code: def check_censoring_consistency(censoring, n_samples):
      """Checks if `censoring` has the correct shape and dtype, which is 'uint64'.
  
      Parameters
      ----------
      censoring : `np.ndarray`, shape=(n_samples, 1), dtype="uint64"
          The censoring data.
          Cf. tick.preprocessing.LongitudinalFeaturesLagger
  
      n_samples : `int`
          The expected number of samples.
  
      Returns
      -------
      output : `np.ndarray`, shape=(n_samples, 1), dtype="uint64"
          The censoring data with right shape and dtype.
  
      """
      if censoring.shape != (n_samples,):
          raise ValueError(
              "`censoring` should be a 1-D numpy ndarray of \
              shape (%i,)"
              % n_samples
          )
      return safe_array(censoring, "uint64")
  
imports: import numpy
  import pandas
  from warnings import warn
label_desc: Use image node_id 3 for calling a global function with example usage: check_censoring_consistency(censoring, n_samples) and returns: safe_array