code: def sharding_types(self, compute_device_type: str) -> List[str]:
      return [self._sharding_type]
  
imports: from typing import cast, Dict, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from torchrec.distributed.embedding import EmbeddingCollectionSharder
  from torchrec.distributed.fbgemm_qcomm_codec import get_qcomm_codecs_registry, QCommsConfig
  from torchrec.distributed.test_utils.test_model import ModelInput, TestDenseArch, TestSparseNNBase
  from torchrec.modules.embedding_configs import BaseEmbeddingConfig, EmbeddingConfig
  from torchrec.modules.embedding_modules import EmbeddingCollection
  from torchrec.modules.embedding_tower import EmbeddingTower
  from torchrec.sparse.jagged_tensor import JaggedTensor, KeyedJaggedTensor
label_desc: Use image node_id 2 for calling the TestEmbeddingCollectionSharder obj's underlying member method code with example usage: obj.sharding_types(compute_device_type) and returns: list