code: def read_csv_landmark(csv_preamble, landmark):
      """
      Read a CSV file with landmark-specific data.
  
      Args:
          csv_preamble (str): The path preamble to the directory containing the CSV file.
          landmark (int): The landmark index associated with the CSV file.
  
      Returns:
          np.ndarray: A numpy array containing the contents of the CSV file.
  
      Raises:
          FileNotFoundError: If the CSV file at the specified path cannot be found.
          ValueError: If the CSV file contains invalid data.
  
      This function reads a CSV file containing landmark-specific data. The path to the CSV file is constructed
      from the `csv_preamble` argument and the `landmark` argument. The CSV file should contain only numeric data,
      with one row per data point and one column per feature. The function reads the CSV file using the pandas
      `read_csv()` function and converts the resulting DataFrame to a numpy array using the `to_numpy()` method.
      If the CSV file cannot be found or contains invalid data, the function raises an appropriate error.
      """
  
      csv_path = os.path.join(
          csv_preamble + "_t" + str(landmark) + ".csv"
      )
      # Read the CSV file
      df = pd.read_csv(csv_path)
  
      # Convert the DataFrame to a numpy array
      read_array = df.to_numpy()
      return read_array
  
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 6 for calling a global function with example usage: read_csv_landmark(csv_preamble, landmark) and returns: read_array