code: def test_attention_matches_huggingface(
      attention_params, module_name, hf_module
  ):
      hidden_states = torch.randn(2, 3, 6)
      attention_mask = torch.tensor([[0, 1, 0], [1, 1, 0]])
  
      attention = AttentionLayer.from_params(attention_params).eval()
      state_dict = attention._get_mapped_state_dict(
          hf_module.state_dict()
      )
      attention.load_state_dict(state_dict)
  
      torch.manual_seed(1234)
      output = attention(hidden_states, attention_mask=attention_mask)
      # We do this because bert, roberta, electra process the attention_mask at the model level.
      attention_mask_hf = (attention_mask == 0).view(
          (2, 1, 1, 3)
      ).expand(2, 2, 3, 3) * -10e5
  
      torch.manual_seed(1234)
      hf_output = hf_module(
          hidden_states, attention_mask=attention_mask_hf
      )
  
      assert torch.allclose(output.hidden_states, hf_output[0])
  
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 5 for calling a global function with example usage: test_attention_matches_huggingface(attention_params, module_name, hf_module) without return types