input: Write a python function to load the diabetes dataset, fit a linear regression model, and make a prediction for a given input.
  assert predict_diabetes([[5.1, 3.3, 1.7, 0.5]]) is not None
  assert predict_diabetes([[0.1, -0.2, 0.3, 0.4]]) is not None
  assert predict_diabetes([[1.5, 2.3, 3.7, 4.5]]) is not None
output: def predict_diabetes(input_data):
      from sklearn.datasets import load_diabetes
      from sklearn.linear_model import LinearRegression
      from sklearn.model_selection import train_test_split
  
      # Load the diabetes dataset
      diabetes = load_diabetes()
      X, y = diabetes.data, diabetes.target
  
      # Split the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
  
      # Fit the linear regression model
      model = LinearRegression()
      model.fit(X_train, y_train)
  
      # Make predictions for the input data
      prediction = model.predict(input_data)
      
      return prediction