code: def _get_valid_inputs_for_compute_scores(
      alpha: float,
      labels: Optional[List[Dict[str, Any]]] = None,
      predictions: Optional[List[np.ndarray]] = None,
  ) -> List[AuxiliaryTypesDict]:
      """Takes in alpha, labels and predictions and returns auxiliary input dictionary containing divided parts of labels and prediction per image."""
      if predictions is None or labels is None:
          raise ValueError(
              f"Predictions and labels can not be None. Both are needed to get valid inputs."
          )
      min_possible_similarity = _get_min_possible_similarity(
          alpha, predictions, labels
      )
  
      auxiliary_inputs = []
  
      for prediction, label in zip(predictions, labels):
          auxiliary_input_dict = (
              _get_valid_inputs_for_compute_scores_per_image(
                  alpha=alpha,
                  label=label,
                  prediction=prediction,
                  min_possible_similarity=min_possible_similarity,
              )
          )
          auxiliary_inputs.append(auxiliary_input_dict)
  
      return auxiliary_inputs
  
imports: import warnings
  from cleanlab.internal.constants import ALPHA, CUSTOM_SCORE_WEIGHT_BADLOC, CUSTOM_SCORE_WEIGHT_OVERLOOKED, CUSTOM_SCORE_WEIGHT_SWAP, EUC_FACTOR, HIGH_PROBABILITY_THRESHOLD, LOW_PROBABILITY_THRESHOLD, MAX_ALLOWED_BOX_PRUNE, TINY_VALUE, TEMPERATURE, LABEL_OVERLAP_THRESHOLD
  import copy
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, TypeVar
  import numpy
  from cleanlab.internal.object_detection_utils import softmin1d, assert_valid_aggregation_weights, assert_valid_inputs
label_desc: Use image node_id 20 for calling a global function with example usage: _get_valid_inputs_for_compute_scores(alpha, labels, predictions) and returns: auxiliary_inputs