input: Write a python function to load a dataset, perform KMeans clustering, and calculate the silhouette score.
  assert kmeans_clustering_and_silhouette_score('data/X.npy', 5) > 0
  assert isinstance(kmeans_clustering_and_silhouette_score('data/X.npy', 5), float)
  assert kmeans_clustering_and_silhouette_score('data/X.npy', 3) > 0
output: def kmeans_clustering_and_silhouette_score(filepath, n_clusters):
      import numpy as np
      from sklearn.cluster import KMeans
      from sklearn.metrics import silhouette_score
  
      # Load the dataset
      X = np.load(filepath)
  
      # Perform KMeans clustering
      kmeans = KMeans(n_clusters=n_clusters, random_state=42)
      labels = kmeans.fit_predict(X)
  
      # Calculate the silhouette score
      score = silhouette_score(X, labels)
  
      return score