code: def calculate_metrics(
      model_name,
      label_predictions_file,
      label_predictions_scores_file,
      pred_img_indices_file,
  ):
      for PREDICTION_CONFIDENCE_THRESHOLD in [0.0, 0.1, 0.3, 0.8]:
          my_model_cc_face_crops_predictions = {
              "model_name": model_name,
              "label_predictions": label_predictions_file,
              "label_predictions_scores": label_predictions_scores_file,
              "pred_img_indices": pred_img_indices_file,
          }
          _, _, _ = generate_openimages_disentangle_analysis(
              COMMON_MAP_TEST,
              my_model_cc_face_crops_predictions,
              topk=TOPK,
              confidence_threshold=PREDICTION_CONFIDENCE_THRESHOLD,
              DISPARATE_THRESHOLD=DISPARATE_THRESHOLD,
              DISPARATE_LABELS_LIST=DISPARATE_LABELS_LIST,
              LABEL_ASSOC_MAPPING=IN22K_SUBSET_LABEL_ASSOCIATION_MAP,
          )
  
file_path: vissl/projects/fairness_indicators/harmful_label_associations/inference_label_assoc_openimages_miap_test.py
imports: import matplotlib.pyplot
  import numpy
  import pandas
  import torchvision.transforms
  from fvcore.common.file_io import PathManager
  from PIL import Image
  from vissl.utils.io import load_file
label_desc: Use image node_id 10 for calling a global function with example usage: calculate_metrics(model_name, label_predictions_file, label_predictions_scores_file, pred_img_indices_file) without return types