code: def test_inference_1_table_perf(self) -> None:
      tables = [
          EmbeddingBagConfig(
              num_embeddings=100,
              embedding_dim=10,
              name="table_0",
              feature_names=["feature_0"],
          )
      ]
      model = TestSparseNN(tables=tables, weighted_tables=[])
      quant_model = _quantize(model, inplace=True)
  
      inference_estimator = EmbeddingPerfEstimator(
          topology=self.topology, is_inference=True
      )
      inference_enumerator = EmbeddingEnumerator(
          topology=self.topology,
          batch_size=BATCH_SIZE,
          estimator=inference_estimator,
      )
      sharding_options = inference_enumerator.enumerate(
          module=quant_model,
          sharders=[
              cast(
                  ModuleSharder[torch.nn.Module],
                  QuantEmbeddingBagCollectionSharder(),
              )
          ],
      )
  
      expected_total_perfs = {
          ("quant", "table_wise"): [0.0001296231579222408],
          ("quant_uvm", "table_wise"): [0.018350937787224266],
          ("quant_uvm_caching", "table_wise"): [0.004269758427175579],
          ("quant", "row_wise"): [
              0.0001819317157451923,
              0.0001819317157451923,
          ],
          ("quant_uvm", "row_wise"): [
              0.023103601792279417,
              0.023103601792279417,
          ],
          ("quant_uvm_caching", "row_wise"): [
              0.005390052899352861,
              0.005390052899352861,
          ],
          ("quant", "column_wise"): [0.0001296231579222408],
          ("quant_uvm", "column_wise"): [0.018350937787224266],
          ("quant_uvm_caching", "column_wise"): [0.004269758427175579],
      }
  
      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(total_perfs, expected_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 5 for calling the TestEmbeddingPerfEstimator obj's underlying member method code with example usage: obj.test_inference_1_table_perf() without return types