code: def check_arrays_survival(X, y, **kwargs):
      
      event, time = check_y_survival(y)
      kwargs.setdefault("dtype", numpy.float64)
      X = check_array(X, ensure_min_samples=2, **kwargs)
      check_consistent_length(X, event, time)
      return X, event, time
docs: Check that all arrays have consistent first dimensions.
  
      Parameters
      ----------
      X : array-like
          Data matrix containing feature vectors.
  
      y : structured array with two fields
          A structured array containing the binary event indicator
          as first field, and time of event or time of censoring as
          second field.
  
      kwargs : dict
          Additional arguments passed to :func:`sklearn.utils.check_array`.
  
      Returns
      -------
      X : array, shape=[n_samples, n_features]
          Feature vectors.
  
      event : array, shape=[n_samples,], dtype=bool
          Binary event indicator.
  
      time : array, shape=[n_samples,], dtype=float
          Time of event or censoring.