code: def forward(
      self,
      hidden_states: Optional[torch.FloatTensor],
      layer_past: Optional[Tuple[torch.Tensor]] = None,
      attention_mask: Optional[torch.FloatTensor] = None,
      position_ids: Optional[torch.LongTensor] = None,
      head_mask: Optional[torch.FloatTensor] = None,
      use_cache: Optional[bool] = False,
      output_attentions: Optional[bool] = False,
  ) -> Union[
      Tuple[torch.Tensor],
      Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]],
  ]:
      adjust_tensors_for_parallel_(hidden_states, attention_mask)
      residual = hidden_states
      hidden_states = self.ln_1(hidden_states)
      attn_outputs = self.attn(
          hidden_states,
          layer_past=layer_past,
          attention_mask=attention_mask,
          position_ids=position_ids,
          head_mask=head_mask,
          use_cache=use_cache,
          output_attentions=output_attentions,
      )
      attn_output = attn_outputs[
          0
      ]  # output_attn: a, present, (attentions)
      outputs = attn_outputs[1:]
  
      feed_forward_hidden_states = self.mlp(hidden_states)
      # See https://github.com/adapter-hub/adapters/pull/426#discussion_r994450898
      hidden_states = self.attention_adapters(
          attn_output, residual, None
      )
  
      hidden_states = self.output_adapters(
          feed_forward_hidden_states, hidden_states, None
      )
  
      if use_cache:
          outputs = (hidden_states,) + outputs
      else:
          outputs = (hidden_states,) + outputs[1:]
  
      return outputs
  
file_path: adapters/src/adapters/models/gptj/modeling_gptj.py
imports: from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from transformers.models.gptj.modeling_gptj import GPTJAttention, GPTJBlock, apply_rotary_pos_emb, get_embed_positions
  from transformers.utils.import_utils import is_torch_fx_proxy
  from ...composition import adjust_tensors_for_parallel, adjust_tensors_for_parallel_, match_attn_matrices_for_parallel
  from .mixin_gptj import GPTJAttentionAdaptersMixin, GPTJDecoderBlockAdaptersMixin
label_desc: Use image node_id 1 for calling the GPTJBlockWithAdapters obj's underlying member method code with example usage: obj.forward(hidden_states, layer_past, attention_mask, position_ids, head_mask, use_cache, output_attentions) and returns: outputs