code: def load_labels_images_features_metadata(input_data):
      # load the image paths
      test_image_paths = load_file(input_data["test_image_paths"])
      print(f"Number of test image_paths: {test_image_paths.shape}\n")
      train_image_paths = load_file(input_data["train_image_paths"])
      print(f"Number of test image_paths: {train_image_paths.shape}\n")
  
      # load the features
      test_features = load_file(input_data["test_features"])
      print(f"test_features: {test_features.shape}\n")
      train_features = load_file(input_data["train_features"])
      print(f"train_features: {train_features.shape}\n")
  
      # load the metadata
      test_metadata = load_file(input_data["test_metadata"])
      if isinstance(test_metadata, list):
          print(f"test_metadata: {len(test_metadata)}")
          print(f"test_metadata keys: {test_metadata[0].keys()}")
      else:
          print(
              f"test_metadata: {list(test_metadata.values())[0].keys()}"
          )
      train_metadata = load_file(input_data["train_metadata"])
      if isinstance(train_metadata, list):
          print(f"train_metadata: {len(train_metadata)}")
          print(f"train_metadata keys: {train_metadata[0].keys()}")
      else:
          print(
              f"train_metadata: {list(train_metadata.values())[0].keys()}"
          )
  
      # load the knn_indices
      test_img_indices = load_file(input_data["test_indices"])
      print(f"Loaded test indices: {len(test_img_indices)}")
      train_img_indices = load_file(input_data["train_indices"])
      print(f"Loaded train indices: {len(train_img_indices)}")
      return (
          test_image_paths,
          test_metadata,
          train_image_paths,
          train_metadata,
          test_features,
          test_img_indices,
          train_features,
          train_img_indices,
      )
  
file_path: vissl/projects/fairness_indicators/similarity_search/inference_casual_conversations.py
imports: import numpy
  import pandas
  import torch
  from vissl.utils.io import load_file
label_desc: Use image node_id 1 for calling a global function with example usage: load_labels_images_features_metadata(input_data) and returns: test_image_paths,  test_metadata,  train_image_paths,  train_metadata,  test_features,  test_img_indices,  train_features,  train_img_indices