code: def forward(
      self,
      hidden_states,
      layer_past=None,
      attention_mask=None,
      head_mask=None,
      encoder_hidden_states=None,
      encoder_attention_mask=None,
      use_cache=False,
      output_attentions=False,
  ):
      attn_outputs = self.attn(
          self.ln_1(hidden_states),
          layer_past=layer_past,
          attention_mask=attention_mask,
          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:]
      # residual connection
      hidden_states = attn_output + hidden_states
  
      if encoder_hidden_states is not None:
          # add one self-attention block for cross-attention
          assert hasattr(
              self, "crossattention"
          ), f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`"
          cross_attn_outputs = self.crossattention(
              self.ln_cross_attn(hidden_states),
              attention_mask=attention_mask,
              head_mask=head_mask,
              encoder_hidden_states=encoder_hidden_states,
              encoder_attention_mask=encoder_attention_mask,
              output_attentions=output_attentions,
          )
          attn_output = cross_attn_outputs[0]
          # residual connection
          hidden_states = hidden_states + attn_output
          outputs = (
              outputs + cross_attn_outputs[2:]
          )  # add cross attentions if we output attention weights
  
      feed_forward_hidden_states = self.mlp(self.ln_2(hidden_states))
      # residual connection
      hidden_states = hidden_states + feed_forward_hidden_states
      # hidden_states = hidden_states + self.adapter_ln(self.adapter_mlp(hidden_states))
  
      outputs = [hidden_states] + outputs
      return outputs
  
imports: import os
  from dataclasses import dataclass
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from transformers.activations import ACT2FN
  from transformers.file_utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions
  from transformers.modeling_utils import Conv1D, PreTrainedModel, SequenceSummary, find_pruneable_heads_and_indices, prune_conv1d_layer
  from transformers.utils import logging
  from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
  from transformers.models.gpt2.configuration_gpt2 import GPT2Config
label_desc: Use image node_id 2 for calling the Block obj's underlying member method code with example usage: obj.forward(hidden_states, layer_past, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions) and returns: outputs