code: def add_fp_params_grad_sync_callback(
      self, lazy_awaitable_kt: LazyAwaitable[KeyedTensor]
  ) -> LazyAwaitable[KeyedTensor]:
      # This will ensure that all feature processor parameters participate in the
      # autograd graph across all ranks. This will protect from mismatched collective
      # calls order when using DistributedDataParallel over feature processors.
      no_op_tensor = (
          self._no_op_zero
          * torch.cat(
              [
                  x.flatten()
                  for x in self._feature_processors.parameters()
              ]
          ).sum()
      )
      lazy_awaitable_kt.callbacks.append(
          partial(param_dp_sync, no_op_tensor=no_op_tensor)
      )
      return lazy_awaitable_kt
  
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 7 for calling the ShardedFeatureProcessedEmbeddingBagCollection obj's underlying member method code with example usage: obj.add_fp_params_grad_sync_callback(lazy_awaitable_kt) and returns: lazy_awaitable_kt