code: def seq2slate_input_prototype_to_ranking_input(
      state_input_prototype,
      candidate_input_prototype,
      state_preprocessor,
      candidate_preprocessor,
  ):
      (
          batch_size,
          candidate_size,
          candidate_dim,
      ) = candidate_input_prototype[0].shape
      preprocessed_state = state_preprocessor(
          state_input_prototype[0], state_input_prototype[1]
      )
      preprocessed_candidates = candidate_preprocessor(
          candidate_input_prototype[0].view(
              batch_size * candidate_size, candidate_dim
          ),
          candidate_input_prototype[1].view(
              batch_size * candidate_size, candidate_dim
          ),
      ).view(batch_size, candidate_size, -1)
      return rlt.PreprocessedRankingInput.from_tensors(
          state=preprocessed_state,
          src_seq=preprocessed_candidates,
      )
  
imports: import random
  import unittest
  import numpy.testing
  import reagent.core.types
  import reagent.models
  import torch
  from reagent.model_utils.seq2slate_utils import Seq2SlateMode, Seq2SlateOutputArch
  from reagent.models.seq2slate import Seq2SlateTransformerNet
  from reagent.prediction.cfeval.predictor_wrapper import BanditRewardNetPredictorWrapper
  from reagent.prediction.predictor_wrapper import ActorPredictorWrapper, ActorWithPreprocessor, DiscreteDqnPredictorWrapper, DiscreteDqnWithPreprocessor, FAKE_STATE_FEATURE_ID, ParametricDqnPredictorWrapper, ParametricDqnWithPreprocessor, Seq2SlatePredictorWrapper, Seq2SlateWithPreprocessor
  from reagent.prediction.ranking.predictor_wrapper import DeterminantalPointProcessPredictorWrapper, Kernel
  from reagent.preprocessing.postprocessor import Postprocessor
  from reagent.preprocessing.preprocessor import Preprocessor
  from reagent.test.prediction.test_prediction_utils import _cont_action_norm, _cont_norm, change_cand_size_slate_ranking
label_desc: Use image node_id 1 for calling a global function with example usage: seq2slate_input_prototype_to_ranking_input(state_input_prototype, candidate_input_prototype, state_preprocessor, candidate_preprocessor) and returns: rlt