code: def test_larger(self) -> None:
      D = 16
      B = 20
      # multi-head attentions
      nhead = 8
      ntransformer_layers = 4
      keys = ["f1", "f2", "f3", "f4"]
      F = len(keys)
      inter_arch = InteractionTransformerArch(
          num_sparse_features=F,
          embedding_dim=D,
          nhead=nhead,
          ntransformer_layers=ntransformer_layers,
      )
      dense_features = torch.rand((B, D))
      sparse_features = torch.rand((B, F, D))
      concat_dense = inter_arch(dense_features, sparse_features)
      self.assertEqual(concat_dense.size(), (B, D * (F + 1)))
  
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 2 for calling the InteractionArchTransformerTest obj's underlying member method code with example usage: obj.test_larger() without return types