class_docstr: Linear regression model with individual intercepts.
  This class gives first order information (gradient and loss) for this model
  
  Parameters
  ----------
  fit_intercept : `bool`, default=`True`
      If `True`, the model uses an intercept
  
  Attributes
  ----------
  features : `numpy.ndarray`, shape=(n_samples, n_features) (read-only)
      The features matrix
  
  labels : `numpy.ndarray`, shape=(n_samples,) (read-only)
      The labels vector
  
  n_samples : `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
  
  n_threads : `int`, default=1 (read-only)
      Number of threads used for parallel computation.
  
      * if ``int <= 0``: the number of threads available on
        the CPU
      * otherwise the desired number of threads
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code: def _loss(self, coeffs: np.ndarray) -> float:
      return self._model.loss(coeffs)
  
file_path: tick/tick/robust/model_linreg_with_intercepts.py
imports: import numpy
  from numpy.linalg import svd
  from tick.base_model import ModelFirstOrder, ModelLipschitz
  from .base import ModelGeneralizedLinearWithIntercepts
  from .build.robust import ModelLinRegWithInterceptsDouble
label_desc: Use image node_id 4 for calling the ModelLinRegWithIntercepts obj's underlying member method code with example usage: obj._loss(coeffs) and returns: self