code: def helper_test_qbin_fit(
      landmark: int,
      all_uncert_error_pairs_to_compare: np.ndarray,
      landmark_results_path_val: str,
      landmark_results_path_test: str,
      num_bins: int,
      cfg: Any,
      gt_test_error_available: bool,
      save_folder: str,
      model: str,
  ) -> None:
      """
      Test the `fit_and_predict()` function with a specific landmark.
  
      Args:
          landmark (int): The landmark index to use for testing.
          all_uncert_error_pairs_to_compare (np.ndarray): An array of shape `(N, 2)` containing uncertainty-error
              pairs to use for testing.
          landmark_results_path_val (str): The path to the directory containing validation results for the
              specified landmark.
          landmark_results_path_test (str): The path to the directory containing test results for the specified
              landmark.
          num_bins (int): The number of bins to use for quantile binning.
          cfg (Any): A configuration object containing various settings for the algorithm.
          gt_test_error_available (bool): Whether or not ground truth test errors are available.
          save_folder (str): The path to the directory where intermediate results should be saved.
  
      Returns:
          None
  
      Raises:
          AssertionError: If any of the tests fail.
  
      This function tests the `fit_and_predict()` function with a specific landmark and set of inputs. It first creates
      a directory at the `save_folder` path if one does not already exist. It then calls `fit_and_predict()` with the
      specified inputs to generate predictions and associated uncertainty estimates. The function then tests the
      resulting `estimated_errors`, `uncert_boundaries`, and `predicted_bins` arrays using the `csv_equality_helper()`
      function.
  
      Note that this function assumes that there are ground truth test errors available for testing. If ground truth
      errors are not available, you should set `gt_test_error_available` to `False`.
      """
      # Create the `save_folder` directory if it does not exist
      os.makedirs(save_folder, exist_ok=True)
  
      # Call `fit_and_predict()` with the specified inputs
      (
          uncert_boundaries,
          estimated_errors,
          predicted_bins,
      ) = fit_and_predict(
          landmark,
          all_uncert_error_pairs_to_compare,
          landmark_results_path_val,
          landmark_results_path_test,
          num_bins,
          cfg,
          groundtruth_test_errors=gt_test_error_available,
          save_folder=save_folder,
      )
  
      # Test `estimated_errors` using `csv_equality_helper()`
      csv_equality_helper(
          estimated_errors,
          os.path.join(
              cfg["DATASET"]["ROOT"], cfg["DATASET"]["BASE_DIR"], model
          )
          + "/4CH/"
          + str(num_bins)
          + "Bins_fit/estimated_error_bounds",
          landmark,
      )
  
      # Test `uncert_boundaries` using `csv_equality_helper()`
      csv_equality_helper(
          uncert_boundaries,
          os.path.join(
              cfg["DATASET"]["ROOT"], cfg["DATASET"]["BASE_DIR"], model
          )
          + "/4CH/"
          + str(num_bins)
          + "Bins_fit/uncertainty_bounds",
          landmark,
      )
  
      # Test `predicted_bins` using `csv_equality_helper()`
      csv_equality_helper(
          predicted_bins,
          os.path.join(
              cfg["DATASET"]["ROOT"], cfg["DATASET"]["BASE_DIR"], model
          )
          + "/4CH/"
          + str(num_bins)
          + "Bins_fit/res_predicted_bins",
          landmark,
      )
  
file_path: pykale/tests/pipeline/test_uncertainty_qbin_pipeline.py
imports: import os
  from typing import Any
  import numpy
  import pandas
  import pytest
  import seaborn
  import kale.utils.logger
  from kale.embed.uncertainty_fitting import fit_and_predict
  from kale.interpret.uncertainty_quantiles import generate_fig_comparing_bins, generate_fig_individual_bin_comparison
label_desc: Use image node_id 4 for calling a global function with example usage: helper_test_qbin_fit(landmark, all_uncert_error_pairs_to_compare, landmark_results_path_val, landmark_results_path_test, num_bins, cfg, gt_test_error_available, save_folder, model) without return types