input: Write a python function to train a Decision Tree classifier on the iris dataset and predict the type of fruit for a given input.
  assert predict_fruit([[1, 0, 0.5, 1]]) == 'The fruit is likely to be an apple'
  assert predict_fruit([[5, 3.5, 1.4, 0.2]]) == 'The fruit is likely to be a banana'
  assert predict_fruit([[6, 3, 5, 2]]) == 'The fruit is likely to be an orange'
output: def predict_fruit(input_data):
      from sklearn.datasets import load_iris
      from sklearn.tree import DecisionTreeClassifier
  
      # Load the iris dataset
      iris = load_iris()
      X, y = iris.data, iris.target
  
      # Mapping of iris target to fruit names
      fruit_mapping = {
          0: 'The fruit is likely to be an apple',
          1: 'The fruit is likely to be a banana',
          2: 'The fruit is likely to be an orange'
      }
  
      # Train a Decision Tree classifier
      clf = DecisionTreeClassifier()
      clf.fit(X, y)
  
      # Predict the fruit type for the given input
      prediction = clf.predict(input_data)[0]
      return fruit_mapping[prediction]