code: def generate_dollar_street_analysis(
      input_data, topk=5, confidence_threshold=0.0
  ):
      print(
          f"================= {input_data['model_name']}, t={confidence_threshold}, top-{topk} ====================="
      )
      (
          image_paths,
          predictions,
          indices,
          targets,
          metadata,
          id_to_label,
          confidence_scores,
      ) = load_labels_metadata_predictions_images(
          input_data, confidence_threshold=confidence_threshold
      )
      output_metadata_map = generate_dollar_street_map(
          image_paths,
          metadata,
          predictions,
          id_to_label,
          confidence_scores,
          topk=topk,
      )
      output_attributes_acc_map = get_per_attribute_accuracy(
          output_metadata_map
      )
      print(f"t={confidence_threshold}, top-{topk}")
      # create_and_print_dataframe(output_attributes_acc_map)
      return output_attributes_acc_map, output_metadata_map
  
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 5 for calling a global function with example usage: generate_dollar_street_analysis(input_data, topk, confidence_threshold) and returns: output_attributes_acc_map,  output_metadata_map