code: def csv_equality_helper(array, csv_preamble, landmark):
      """
      Test if a given numpy array is equal to a csv file with a specific landmark and path.
  
      Parameters:
          array (numpy.ndarray): The numpy array to test for equality with the CSV file.
          csv_preamble (str): The path preamble for the CSV file to be compared with the numpy array.
          landmark (int): The landmark index for the CSV file.
  
      Raises:
          AssertionError: If the numpy array and the CSV file do not match.
  
      Returns:
          None.
      """
  
      array = array.to_numpy()
      # Convert the DataFrame to a numpy array
      read_array = read_csv_landmark(csv_preamble, landmark)
  
      for idx, val in enumerate(read_array):
          for inner_idx, inner_val in enumerate(val):
              if inner_idx == 0:
                  continue
              assert str(inner_val) == str(array[idx][inner_idx])
  
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 5 for calling a global function with example usage: csv_equality_helper(array, csv_preamble, landmark) without return types