code: def build(
      world_size: int, num_tables: int
  ) -> Tuple[EmbeddingEnumerator, nn.Module]:
      compute_device = "cuda"
      topology = Topology(
          world_size=world_size,
          local_world_size=8,
          compute_device=compute_device,
      )
      tables = [
          EmbeddingBagConfig(
              num_embeddings=100 + i,
              embedding_dim=128,
              name="table_" + str(i),
              feature_names=["feature_" + str(i)],
          )
          for i in range(num_tables)
      ]
      model = TestSparseNN(tables=tables, weighted_tables=[])
      enumerator = EmbeddingEnumerator(
          topology=topology, batch_size=BATCH_SIZE
      )
      return enumerator, model
  
imports: import time
  import unittest
  from typing import List, Tuple
  from torch import nn
  from torchrec.distributed.embedding_types import EmbeddingComputeKernel
  from torchrec.distributed.embeddingbag import EmbeddingBagCollectionSharder
  from torchrec.distributed.planner.constants import BATCH_SIZE
  from torchrec.distributed.planner.enumerators import EmbeddingEnumerator
  from torchrec.distributed.planner.types import Topology
  from torchrec.distributed.test_utils.test_model import TestSparseNN
  from torchrec.distributed.types import ModuleSharder, ShardingType
  from torchrec.modules.embedding_configs import EmbeddingBagConfig
label_desc: Use image node_id 1 for calling the TestEnumeratorBenchmark obj's underlying member method code with example usage: obj.build(world_size, num_tables) and returns: enumerator,  model