code: def sharding_types(self, compute_device_type: str) -> List[str]:
      types = [
          ShardingType.DATA_PARALLEL.value,
          ShardingType.TABLE_WISE.value,
          ShardingType.COLUMN_WISE.value,
          ShardingType.ROW_WISE.value,
      ]
  
      return types
  
imports: from typing import Dict, Iterator, List, Optional, Type
  import torch
  from torch import nn
  from torch.nn.parallel import DistributedDataParallel
  from torchrec.distributed.embedding import ShardedEmbeddingCollection
  from torchrec.distributed.embedding_types import BaseEmbeddingSharder, EmbeddingComputeKernel
  from torchrec.distributed.sharding.dp_sharding import DpPooledEmbeddingSharding
  from torchrec.distributed.types import ParameterSharding, ShardingEnv, ShardingType
  from torchrec.distributed.utils import append_prefix
  from torchrec.modules.fused_embedding_modules import convert_optimizer_type_and_kwargs, FusedEmbeddingCollection
  from torchrec.optim.fused import FusedOptimizerModule
label_desc: Use image node_id 4 for calling the FusedEmbeddingCollectionSharder obj's underlying member method code with example usage: obj.sharding_types(compute_device_type) and returns: types