code: def test_layer_with_cross_attention(layer_params):
      layer_params["add_cross_attention"] = True
  
      transformer_layer = TransformerLayer.from_params(
          layer_params
      ).eval()
      assert hasattr(transformer_layer, "cross_attention")
  
      attention_mask = torch.tensor([[0, 1, 0], [1, 1, 0]])
      transformer_layer(
          torch.randn(2, 3, 6),
          attention_mask=attention_mask,
          encoder_hidden_states=torch.randn(2, 3, 6),
      )
  
imports: import copy
  import torch
  import pytest
  from transformers.models.bert.configuration_bert import BertConfig
  from transformers.models.bert.modeling_bert import BertAttention, BertLayer
  from transformers.models.roberta.configuration_roberta import RobertaConfig
  from transformers.models.roberta.modeling_roberta import RobertaAttention, RobertaLayer
  from transformers.models.electra.configuration_electra import ElectraConfig
  from transformers.models.electra.modeling_electra import ElectraAttention, ElectraLayer
  from allennlp.common import Params, cached_transformers
  from allennlp.common.testing import run_distributed_test
  from allennlp.modules.transformer import AttentionLayer, TransformerLayer
label_desc: Use image node_id 9 for calling a global function with example usage: test_layer_with_cross_attention(layer_params) without return types