input: 
  from typing import List, Tuple
  
  def load_and_process_data(profile_path: str, frame_id: int, threshold: float) -> Tuple[str, str, str]:
      """ This function loads a data file from the given profile path, performs cubic spline interpolation on the data, applies a threshold to remove small values, and generates three plots: a histogram, an interpolated image, and a contour plot. The plots are saved as PDF files.
      >>> load_and_process_data("foam_ball_short.dsa", 230, 25)
      ('histogram.pdf', 'interpolation.pdf', 'contour.pdf')
      """
  
output:     
      import numpy as np
      import matplotlib.pyplot as plt
      from scipy.interpolate import interp1d
  
      # Load data from file
      data = np.loadtxt(profile_path)
  
      # Extract relevant frame
      frame_data = data[data[:, 0] == frame_id]
  
      # Perform cubic spline interpolation
      x = frame_data[:, 1]
      y = frame_data[:, 2]
      f = interp1d(x, y, kind='cubic')
  
      # Generate new x values for interpolation
      x_new = np.linspace(x.min(), x.max(), num=1000)
      y_new = f(x_new)
  
      # Apply threshold to remove small values
      y_new[y_new < threshold] = 0
  
      # Generate histogram plot
      plt.hist(y_new, bins=50)
      plt.title('Histogram')
      histogram_path = 'histogram.pdf'
      plt.savefig(histogram_path)
      plt.close()
  
      # Generate interpolated image plot
      plt.plot(x_new, y_new)
      plt.title('Interpolated Image')
      interpolation_path = 'interpolation.pdf'
      plt.savefig(interpolation_path)
      plt.close()
  
      # Generate contour plot
      plt.tricontourf(x_new, y_new, levels=50)
      plt.title('Contour Plot')
      contour_path = 'contour.pdf'
      plt.savefig(contour_path)
      plt.close()
  
      return histogram_path, interpolation_path, contour_path
  