_hidden_states = () if output_hidden_states else None
      all_attentions = () if output_attentions else None
      all_cross_attentions = (
          () if (output_attentions and self.is_decoder) else None
      )
      position_bias = None
      encoder_decoder_position_bias = None
  
      hidden_states = self.dropout(inputs_embeds)
      if not self.is_decoder:
          hidden_states = self.post_embedding_forward(hidden_states)
  
      for i, (layer_module, past_key_value) in enumerate(
          zip(self.block, past_key_values)
      ):
          layer_head_mask = head_mask[i]
          cross_attn_layer_head_mask = cross_attn_head_mask[i]
          # Model parallel
          if self.model_parallel:
              torch.cuda.set_device(hidden_states.device)
              # Ensure that attention_mask is always on the same device as hidden_states
              if attention_mask is not None:
                  attention_mask = attention_mask.to(
                      hidden_states.device
                  )
              if position_bias is not None:
                  position_bias = position_bias.to(hidden_states.device)
              if encoder_hidden_states is not None:
                  encoder_hidden_states = encoder_hidden_states.to(
                      hidden_states.device
                  )
              if encoder_extended_attention_mask is not None:
                  encoder_extended_attention_mask = (
                      encoder_extended_attention_mask.to(
                          hidden_states.device
                      )
                  )
              if encoder_decoder_position_bias is not None:
                  encoder_decoder_position_bias = (
                      encoder_decoder_position_bias.to(
                          hidden_states.device
                      )
                  )
              if layer_head_mask is not None:
                  layer_head_mask = layer_head_mask.to(
                      hidden_states.device
                  )
              if cross_attn_layer_head_mask is not None:
                  cross_attn_layer_head_mask = (
                      cross_attn_layer_head_mask.to(
                          hidden_states.device
                      )
                  )
          if output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_states,)
  
          if self.gradient_checkpointing and self.training:
  
              def create_custom_forward(module):
                  def custom_forward(*inputs):
                      return tuple(
                          module(*inputs, use_cache, output_attentions)
                      )
  
                  return custom_forward
  
              layer_outputs = checkpoint(
                  create_custom_forward(layer_module),
                  hidden_states,
                  extended_attention_mask,
                  position_bias,
                  encoder_hidden_states,
                  encoder_extended_attention_mask,
                  encoder_decoder_position_bias,
                  layer_head_mask,
                  cross_attn_layer_head_mask,
                  None,  # past_key_value is always None with gradient checkpointing
              )
          else:
              layer_outputs = layer_module(
                  hidden_states,
                  attention_mask=extended_attention_mask,
                  position_bias=position_bias,
                  encoder_hidden_states=encoder_hidden_states,
                  encoder_attention_mask=encoder_extended_attention_mask,
                  encoder_decoder_position_bias=encoder_decoder_position_bias,
                  layer_head_mask=layer_head_mask,
                  cross_attn_layer_head_mask=cross_attn_layer_head_mask,
                  past_key_value=past_key_value,
                  use_cache=use_cache,
                  output_attentions=output_attentions,
              )
  
          # layer_outputs is a tuple with:
          # hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
          if use_cache is False:
              layer_outputs = (
                  layer_outputs[:1] + (None,) + layer_outputs[1:]
              )
  
          hidden_states, present_key_value_state = layer_outputs[:2]
  
          (
              attention_mask,
              extended_attention_mask,
          ) = adjust_tensors_for_parallel(
              hidden_states, attention_mask, extended_attention_mask
          )
  
          # We share the position biases between the layers - the first layer store them
          # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights),
          # (cross-attention position bias), (cross-attention weights)
          position_bias = layer_outputs[2]
          if self.is_decoder and encoder_hidden_states is not None:
              encoder_decoder_position_bias = layer_outputs[
                  4 if output_attentions else 3
              ]
          # append next layer key value states
          if use_cache:
              present_key_value_states = present_key_value_states + (
                  present_key_value_state,
              )
  
          if position_bias is not None:
              position_bias = adjust_tensors_for_parallel(
                  hidden_states, position_bias
              )[0]
          if encoder_decoder_position_bias is not None:
              encoder_decoder_position_bias = (
                  adjust_tensors_for_parallel(
                      hidden_states, encoder_decoder_position_bias
                  )[0]
              )
  
          if output_attentions:
              all_attentions = all_attentions + (layer_outputs[3],)
              if self.is_decoder:
                  all_cross_attentions = all_cross_attentions + (
                      layer_outputs[5],
                  )
  
          # Model Parallel: If it's the last layer for that device, put things on the next device
          if self.model_parallel:
              for k, v in self.device_map.items():
                  if (
                      i == v[-1]
                      and "cuda:" + str(k) != self.last_device
                  ):
                      hidden_states = hidden_states.to(
                          "cuda:" + str(k + 1)
                      )
  
      hidden_states = self.final_layer_norm(hidden_states)
      hidden_states = self.dropout(hidden_states)
  
      # Add last layer
      if output_hidden_states:
          all_hidden_states = all_hidden_states + (hidden_states,)
  
      if not return_dict:
          return tuple(
              v
              for v in [
                  hidden_states,
                  present_key_value_states,
                  all_hidden_states,
                  all_attentions,
                  all_cross_attentions,
              ]
              if v is not None
          )
      return BaseModelOutputWithPastAndCrossAttentions(
          last_hidden_state=hidden_states,
          past_key_values=present_key_value_states,
          hidden_states=all_hidden_states,
          attentions=all_attentions,
          cross_attentions=all_cross_attentions,
      )
  
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 T5StackWithAdapters obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, encoder_hidden_states, encoder_attention_mask, inputs_embeds, head_mask, cross_attn_head_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict) and returns: BaseModelOutputWithPastAndCrossAttentions, tuple, custom_forward, tuple