code: def test_basic(self) -> None:
      stats = EmbeddingOffloadStats(
          cacheability=0.42,
          expected_lookups=31,
          mrc_hist_counts=torch.tensor([99, 98, 97]),
          height=92,
      )
      self.assertEqual(stats.cacheability, 0.42)
      self.assertEqual(stats.expected_lookups, 31)
      self.assertEqual(stats.expected_miss_rate(0), 1.0)
      self.assertEqual(stats.expected_miss_rate(1), 0.0)
      self.assertAlmostEqual(
          stats.expected_miss_rate(0.5), 1 - (99 + 98) / (99 + 98 + 97)
      )
  
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 1 for calling the TestEmbeddingOffloadStats obj's underlying member method code with example usage: obj.test_basic() without return types