class_docstr: Partial likelihood of the Cox regression model (proportional
  hazards).
  This class gives first order information (gradient and loss) for
  this model.
  
  Attributes
  ----------
  features : `numpy.ndarray`, shape=(n_samples, n_features), (read-only)
      The features matrix
  
  times : `numpy.ndarray`, shape = (n_samples,), (read-only)
      Obverved times
  
  censoring : `numpy.ndarray`, shape = (n_samples,), (read-only)
      Boolean indicator of censoring of each sample.
      ``True`` means true failure, namely non-censored time
  
  n_samples : `int` (read-only)
      Number of samples
  
  n_features : `int` (read-only)
      Number of features
  
  n_failures : `int` (read-only)
      Number of true failure times
  
  n_coeffs : `int` (read-only)
      Total number of coefficients of the model
  
  censoring_rate : `float`
      The censoring_rate (percentage of ???)
  
  Notes
  -----
  There is no intercept in this model
code: def _loss(self, coeffs: np.ndarray) -> float:
      return self._model.loss(coeffs)
  
file_path: tick/tick/survival/model_coxreg_partial_lik.py
imports: import numpy
  from tick.base_model import Model, ModelFirstOrder
  from tick.preprocessing.utils import safe_array
  from .build.survival import ModelCoxRegPartialLikDouble
  from .build.survival import ModelCoxRegPartialLikFloat
label_desc: Use image node_id 5 for calling the ModelCoxRegPartialLik obj's underlying member method code with example usage: obj._loss(coeffs) and returns: self