input: Write a python function to read a dataset, vectorize the sentences using TfidfVectorizer, and train a SVC classifier. 
  assert train_classifier([["The car is a Ford Mustang", "Ford Mustang"]]) is not None
  assert train_classifier([["I love programming", "positive"], ["I hate bugs", "negative"]]) is not None
  assert train_classifier([["Python is great", "positive"], ["Python is hard", "negative"]]) is not None
output: def train_classifier(data):
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.svm import SVC
      from sklearn.pipeline import make_pipeline
      from sklearn.preprocessing import LabelEncoder
      
      # Split the data into texts and labels
      texts, labels = zip(*data)
      
      # Encode the labels
      label_encoder = LabelEncoder()
      encoded_labels = label_encoder.fit_transform(labels)
      
      # Create a pipeline with TfidfVectorizer and SVC
      model = make_pipeline(TfidfVectorizer(), SVC())
      
      # Train the model
      model.fit(texts, encoded_labels)
      
      return model