code: def shard(
      self,
      module: FeatureProcessedEmbeddingBagCollection,
      params: Dict[str, ParameterSharding],
      env: ShardingEnv,
      device: Optional[torch.device] = None,
  ) -> ShardedFeatureProcessedEmbeddingBagCollection:
      if device is None:
          device = torch.device("cuda")
  
      return ShardedFeatureProcessedEmbeddingBagCollection(
          module,
          params,
          ebc_sharder=self._ebc_sharder,
          env=env,
          device=device,
      )
  
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 2 for calling the FeatureProcessedEmbeddingBagCollectionSharder obj's underlying member method code with example usage: obj.shard(module, params, env, device) and returns: ShardedFeatureProcessedEmbeddingBagCollection