code: def _load_json(model, data, thresholds):
      (
          training,
          architecture,
          tdata,
          tdata_source,
          tdata_size,
      ) = parse_file_name(Path(model).name)
  
      records = []
      for image_id, image_data in data.items():
          label = image_data["concept"]
          hits, covers = _is_hit_naive(
              image_data["confidence_scores"],
              thresholds,
              label,
              image_data["raw_prediction"],
          )
          houshold = image_id.split("/")[1]
          image_checksum = (
              image_data["url"]
              .split("/")[-1]
              .split(".")[0]
              .split("-")[-1]
          )
          for t, hit, cover in zip(thresholds, hits, covers):
              model_name = f"{training} {architecture} {tdata} t={t}"
              records.append(
                  (
                      training,
                      architecture,
                      tdata,
                      tdata_source,
                      tdata_size,
                      t,
                      _model_idx(model_name),
                      model_name,
                      image_data["region"],
                      houshold,
                      image_data["country"],
                      float(image_data["income"]),
                      image_checksum,
                      label,
                      hit,
                      cover,
                  )
              )
      return pd.DataFrame.from_records(records, columns=COLUMNS)
  
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 9 for calling a global function with example usage: _load_json(model, data, thresholds) and returns: pd