code: def test_never_fit(self) -> None:
      tables = [
          EmbeddingBagConfig(
              num_embeddings=10000000,
              embedding_dim=10000000,
              name="table_" + str(i),
              feature_names=["feature_" + str(i)],
          )
          for i in range(2)
      ]
      model = TestSparseNN(
          tables=tables, sparse_device=torch.device("meta")
      )
  
      with self.assertRaises(PlannerError) as context:
          self.planner.plan(module=model, sharders=[TWvsRWSharder()])
      self.assertEqual(
          context.exception.error_type,
          PlannerErrorType.INSUFFICIENT_STORAGE,
      )
  
      # since it has negative storage_constraint
      self.assertEqual(self.planner._num_proposals, 0)
  
imports: import unittest
  from typing import cast, List
  import torch
  from torch import nn
  from torchrec.distributed.embedding_types import EmbeddingComputeKernel
  from torchrec.distributed.embeddingbag import EmbeddingBagCollectionSharder
  from torchrec.distributed.planner import ParameterConstraints
  from torchrec.distributed.planner.planners import EmbeddingShardingPlanner
  from torchrec.distributed.planner.types import PlannerError, PlannerErrorType, Topology
  from torchrec.distributed.sharding_plan import get_default_sharders
  from torchrec.distributed.test_utils.test_model import TestSparseNN
  from torchrec.distributed.types import BoundsCheckMode, CacheAlgorithm, CacheParams, DataType, EmbeddingModuleShardingPlan, ModuleSharder, ShardingPlan, ShardingType
  from torchrec.modules.embedding_configs import EmbeddingBagConfig
label_desc: Use image node_id 4 for calling the TestEmbeddingShardingPlanner obj's underlying member method code with example usage: obj.test_never_fit() without return types