class_docstr: Least-squares loss for linear regression. This class gives first
  order information (gradient and loss) for this model and can be passed
  to any solver through the solver's ``set_model`` method.
  
  Given training data :math:`(x_i, y_i) \in \mathbb R^d \times \mathbb R`
  for :math:`i=1, \ldots, n`, this model considers a goodness-of-fit
  
  .. math::
      f(w, b) = \frac 1n \sum_{i=1}^n \ell(y_i, b + x_i^\top w),
  
  where :math:`w \in \mathbb R^d` is a vector containing the model-weights,
  :math:`b \in \mathbb R` is the intercept (used only whenever
  ``fit_intercept=True``) and
  :math:`\ell : \mathbb R^2 \rightarrow \mathbb R` is the loss given by
  
  .. math::
      \ell(y, y') = \frac 12 (y - y')^2
  
  for :math:`y, y' \in \mathbb R`. Data is passed to this model through the
  ``fit(X, y)`` method where X is the features matrix (dense or sparse) and
  y is the vector of labels.
  
  Parameters
  ----------
  fit_intercept : `bool`
      If `True`, the model uses an intercept
  
  Attributes
  ----------
  features : {`numpy.ndarray`, `scipy.sparse.csr_matrix`}, shape=(n_samples, n_features)
      The features matrix, either dense or sparse
  
  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
  
  dtype : `{'float64', 'float32'}`
      Type of the data arrays used.
  
  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 _grad(self, coeffs: np.ndarray, out: np.ndarray) -> None:
      self._model.grad(coeffs, out)
  
file_path: tick/tick/linear_model/model_linreg.py
imports: import numpy
  from numpy.linalg import svd
  from tick.base_model import ModelGeneralizedLinear, ModelFirstOrder, ModelLipschitz
  from .build.linear_model import ModelLinRegDouble
  from .build.linear_model import ModelLinRegFloat
label_desc: Use image node_id 3 for calling the ModelLinReg obj's underlying member method code with example usage: obj._grad(coeffs, out) without return types