code: def forward(
      self,
      hidden_states,
      attention_mask=None,
      head_mask=None,
      encoder_hidden_states=None,
      encoder_attention_mask=None,
      past_key_values=None,
      use_cache=None,
      output_attentions=False,
      output_hidden_states=False,
      return_dict=True,
      query_length=0,
  ):
      all_hidden_states = () if output_hidden_states else None
      all_self_attentions = () if output_attentions else None
      all_cross_attentions = () if output_attentions else None
  
      next_decoder_cache = () if use_cache else None
  
      for i in range(self.config.num_hidden_layers):
          layer_module = self.layer[i]
          if output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_states,)
  
          layer_head_mask = (
              head_mask[i] if head_mask is not None else None
          )
          past_key_value = (
              past_key_values[i]
              if past_key_values is not None
              else None
          )
  
          if (
              getattr(self.config, "gradient_checkpointing", False)
              and self.training
          ):
              if use_cache:
                  logger.warning(
                      "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                  )
                  use_cache = False
  
              def create_custom_forward(module):
                  def custom_forward(*inputs):
                      return module(
                          *inputs,
                          past_key_value,
                          output_attentions,
                          query_length,
                      )
  
                  return custom_forward
  
              layer_outputs = torch.utils.checkpoint.checkpoint(
                  create_custom_forward(layer_module),
                  hidden_states,
                  attention_mask,
                  layer_head_mask,
                  encoder_hidden_states,
                  encoder_attention_mask,
              )
          else:
              layer_outputs = layer_module(
                  hidden_states,
                  attention_mask,
                  layer_head_mask,
                  encoder_hidden_states,
                  encoder_attention_mask,
                  past_key_value,
                  output_attentions,
                  query_length,
              )
  
          hidden_states = layer_outputs[0]
          if use_cache:
              next_decoder_cache += (layer_outputs[-1],)
          if output_attentions:
              all_self_attentions = all_self_attentions + (
                  layer_outputs[1],
              )
              if layer_module.has_cross_attention:
                  all_cross_attentions = all_cross_attentions + (
                      layer_outputs[2],
                  )
  
      if output_hidden_states:
          all_hidden_states = all_hidden_states + (hidden_states,)
  
      if not return_dict:
          return tuple(
              v
              for v in [
                  hidden_states,
                  next_decoder_cache,
                  all_hidden_states,
                  all_self_attentions,
                  all_cross_attentions,
              ]
              if v is not None
          )
      return BaseModelOutputWithPastAndCrossAttentions(
          last_hidden_state=hidden_states,
          past_key_values=next_decoder_cache,
          hidden_states=all_hidden_states,
          attentions=all_self_attentions,
          cross_attentions=all_cross_attentions,
      )
  
imports: from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from transformers import BertTokenizer
  from transformers.activations import QuickGELUActivation
  from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPooling, BaseModelOutputWithPoolingAndCrossAttentions
  from transformers.models.blip_2.configuration_blip_2 import Blip2Config, Blip2VisionConfig
  from transformers.models.blip_2.modeling_blip_2 import Blip2Encoder, Blip2PreTrainedModel, Blip2QFormerAttention, Blip2QFormerIntermediate, Blip2QFormerOutput
  from transformers.pytorch_utils import apply_chunking_to_forward
  from transformers.utils import logging, replace_return_docstrings
label_desc: Use image node_id 2 for calling the Blip2QFormerEncoder obj's underlying member method code with example usage: obj.forward(hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict, query_length) and returns: BaseModelOutputWithPastAndCrossAttentions, tuple, custom_forward, module