input: Write a python function to fit a logistic regression model and make predictions using LabelEncoder for the 'educationYears' feature.
  assert predict_age([15], [30])==30
  assert predict_age([16, 17], [35, 40])==[35, 40]
  assert predict_age([10, 12, 14], [20, 25, 30])==[20, 25, 30]
output: def predict_age(educationYears, ages):
      from sklearn.linear_model import LogisticRegression
      from sklearn.preprocessing import LabelEncoder
      import numpy as np
  
      # Label encode the educationYears
      le = LabelEncoder()
      educationYears_encoded = le.fit_transform(educationYears)
  
      # Reshape for the model
      educationYears_encoded = np.array(educationYears_encoded).reshape(-1, 1)
      ages = np.array(ages)
  
      # Fit the logistic regression model
      model = LogisticRegression()
      model.fit(educationYears_encoded, ages)
  
      # Predict ages
      predicted_ages = model.predict(educationYears_encoded)
  
      return predicted_ages.tolist() if len(predicted_ages) > 1 else predicted_ages[0]