input: Write a python function to train a K-Nearest Neighbors classifier on the Iris dataset and make predictions.
  assert len(train_knn_classifier()[0]) == len(load_iris(return_X_y=True)[0]) * 0.3
  assert isinstance(train_knn_classifier()[1], np.ndarray)
  assert len(train_knn_classifier()[1]) == len(load_iris(return_X_y=True)[1]) * 0.3
output: from sklearn.datasets import load_iris
  from sklearn.model_selection import train_test_split
  from sklearn.neighbors import KNeighborsClassifier
  import numpy as np
  
  def train_knn_classifier():
      # Load the iris dataset
      iris = load_iris()
      X, y = iris.data, iris.target
      
      # Split the dataset into 70% training and 30% testing
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
      
      # Train the K-Nearest Neighbors classifier
      knn = KNeighborsClassifier(n_neighbors=3)
      knn.fit(X_train, y_train)
      
      # Make predictions on the test set
      predictions = knn.predict(X_test)
      
      return X_test, predictions