input: Write a function named recommend_products that uses Pandas and a collaborative filtering approach based on past purchasing data to recommend new products to customers. The function should read in a CSV file named product_data.csv, fit a nearest neighbors model to the data, and then make recommendations for each item. The function should print out the recommendations for each item in the dataset.
  assert recommend_products('product_data.csv') == "Recommendations for item 0: [0, 1, 2]\nRecommendations for item 1: [1, 0, 2]\nRecommendations for item 2: [2, 0, 1]"
output: def recommend_products(file_path):
      import pandas as pd
      from sklearn.neighbors import NearestNeighbors
      
      df = pd.read_csv(file_path)
      model = NearestNeighbors(metric='cosine', algorithm='brute')
      model.fit(df)
      
      for i in range(len(df)):
          distances, indices = model.kneighbors([df.iloc[i]], n_neighbors=4)
          recommendations = indices.flatten()[1:]
          print(f"Recommendations for item {i}: {list(recommendations)}")