input: Write a python function to perform k-means clustering on a dataset and return the labels.
  assert kmeans_clustering('data.csv', 3).tolist()==[0, 1, 2, 0, 1, 2]
  assert kmeans_clustering('another_data.csv', 4).tolist()==[0, 1, 2, 3, 0, 1, 2, 3]
  assert len(set(kmeans_clustering('yet_another_data.csv', 5)))==5
output: def kmeans_clustering(file_path, num_clusters):
      import pandas as pd
      from sklearn.cluster import KMeans
      
      # Load the dataset
      data = pd.read_csv(file_path)
      
      # Assume the dataset is numeric and doesn't require preprocessing
      kmeans = KMeans(n_clusters=num_clusters, random_state=0).fit(data)
      
      return kmeans.labels_