code: def shard(
      self,
      module: QuantFeatureProcessedEmbeddingBagCollection,
      params: Dict[str, ParameterSharding],
      env: ShardingEnv,
      device: Optional[torch.device] = None,
  ) -> ShardedQuantEmbeddingBagCollection:
      qebc = module
      assert isinstance(qebc, QuantEmbeddingBagCollection)
      fused_params = self.fused_params if self.fused_params else {}
      fused_params["output_dtype"] = data_type_to_sparse_type(
          dtype_to_data_type(qebc.output_dtype())
      )
      if (
          FUSED_PARAM_QUANT_STATE_DICT_SPLIT_SCALE_BIAS
          not in fused_params
      ):
          fused_params[
              FUSED_PARAM_QUANT_STATE_DICT_SPLIT_SCALE_BIAS
          ] = getattr(
              qebc, MODULE_ATTR_QUANT_STATE_DICT_SPLIT_SCALE_BIAS, False
          )
      if FUSED_PARAM_REGISTER_TBE_BOOL not in fused_params:
          fused_params[FUSED_PARAM_REGISTER_TBE_BOOL] = getattr(
              qebc, FUSED_PARAM_REGISTER_TBE_BOOL, False
          )
  
      return ShardedQuantFeatureProcessedEmbeddingBagCollection(
          qebc,
          params,
          env,
          fused_params,
          device=device,
          feature_processor=module.feature_processor,
      )
  
imports: from typing import Any, Dict, List, Optional, Type
  import torch
  from fbgemm_gpu.split_table_batched_embeddings_ops_inference import IntNBitTableBatchedEmbeddingBagsCodegen
  from torch import nn
  from torchrec.distributed.embedding_lookup import EmbeddingComputeKernel
  from torchrec.distributed.embedding_sharding import EmbeddingSharding, EmbeddingShardingInfo
  from torchrec.distributed.embedding_types import BaseQuantEmbeddingSharder, FeatureShardingMixIn, GroupedEmbeddingConfig, KJTList, ListOfKJTList
  from torchrec.distributed.embeddingbag import construct_output_kt, create_sharding_infos_by_sharding
  from torchrec.distributed.fused_params import FUSED_PARAM_QUANT_STATE_DICT_SPLIT_SCALE_BIAS, FUSED_PARAM_REGISTER_TBE_BOOL, get_tbes_to_register_from_iterable, is_fused_param_quant_state_dict_split_scale_bias, is_fused_param_register_tbe
  from torchrec.distributed.quant_state import ShardedQuantEmbeddingModuleState
  from torchrec.distributed.sharding.cw_sharding import InferCwPooledEmbeddingSharding
  from torchrec.distributed.sharding.rw_sharding import InferRwPooledEmbeddingSharding
  from torchrec.distributed.sharding.tw_sharding import InferTwEmbeddingSharding
  from torchrec.distributed.types import NullShardedModuleContext, NullShardingContext, ParameterSharding, ShardingEnv, ShardingType
  from torchrec.distributed.utils import copy_to_device
  from torchrec.modules.embedding_configs import data_type_to_sparse_type, dtype_to_data_type, EmbeddingBagConfig
  from torchrec.modules.embedding_modules import EmbeddingBagCollectionInterface
  from torchrec.modules.feature_processor_ import FeatureProcessorsCollection
  from torchrec.quant.embedding_modules import EmbeddingBagCollection, FeatureProcessedEmbeddingBagCollection, MODULE_ATTR_QUANT_STATE_DICT_SPLIT_SCALE_BIAS
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor, KeyedTensor
label_desc: Use image node_id 3 for calling the QuantFeatureProcessedEmbeddingBagCollectionSharder obj's underlying member method code with example usage: obj.shard(module, params, env, device) and returns: ShardedQuantFeatureProcessedEmbeddingBagCollection