input: Write a python function to train and evaluate a Support Vector Classifier using sklearn on a given dataset.
  assert train_evaluate_svc('dataset.npy')['accuracy'] > 0.8
  assert isinstance(train_evaluate_svc('dataset.npy')['model'], SVC)
  assert 'predictions' in train_evaluate_svc('dataset.npy')
output: def train_evaluate_svc(dataset_path):
      import numpy as np
      from sklearn.model_selection import train_test_split
      from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score
      
      # Load dataset
      data = np.load(dataset_path)
      X, y = data['X'], data['y']
      
      # Split 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=42)
      
      # Train Support Vector Classifier
      model = SVC()
      model.fit(X_train, y_train)
      
      # Evaluate the model
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      
      # Return the results
      return {'model': model, 'accuracy': accuracy, 'predictions': y_pred}