input: Write a function to load a configuration file and libraries, import necessary modules, define two custom colormaps, load a data file, perform cubic spline interpolation on the data, apply a threshold to remove small values, and generate three plots: a histogram, an interpolated image, and a contour plot. The function should return whether the plots were successfully generated. If the plots are successfully generated, return True; otherwise, return False.
  
  assert generate_plots("foam_ball_short.dsa", 230, 1)==True
  assert generate_plots("invalid_file.dsa", 230, 1)==False
  assert generate_plots("foam_ball_short.dsa", 999, 1)==False
  assert generate_plots("foam_ball_short.dsa", 230, 999)==False
  assert generate_plots("foam_ball_short.dsa", 230, 1, min_threshold=50)==True
output: def generate_plots(file_path, threshold, min_threshold):
      try:
          import numpy as np
          import matplotlib.pyplot as plt
          from scipy.interpolate import griddata
          from scipy.ndimage import gaussian_filter
  
          # Load data
          data = np.loadtxt(file_path)
          x, y, z = data[:, 0], data[:, 1], data[:, 2]
  
          # Define custom colormaps
          cmap1 = plt.cm.get_cmap('viridis').copy()
          cmap2 = plt.cm.get_cmap('plasma').copy()
  
          # Perform cubic spline interpolation
          grid_x, grid_y = np.mgrid[min(x):max(x):100j, min(y):max(y):100j]
          grid_z = griddata((x, y), z, (grid_x, grid_y), method='cubic')
  
          # Apply threshold to remove small values
          grid_z[grid_z < min_threshold] = np.nan
  
          # Generate plots
          plt.figure(figsize=(15, 5))
  
          # Histogram
          plt.subplot(131)
          plt.hist(z, bins=50, color='blue', alpha=0.7)
          plt.title('Histogram')
  
          # Interpolated image
          plt.subplot(132)
          plt.imshow(grid_z.T, extent=(min(x), max(x), min(y), max(y)), origin='lower', cmap=cmap1)
          plt.colorbar()
          plt.title('Interpolated Image')
  
          # Contour plot
          plt.subplot(133)
          plt.contourf(grid_x, grid_y, grid_z, levels=20, cmap=cmap2)
          plt.colorbar()
          plt.title('Contour Plot')
  
          plt.tight_layout()
          plt.show()
  
          return True
      except Exception as e:
          return False