input: Write a python function to create an item-based collaborative filter using the NearestNeighbors from sklearn and make recommendations. 
  assert recommender('product_data.csv', 3) == [[0, 1, 2], [1, 2, 0], [2, 0, 1]]
  assert recommender('another_product_data.csv', 2) == [[0, 3], [1, 2], [2, 0], [3, 1]]
output: def recommender(file_path, n_neighbors):
      import pandas as pd
      from sklearn.neighbors import NearestNeighbors
      
      # Load the data
      data = pd.read_csv(file_path, index_col=0)
      
      # Fit the NearestNeighbors model
      model = NearestNeighbors(n_neighbors=n_neighbors, algorithm='auto', metric='cosine')
      model.fit(data)
      
      # Find the nearest neighbors for each item
      distances, indices = model.kneighbors(data)
      
      # Return the indices of the nearest neighbors
      return indices.tolist()