input: Write a python function to train a KMeans model on a given dataset and identify potential anomalies.
  assert identify_anomalies('data.csv')==[1, 0, 1, 1, 0, 1]
  assert identify_anomalies('another_data.csv')==[0, 0, 1, 0, 1, 1]
  assert identify_anomalies('yet_another_data.csv')==[1, 1, 0, 1, 0, 0]
output: def identify_anomalies(file_path):
      import pandas as pd
      from sklearn.cluster import KMeans
      from sklearn.preprocessing import StandardScaler
  
      # Load dataset
      data = pd.read_csv(file_path)
  
      # Standardize the data
      scaler = StandardScaler()
      data_scaled = scaler.fit_transform(data)
  
      # Train KMeans model
      kmeans = KMeans(n_clusters=2, random_state=42)
      kmeans.fit(data_scaled)
  
      # Identify anomalies (points assigned to the smaller cluster)
      cluster_sizes = pd.Series(kmeans.labels_).value_counts()
      anomaly_cluster = cluster_sizes.idxmin()
      anomalies = (kmeans.labels_ == anomaly_cluster).astype(int)
  
      return anomalies.tolist()