code: def test_fx_and_shape(self) -> None:
      D = 3
      eb1_config = EmbeddingBagConfig(
          name="t1",
          embedding_dim=D,
          num_embeddings=10,
          feature_names=["f1", "f3"],
      )
      eb2_config = EmbeddingBagConfig(
          name="t2",
          embedding_dim=D,
          num_embeddings=10,
          feature_names=["f2"],
      )
  
      ebc = EmbeddingBagCollection(tables=[eb1_config, eb2_config])
      sparse_arch = SparseArch(ebc)
      F = len(sparse_arch.sparse_feature_names)
      gm = symbolic_trace(sparse_arch)
  
      FileCheck().check("KeyedJaggedTensor").check("cat").run(gm.code)
  
      keys = ["f1", "f2", "f3", "f4", "f5"]
      offsets = torch.tensor([0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 19])
      features = KeyedJaggedTensor.from_offsets_sync(
          keys=keys,
          values=torch.tensor(
              [1, 2, 4, 5, 4, 3, 2, 9, 1, 2, 4, 5, 4, 3, 2, 9, 1, 2, 3]
          ),
          offsets=offsets,
      )
      B = (len(offsets) - 1) // len(keys)
  
      sparse_features = gm(features)
      self.assertEqual(sparse_features.shape, (B, F, D))
  
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 SparseArchTest obj's underlying member method code with example usage: obj.test_fx_and_shape() without return types