code: def share_cross_attention(self, model):
      for i in range(6):
          layer_num = 6 + i * 2
          modules_0 = model.layer[
              layer_num
          ].crossattention.self._modules
          modules_1 = model.layer[
              layer_num + 1
          ].crossattention.self._modules
  
          for name in modules_0.keys():
              if "key" in name or "value" in name:
                  module_0 = modules_0[name]
                  module_1 = modules_1[name]
                  if hasattr(module_0, "weight"):
                      module_0.weight = module_1.weight
                      if hasattr(module_0, "bias"):
                          module_0.bias = module_1.bias
  
imports: from copy import deepcopy
  import torch
  import torch.nn.functional
  from lavis.common.registry import registry
  from lavis.common.utils import get_abs_path
  from lavis.models.albef_models import AlbefBase
  from lavis.models.albef_models.albef_outputs import AlbefIntermediateOutput, AlbefOutput
  from lavis.models.base_model import MomentumDistilationMixin
  from lavis.models.med import BertModel
  from lavis.models.vit import VisionTransformerEncoder
  from torch import nn
  from transformers import BertConfig
label_desc: Use image node_id 4 for calling the AlbefNLVR obj's underlying member method code with example usage: obj.share_cross_attention(model) without return types