old_contents: import numpy as np
  from sklearn.utils import check_array
  
  
  def Y_from_censored(T, E=None):
      if T is None:
          return None
      elif T.dtype == [
          ("Event", "?"),
          ("Time", "<f8"),
      ]:  # already processed. Necessary for when d_score() calls score() as in LogNormalCRPScore
          return T
      else:
          T = check_array(T, ensure_2d=False)
          T = T.reshape(T.shape[0])
      if E is None:
          E = np.ones_like(T)
      else:
          E = check_array(E, ensure_2d=False)
          E = E.reshape(E.shape[0])
      Y = np.empty(dtype=[("Event", np.bool), ("Time", np.float64)], shape=T.shape[0])
      Y["Event"] = E.astype(np.bool)
      Y["Time"] = T.astype(np.float64)
      return Y
  
new_contents: import numpy as np
  from sklearn.utils import check_array
  
  
  def Y_from_censored(T, E=None):
      if T is None:
          return None
      if T.dtype == [
          ("Event", "?"),
          ("Time", "<f8"),
      ]:  # already processed. Necessary for when d_score() calls score() as in LogNormalCRPScore
          return T
      T = check_array(T, ensure_2d=False)
      T = T.reshape(T.shape[0])
      if E is None:
          E = np.ones_like(T)
      else:
          E = check_array(E, ensure_2d=False)
          E = E.reshape(E.shape[0])
      Y = np.empty(dtype=[("Event", np.bool), ("Time", np.float64)], shape=T.shape[0])
      Y["Event"] = E.astype(np.bool)
      Y["Time"] = T.astype(np.float64)
      return Y
  
subject: fix linting issues with else and the returns