code: def test_forward_against_huggingface_output(params_dict):
      hidden_states = torch.randn(2, 3, 6)
      attention_mask = torch.tensor([[0, 1, 0], [1, 1, 0]])
  
      hf_kwargs = {
          "d_model": params_dict["hidden_size"],
          "d_kv": params_dict["key_value_proj_dim"],
          "num_heads": params_dict["num_heads"],
          "relative_attention_num_buckets": params_dict[
              "relative_attention_num_buckets"
          ],
          "dropout_rate": params_dict["dropout"],
      }
  
      torch.manual_seed(1234)
      hf_module = HFT5Attention(
          T5Config(**hf_kwargs), has_relative_attention_bias=False
      )
  
      torch.manual_seed(1234)
  
      params = copy.deepcopy(params_dict)
      params[
          "normalize"
      ] = False  # only for this test, as HF does not normalize.
      t5_attention = T5Attention(**params)
  
      # setting to eval mode to avoid non-deterministic dropout.
      t5_attention = t5_attention.eval()
      hf_module = hf_module.eval()
  
      output = t5_attention.forward(hidden_states, mask=attention_mask)
      attention_mask_hf = (attention_mask == 0).view(
          (2, 1, 1, 3)
      ).expand(2, 2, 3, 3) * min_value_of_dtype(hidden_states.dtype)
      hf_output = hf_module.forward(
          hidden_states, mask=attention_mask_hf
      )
  
      hs = output.hidden_states
  
      assert torch.allclose(hs, hf_output[0])
  
file_path: allennlp/tests/modules/transformer/t5_self_attention_test.py
imports: import copy
  import torch
  import pytest
  from transformers import AutoModel
  from allennlp.common import Params
  from allennlp.modules.transformer.attention_module import T5Attention
  from transformers.models.t5.configuration_t5 import T5Config
  from transformers.models.t5.modeling_t5 import T5Attention
  from allennlp.nn.util import min_value_of_dtype
label_desc: Use image node_id 5 for calling a global function with example usage: test_forward_against_huggingface_output(params_dict) without return types