class_docstr: Discrete-time Self Control Case Series (SCCS) likelihood. This class
  provides first order information (gradient and loss) model.
  
  Parameters
  ----------
  n_intervals : `int`
      Number of time intervals observed for each sample.
  
  n_lags : `numpy.ndarray`, shape=(n_features,), dtype="uint64"
      Number of lags per feature. The model will regress labels on the last
      observed values of the features over the corresponding `n_lags` time
      intervals. `n_lags` values must be between 0 and `n_intervals` - 1.
  
  Attributes
  ----------
  features : `list` of `numpy.ndarray` or `list` of `scipy.sparse.csr_matrix`,
      list of length n_cases, each element of the list of
      shape=(n_intervals, n_features)
      The list of features matrices.
  
  labels : `list` of `numpy.ndarray`,
      list of length n_cases, each element of the list of
      shape=(n_intervals,)
      The labels vector
  
  censoring : `numpy.ndarray`, shape=(n_cases,), dtype="uint64"
      The censoring data. This array should contain integers in
      [1, n_intervals]. If the value i is equal to n_intervals, then there
      is no censoring for sample i. If censoring = c < n_intervals, then
      the observation of sample i is stopped at interval c, that is, the
      row c - 1 of the corresponding matrix. The last n_intervals - c rows
      are then set to 0.
  
  n_cases : `int` (read-only)
      Number of samples
  
  n_features : `int` (read-only)
      Number of features
  
  n_coeffs : `int` (read-only)
      Total number of coefficients of the model
code: def _rand_max(self):
      return self._model.get_rand_max()
  
file_path: tick/tick/survival/model_sccs.py
imports: import numpy
  from tick.base_model import ModelFirstOrder, ModelLipschitz
  from .build.survival import ModelSCCS
  from tick.preprocessing.utils import check_longitudinal_features_consistency, check_censoring_consistency
label_desc: Use image node_id 9 for calling the ModelSCCS obj's underlying member method code with example usage: obj._rand_max() and returns: self