input: Write a function to create a recommendation system using Pandas and the NearestNeighbors algorithm from scikit-learn to recommend new products to customers based on past purchases. The function should read product data from a CSV file and fit the NearestNeighbors model on the data. It should then make recommendations for each product by finding its nearest neighbors and print the recommendations.
  
  def recommend_products(csv_file_path):
      import pandas as pd
      from sklearn.neighbors import NearestNeighbors
  
      # Read in data
      data = pd.read_csv(csv_file_path)
  
      # Create an item-based collaborative filter
      cf = NearestNeighbors(metric='cosine', algorithm='brute')
      cf.fit(data)
  
      # Make recommendations
      distances, indices = cf.kneighbors(data, n_neighbors=3)
  
      # Print results 
      for i in range(len(data)):
          print('Recommendations for item {}:'.format(i), indices[i])
  
  assert recommend_products('product_data.csv')  # This should print the recommendations based on the data in 'product_data.csv'
output: def recommend_products(csv_file_path):
      import pandas as pd
      from sklearn.neighbors import NearestNeighbors
  
      data = pd.read_csv(csv_file_path)
      cf = NearestNeighbors(metric='cosine', algorithm='brute')
      cf.fit(data)
      distances, indices = cf.kneighbors(data, n_neighbors=3)
      for i in range(len(data)):
          print('Recommendations for item {}:'.format(i), indices[i])