code: def generate_output_json_data(
      model_name,
      predictions_file,
      pred_confidence_scores_file,
      pred_img_indices_file,
  ):
      my_model_in22k_subset_dollar_street_full_finetuned = {
          "model_name": model_name,
          "image_paths": "https://dl.fbaipublicfiles.com/vissl/fairness/dollarstreet_in22k_cls_overlapped_images.npy",
          "targets": "https://dl.fbaipublicfiles.com/vissl/fairness//subset_dollarstreet_in22k_cls_overlapped_labels.npy",
          "metadata": "https://dl.fbaipublicfiles.com/vissl/fairness/metadata_full_dollar_street.json",
          "id_to_label_map": "https://dl.fbaipublicfiles.com/vissl/fairness/in22k_cls_idx_to_dollar_street_labels_map.json",
          "predictions": predictions_file,
          "pred_img_indices": pred_img_indices_file,
          "pred_confidence_scores": pred_confidence_scores_file,
      }
  
      (
          my_model_output_attributes_acc_map,
          my_model_output_metadata_map,
      ) = generate_dollar_street_analysis(
          my_model_in22k_subset_dollar_street_full_finetuned,
          topk=PRED_TOPK,
          confidence_threshold=PRED_CONFIDENCE_THRESHOLD,
      )
      print(list(my_model_output_metadata_map.values())[:10])
      output_dir = "/tmp/dollar_street_models"
      output_file = f"{output_dir}/my_model_output_metadata_map.json"
      save_file(my_model_output_metadata_map, output_file)
      test_data_save = load_file(output_file)
      print(len(test_data_save))
      return output_dir
  
imports: import json
  import os
  from math import ceil, floor
  from pathlib import Path
  import matplotlib.pyplot
  import numpy
  import pandas
  import seaborn
  from IPython.display import display_html
  from matplotlib.ticker import FixedLocator
  from vissl.utils.io import load_file, save_file
label_desc: Use image node_id 13 for calling a global function with example usage: generate_output_json_data(model_name, predictions_file, pred_confidence_scores_file, pred_img_indices_file) and returns: output_dir