code: def sharding_types(self, compute_device_type: str) -> List[str]:
      # No row wise because position weighted FP and RW don't play well together.
      types = [
          ShardingType.DATA_PARALLEL.value,
          ShardingType.TABLE_WISE.value,
          ShardingType.COLUMN_WISE.value,
          ShardingType.TABLE_COLUMN_WISE.value,
      ]
  
      return types
  
imports: from functools import partial
  from typing import Any, Dict, Iterator, List, Optional, Type, Union
  import torch
  from torch import nn
  from torchrec.distributed.embedding_types import BaseEmbeddingSharder, KJTList, ShardedEmbeddingModule
  from torchrec.distributed.embeddingbag import EmbeddingBagCollectionContext, EmbeddingBagCollectionSharder, ShardedEmbeddingBagCollection
  from torchrec.distributed.types import Awaitable, LazyAwaitable, ParameterSharding, QuantizedCommCodecs, ShardingEnv, ShardingType
  from torchrec.distributed.utils import append_prefix, init_parameters
  from torchrec.modules.feature_processor_ import FeatureProcessorsCollection
  from torchrec.modules.fp_embedding_modules import apply_feature_processors_to_kjt, FeatureProcessedEmbeddingBagCollection
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor, KeyedTensor
label_desc: Use image node_id 6 for calling the FeatureProcessedEmbeddingBagCollectionSharder obj's underlying member method code with example usage: obj.sharding_types(compute_device_type) and returns: types