code: def compute_and_output_dist(
      self, ctx: EmbeddingBagCollectionContext, input: KJTList
  ) -> LazyAwaitable[KeyedTensor]:
      fp_features = self.apply_feature_processors_to_kjt_list(input)
      lazy_awaitable_kt = (
          self._embedding_bag_collection.compute_and_output_dist(
              ctx, fp_features
          )
      )
      return self.add_fp_params_grad_sync_callback(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 6 for calling the ShardedFeatureProcessedEmbeddingBagCollection obj's underlying member method code with example usage: obj.compute_and_output_dist(ctx, input) and returns: self