input: Write a python function to classify text data into sports or politics using SVM and TfidfVectorizer.
  assert classify_article('Soccer player scores winning goal in extra time...')==1
  assert classify_article('Supreme Court rules against the president…')==0
  assert classify_article('Basketball game ends in a tie…')==1
  assert classify_article('President makes statement on the economy…')==0
output: def classify_article(text):
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.svm import SVC
      from sklearn.pipeline import make_pipeline
  
      # Sample training data
      training_texts = [
          "The government has announced new policies...",
          "The president gave a speech on healthcare...",
          "The senator from New York spoke about...",
          "Soccer player scores winning goal in extra time...",
          "Basketball game ends in a tie...",
          "The baseball team won their match...",
      ]
      training_labels = [
          0,  # Politics
          0,  # Politics
          0,  # Politics
          1,  # Sports
          1,  # Sports
          1,  # Sports
      ]
  
      # Create a pipeline with TfidfVectorizer and SVC
      model = make_pipeline(TfidfVectorizer(), SVC())
  
      # Train the model
      model.fit(training_texts, training_labels)
  
      # Classify the input text
      return model.predict([text])[0]