code: def apply_perturbations(i, j, X, increase, theta, clip_min, clip_max):
    
    warnings.warn(
        "This function is dead code and will be removed on or after 2019-07-18")
  
    # perturb our input sample
    if increase:
      X[0, i] = np.minimum(clip_max, X[0, i] + theta)
      X[0, j] = np.minimum(clip_max, X[0, j] + theta)
    else:
      X[0, i] = np.maximum(clip_min, X[0, i] - theta)
      X[0, j] = np.maximum(clip_min, X[0, j] - theta)
  
    return X
docs: TensorFlow implementation for apply perturbations to input features based
    on salency maps
    :param i: index of first selected feature
    :param j: index of second selected feature
    :param X: a matrix containing our input features for our sample
    :param increase: boolean; true if we are increasing pixels, false otherwise
    :param theta: delta for each feature adjustment
    :param clip_min: mininum value for a feature in our sample
    :param clip_max: maximum value for a feature in our sample
    : return: a perturbed input feature matrix for a target class