code: def test_dense_size(self) -> None:
      torch.manual_seed(0)
      D = 8
      dense_in_features = 100
  
      eb1_config = EmbeddingBagConfig(
          name="t1",
          embedding_dim=D,
          num_embeddings=100,
          feature_names=["f1", "f3"],
      )
      eb2_config = EmbeddingBagConfig(
          name="t2",
          embedding_dim=D,
          num_embeddings=100,
          feature_names=["f2"],
      )
  
      ebc = EmbeddingBagCollection(tables=[eb1_config, eb2_config])
      with self.assertRaises(ValueError):
          sparse_nn = DLRM_Projection(  # noqa
              embedding_bag_collection=ebc,
              dense_in_features=dense_in_features,
              dense_arch_layer_sizes=[20, D + 1],
              interaction_branch1_layer_sizes=[3 * D + D, 4 * D],
              interaction_branch2_layer_sizes=[3 * D + D, 4 * D],
              over_arch_layer_sizes=[5, 1],
          )
  
imports: import unittest
  import torch
  from torch import nn
  from torch.testing import FileCheck
  from torchrec.datasets.utils import Batch
  from torchrec.fx import symbolic_trace
  from torchrec.models.dlrm import choose, DenseArch, DLRM, DLRM_DCN, DLRM_Projection, DLRMTrain, InteractionArch, InteractionDCNArch, InteractionProjectionArch, SparseArch
  from torchrec.modules.crossnet import LowRankCrossNet
  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 DLRM_ProjectionTest obj's underlying member method code with example usage: obj.test_dense_size() without return types