code: def generate_prediction(
      annotation, num_classes, image_size, max_boxes, issue
  ):
      """Generates a single prediction based on passed in hyperparameters in same format as expected by find_label_issues and get_label_quality_scores"""
  
      prediction = [[] for _ in range(num_classes)]
      if annotation is None and issue is False:
          return
      else:
          if issue is False:
              for label, bboox in zip(
                  annotation["labels"], annotation["bboxes"]
              ):
                  rand_probability = (
                      np.random.randint(low=96, high=100) / 100
                  )
                  prediction[label].append(
                      list(bboox) + [rand_probability]
                  )
          else:
              num_predictions = np.random.randint(
                  low=1, high=max_boxes + 1
              )
              rand_labels = generate_labels(
                  num_classes, num_predictions
              )
              for label in rand_labels:
                  rand_bbox = generate_bbox(image_size)
                  rand_probability = (
                      np.random.randint(low=96, high=100) / 100
                  )
                  prediction[label].append(
                      list(rand_bbox) + [rand_probability]
                  )
          prediction = [
              np.array(p)
              if len(p) > 0
              else np.empty(shape=[0, 5], dtype=np.float32)
              for p in prediction
          ]
      return np.array(prediction, dtype=object)
  
imports: import os
  from collections import Counter, defaultdict
  from multiprocessing import Pool
  import numpy
  from cleanlab.internal.constants import ALPHA, BADLOC_THRESHOLD_FACTOR, CUSTOM_SCORE_WEIGHT_BADLOC, CUSTOM_SCORE_WEIGHT_OVERLOOKED, CUSTOM_SCORE_WEIGHT_SWAP, HIGH_PROBABILITY_THRESHOLD, LOW_PROBABILITY_THRESHOLD, OVERLOOKED_THRESHOLD_FACTOR, SWAP_THRESHOLD_FACTOR, TEMPERATURE
  from cleanlab.internal.object_detection_utils import bbox_xyxy_to_xywh, softmax, softmin1d
  from cleanlab.object_detection.filter import _calculate_true_positives_false_positives, _filter_by_class, _find_label_issues, _find_label_issues_per_box, _get_per_class_ap, _pool_box_scores_per_image, _process_class_list, find_label_issues
  from cleanlab.object_detection.rank import _compute_label_quality_scores, _get_aggregation_weights, _get_dist_matrix, _get_min_pred_prob, _get_overlap_matrix, _get_prediction_type, _get_valid_inputs_for_compute_scores, _get_valid_inputs_for_compute_scores_per_image, _get_valid_score, _get_valid_subtype_score_params, _has_overlap, _prune_by_threshold, _separate_label, _separate_prediction, compute_badloc_box_scores, compute_overlooked_box_scores, compute_swap_box_scores, get_label_quality_scores, issues_from_scores
  from cleanlab.object_detection.summary import bounding_box_size_distribution, class_label_distribution, get_sorted_bbox_count_idxs, object_counts_per_image, plot_class_distribution, plot_class_size_distributions, visualize, calculate_per_class_metrics, get_average_per_class_confusion_matrix
  import copy
  import warnings
  import matplotlib.pyplot
  import numpy
  import pytest
  from PIL import Image
label_desc: Use image node_id 5 for calling a global function with example usage: generate_prediction(annotation, num_classes, image_size, max_boxes, issue) and returns: np