code: def _get_min_possible_similarity(
      alpha: float,
      predictions,
      labels: List[Dict[str, Any]],
  ) -> float:
      """Gets the min possible similarity score between two bounding boxes out of all images."""
      min_possible_similarity = 1.0
      for prediction, label in zip(predictions, labels):
          lab_bboxes, lab_labels = _separate_label(label)
          pred_bboxes, pred_labels, _ = _separate_prediction(prediction)
          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
          )
          non_zero_similarity_matrix = similarity_matrix[
              np.nonzero(similarity_matrix)
          ]
          min_image_similarity = (
              1.0
              if 0 in non_zero_similarity_matrix.shape
              else np.min(non_zero_similarity_matrix)
          )
          min_possible_similarity = np.min(
              [min_possible_similarity, min_image_similarity]
          )
      return min_possible_similarity
  
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 18 for calling a global function with example usage: _get_min_possible_similarity(alpha, predictions, labels) and returns: min_possible_similarity