code: def change_cand_size_slate_ranking(
      input_prototype, candidate_size_override
  ):
      state_prototype, candidate_prototype = input_prototype
      candidate_prototype = (
          candidate_prototype[0][:, :1, :].repeat(
              1, candidate_size_override, 1
          ),
          candidate_prototype[1][:, :1, :].repeat(
              1, candidate_size_override, 1
          ),
      )
      return (
          (
              torch.randn_like(state_prototype[0]),
              torch.ones_like(state_prototype[1]),
          ),
          (
              torch.randn_like(candidate_prototype[0]),
              torch.ones_like(candidate_prototype[1]),
          ),
      )
  
imports: import torch
  from reagent.preprocessing.identify_types import CONTINUOUS, CONTINUOUS_ACTION
  from reagent.preprocessing.normalization import NormalizationParameters
label_desc: Use image node_id 3 for calling a global function with example usage: change_cand_size_slate_ranking(input_prototype, candidate_size_override) and returns: torch,  torch,  torch,  torch