input: Write a python function to create, train and predict using a K-Nearest Neighbors classifier.
  assert knn_classifier(data.data, data.target, test_size=0.25, n_neighbors=3, metric='minkowski', p=2) is not None
  assert knn_classifier(data.data, data.target, test_size=0.30, n_neighbors=5, metric='minkowski', p=2) is not None
output: def knn_classifier(X, y, test_size=0.25, n_neighbors=3, metric='minkowski', p=2):
      from sklearn.model_selection import train_test_split
      from sklearn.neighbors import KNeighborsClassifier
      from sklearn import datasets
  
      # Split the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)
  
      # Initialize the KNN classifier with the given parameters
      knn = KNeighborsClassifier(n_neighbors=n_neighbors, metric=metric, p=p)
  
      # Train the classifier
      knn.fit(X_train, y_train)
  
      # Make predictions
      predictions = knn.predict(X_test)
  
      return predictions