code: def test_rank_and_mask(
      input_values: List[float],
      ranking_length: int,
      possible_output_values: List[List[float]],
      renormalize: bool,
      resulting_ranking_length: int,
  ):
      confidences = np.array(input_values)
      indices, modified_confidences = train_utils.rank_and_mask(
          confidences=confidences,
          ranking_length=ranking_length,
          renormalize=renormalize,
      )
      assert any(
          np.allclose(modified_confidences, np.array(possible_output))
          for possible_output in possible_output_values
      )
      assert np.allclose(
          sorted(input_values, reverse=True)[:resulting_ranking_length],
          confidences[indices],
      )
  
imports: from typing import Any, Dict, List
  import numpy
  import pytest
  from typing import Text
  import rasa.utils.train_utils
  from rasa.nlu.constants import NUMBER_OF_SUB_TOKENS
  from rasa.nlu.tokenizers.tokenizer import Token
  from rasa.shared.nlu.constants import SPLIT_ENTITIES_BY_COMMA_DEFAULT_VALUE, SPLIT_ENTITIES_BY_COMMA
  from rasa.utils.tensorflow.constants import MODEL_CONFIDENCE, RANKING_LENGTH, RENORMALIZE_CONFIDENCES, SIMILARITY_TYPE, LOSS_TYPE, COSINE, SOFTMAX, INNER, CROSS_ENTROPY, MARGIN, AUTO, TOLERANCE, CHECKPOINT_MODEL, EVAL_NUM_EPOCHS, EVAL_NUM_EXAMPLES, EPOCHS
  from rasa.shared.exceptions import InvalidConfigException
label_desc: Use image node_id 2 for calling a global function with example usage: test_rank_and_mask(input_values, ranking_length, possible_output_values, renormalize, resulting_ranking_length) without return types