code: def _get_valid_inputs_for_compute_scores_per_image(
      alpha: float,
      *,
      label: Optional[Dict[str, Any]] = None,
      prediction: Optional[np.ndarray] = None,
      pred_labels: Optional[np.ndarray] = None,
      pred_label_probs: Optional[np.ndarray] = None,
      pred_bboxes: Optional[np.ndarray] = None,
      lab_labels: Optional[np.ndarray] = None,
      lab_bboxes: Optional[np.ndarray] = None,
      similarity_matrix: Optional[np.ndarray] = None,
      iou_matrix: Optional[np.ndarray] = None,
      min_possible_similarity: Optional[float] = None,
  ) -> AuxiliaryTypesDict:
      """Returns valid inputs for compute scores by either passing through values or calculating the inputs internally."""
      if lab_labels is None or lab_bboxes is None:
          if label is None:
              raise ValueError(
                  f"Pass in either one of label or label labels into auxiliary inputs. Both can not be None."
              )
          lab_bboxes, lab_labels = _separate_label(label)
  
      if (
          pred_labels is None
          or pred_label_probs is None
          or pred_bboxes is None
      ):
          if prediction is None:
              raise ValueError(
                  f"Pass in either one of prediction or prediction labels and prediction probabilities into auxiliary inputs. Both can not be None."
              )
          (
              pred_bboxes,
              pred_labels,
              pred_label_probs,
          ) = _separate_prediction(prediction)
  
      if similarity_matrix is None:
          iou_matrix = _get_overlap_matrix(lab_bboxes, pred_bboxes)
          dist_matrix = 1 - _get_dist_matrix(lab_bboxes, pred_bboxes)
          similarity_matrix = iou_matrix * alpha + (1 - alpha) * (
              1 - dist_matrix
          )
  
      if iou_matrix is None:
          iou_matrix = _get_overlap_matrix(lab_bboxes, pred_bboxes)
  
      if min_possible_similarity is None:
          min_possible_similarity = (
              1.0
              if 0 in similarity_matrix.shape
              else np.min(
                  similarity_matrix[np.nonzero(similarity_matrix)]
              )
          )
  
      auxiliary_input_dict: AuxiliaryTypesDict = {
          "pred_labels": pred_labels,
          "pred_label_probs": pred_label_probs,
          "pred_bboxes": pred_bboxes,
          "lab_labels": lab_labels,
          "lab_bboxes": lab_bboxes,
          "similarity_matrix": similarity_matrix,
          "iou_matrix": iou_matrix,
          "min_possible_similarity": min_possible_similarity,
      }
  
      return auxiliary_input_dict
  
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 19 for calling a global function with example usage: _get_valid_inputs_for_compute_scores_per_image(alpha) and returns: auxiliary_input_dict