input: Write a python function to train a Support Vector Classifier with a linear kernel and return the model's probability estimates for given inputs.
  assert isinstance(train_and_predict_svc(X_train, y_train, X_test), list)
  assert len(train_and_predict_svc(X_train, y_train, X_test)) == len(X_test)
  assert all(0 <= prob <= 1 for prob in train_and_predict_svc(X_train, y_train, X_test))
output: def train_and_predict_svc(X_train, y_train, X_test):
      from sklearn.svm import SVC
      from sklearn.preprocessing import StandardScaler
      from sklearn.pipeline import make_pipeline
      
      # Create a pipeline that standardizes, then trains a SVC
      model = make_pipeline(StandardScaler(), SVC(kernel='linear', probability=True))
      
      # Fit the model
      model.fit(X_train, y_train)
      
      # Predict probabilities
      probabilities = model.predict_proba(X_test)
      
      # Return the probabilities of the positive class
      return probabilities[:, 1].tolist()