code: def __init__(
      self,
      module: FeatureProcessedEmbeddingBagCollection,
      table_name_to_parameter_sharding: Dict[str, ParameterSharding],
      ebc_sharder: EmbeddingBagCollectionSharder,
      env: ShardingEnv,
      device: torch.device,
  ) -> None:
      super().__init__()
  
      self._device = device
      self._env = env
  
      self._embedding_bag_collection: ShardedEmbeddingBagCollection = (
          ebc_sharder.shard(
              module._embedding_bag_collection,
              table_name_to_parameter_sharding,
              env=env,
              device=device,
          )
      )
  
      self._lookups: List[
          nn.Module
      ] = self._embedding_bag_collection._lookups
  
      self._is_collection: bool = False
      self._feature_processors: Union[
          nn.ModuleDict, FeatureProcessorsCollection
      ]
      if isinstance(
          module._feature_processors, FeatureProcessorsCollection
      ):
          self._feature_processors = module._feature_processors
          self._is_collection = True
      else:
          self._feature_processors = torch.nn.ModuleDict(
              module._feature_processors.items()
          )
          self._is_collection = False
  
      init_parameters(self._feature_processors, device)
      self._no_op_zero: torch.Tensor = torch.zeros(
          (1,), device=self._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 1 to create a new ShardedFeatureProcessedEmbeddingBagCollection object from inherited base classes: ShardedEmbeddingModule with example: obj = ShardedFeatureProcessedEmbeddingBagCollection(module, table_name_to_parameter_sharding, ebc_sharder, env, device)