code: def test_sequence_2_table_perf(self) -> None:
      tables = [
          EmbeddingConfig(
              num_embeddings=128,
              embedding_dim=32,
              name="table_0",
              feature_names=["feature_0"],
          ),
          EmbeddingConfig(
              num_embeddings=256,
              embedding_dim=32,
              name="table_1",
              feature_names=["feature_1"],
          ),
      ]
      model = TestSequenceSparseNN(tables=tables)
      sharding_options = self.enumerator.enumerate(
          module=model,
          sharders=[
              cast(
                  ModuleSharder[torch.nn.Module],
                  EmbeddingCollectionSharder(),
              )
          ],
      )
  
      expected_total_perfs = {
          ("dense", "data_parallel"): [
              0.0026901057997143255,
              0.0026901057997143255,
          ],
          ("fused", "table_wise"): [0.001880471390093715],
          ("fused_uvm", "table_wise"): [0.25958192114736517],
          ("fused_uvm_caching", "table_wise"): [0.06043381305524807],
          ("fused", "column_wise"): [0.001880471390093715],
          ("fused_uvm", "column_wise"): [0.25958192114736517],
          ("fused_uvm_caching", "column_wise"): [0.06043381305524807],
          ("fused", "row_wise"): [
              0.0007915177871551004,
              0.0007915177871551004,
          ],
          ("fused_uvm", "row_wise"): [
              0.10363410500919118,
              0.10363410500919118,
          ],
          ("fused_uvm_caching", "row_wise"): [
              0.024158779217047004,
              0.024158779217047004,
          ],
      }
  
      total_perfs = {
          (
              sharding_option.compute_kernel,
              sharding_option.sharding_type,
          ): [
              cast(Perf, shard.perf).total
              for shard in sharding_option.shards
          ]
          for sharding_option in sharding_options
      }
  
      self.assertEqual(expected_total_perfs, total_perfs)
  
imports: import unittest
  from typing import cast
  import torch
  import torchrec.optim
  from torchrec.distributed.embedding import EmbeddingCollectionSharder
  from torchrec.distributed.embedding_types import EmbeddingComputeKernel
  from torchrec.distributed.embeddingbag import EmbeddingBagCollectionSharder
  from torchrec.distributed.fbgemm_qcomm_codec import CommType, get_qcomm_codecs_registry, QCommsConfig
  from torchrec.distributed.planner.constants import BATCH_SIZE
  from torchrec.distributed.planner.enumerators import EmbeddingEnumerator
  from torchrec.distributed.planner.shard_estimators import _calculate_storage_specific_sizes, EmbeddingOffloadStats, EmbeddingPerfEstimator
  from torchrec.distributed.planner.types import ParameterConstraints, Perf, Topology
  from torchrec.distributed.quant_embeddingbag import QuantEmbeddingBagCollectionSharder
  from torchrec.distributed.test_utils.test_model import TestSparseNN
  from torchrec.distributed.tests.test_quant_model_parallel import _quantize
  from torchrec.distributed.tests.test_sequence_model import TestSequenceSparseNN
  from torchrec.distributed.types import CacheParams, CacheStatistics, ModuleSharder, ShardingType
  from torchrec.modules.embedding_configs import EmbeddingBagConfig, EmbeddingConfig
label_desc: Use image node_id 4 for calling the TestEmbeddingPerfEstimator obj's underlying member method code with example usage: obj.test_sequence_2_table_perf() without return types