code: def prepare_inputs(self):
      input_ids = ids_tensor(
          (self.batch_size * self.num_beams, self.sequence_length),
          self.vocab_size,
      )
      next_tokens = ids_tensor(
          (self.batch_size, 2 * self.num_beams), self.vocab_size
      ).to(torch_device)
      next_indices = ids_tensor(
          (self.batch_size, 2 * self.num_beams), self.num_beams
      ).to(torch_device)
      next_scores, _ = (
          -floats_tensor((self.batch_size, 2 * self.num_beams)).to(
              torch_device
          )
      ).sort(descending=True)
      scores_for_all_vocab, _ = (
          -floats_tensor(
              (self.batch_size * self.num_beams, self.vocab_size)
          ).to(torch_device)
      ).sort(descending=True)
      return (
          input_ids,
          next_tokens,
          next_indices,
          next_scores,
          scores_for_all_vocab,
      )
  
imports: import unittest
  from transformers import is_torch_available
  from transformers.testing_utils import require_torch, torch_device
  from ..test_modeling_common import floats_tensor, ids_tensor
label_desc: Use image node_id 3 for calling the ConstrainedBeamSearchTester obj's underlying member method code with example usage: obj.prepare_inputs() and returns: input_ids,  next_tokens,  next_indices,  next_scores,  scores_for_all_vocab