code: def __init__(self, config, layer_idx):
      super().__init__()
      self.chunk_size_feed_forward = config.chunk_size_feed_forward
      self.seq_len_dim = 1
      self.attention = Blip2QFormerAttention(config)
  
      self.layer_idx = layer_idx
  
      if layer_idx % config.cross_attention_frequency == 0:
          self.crossattention = Blip2QFormerAttention(
              config, is_cross_attention=True
          )
          self.has_cross_attention = True
      else:
          self.has_cross_attention = False
  
      self.intermediate = Blip2QFormerIntermediate(config)
      self.intermediate_query = Blip2QFormerIntermediate(config)
      self.output_query = Blip2QFormerOutput(config)
      self.output = Blip2QFormerOutput(config)
  
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 1 to create a new Blip2QFormerLayer object from inherited base classes: nn with example: obj = Blip2QFormerLayer(config, layer_idx)