code: def test_numerical_stability(self) -> None:
      D = 4
      B = 3
      # multi-head attentions
      nhead = 4
      ntransformer_layers = 4
      keys = ["f1", "f2"]
      F = len(keys)
      torch.manual_seed(0)
      inter_arch = InteractionTransformerArch(
          num_sparse_features=F,
          embedding_dim=D,
          nhead=nhead,
          ntransformer_layers=ntransformer_layers,
      )
      dense_features = 10 * torch.rand(B, D)
      sparse_features = 10 * torch.rand(B, F, D)
      concat_dense = inter_arch(dense_features, sparse_features)
      expected = torch.LongTensor(
          [
              [0, 1, -1, 0, 0, 0, 0, -1, 0, 0, -1, 1],
              [0, 0, 0, 1, -1, 0, 1, 0, 0, 0, 1, 0],
              [-1, 0, 0, 0, 0, 0, -1, 1, 0, 1, -1, 0],
          ]
      )
      self.assertTrue(torch.equal(concat_dense.long(), expected))
  
imports: import unittest
  import torch
  from torchrec.datasets.utils import Batch
  from torchrec.models.dlrm import DLRMTrain
  from torchrec.models.experimental.transformerdlrm import DLRM_Transformer, InteractionTransformerArch
  from torchrec.modules.embedding_configs import EmbeddingBagConfig
  from torchrec.modules.embedding_modules import EmbeddingBagCollection
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 4 for calling the InteractionArchTransformerTest obj's underlying member method code with example usage: obj.test_numerical_stability() without return types