class_docstr: The base class for a first order solver. It defines methods for
  setting a model (giving first order information) and a proximal
  operator
  
  In only deals with verbosing information, and setting parameters
  
  Parameters
  ----------
  step : `float` default=None
      Step-size of the algorithm
  
  tol : `float`, default=0
      The tolerance of the solver (iterations stop when the stopping
      criterion is below it). By default the solver does ``max_iter``
      iterations
  
  max_iter : `int`
      Maximum number of iterations of the solver
  
  verbose : `bool`, default=True
      If `True`, we verbose things, otherwise the solver does not
      print anything (but records information in history anyway)
  
  print_every : `int`, default = 10
      Print history information every time the iteration number is a
      multiple of ``print_every``
  
  record_every : `int`, default = 1
      Information along iteration is recorded in history each time the
      iteration number of a multiple of ``record_every``
  
  Attributes
  ----------
  model : `Model`
      The model to solve
  
  prox : `Prox`
      Proximal operator to solve
  
  dtype : `{'float64', 'float32'}`, default='float64'
      Type of the arrays used. This value is set from model and prox dtypes.
  
  Notes
  -----
  This class should not be used by end-users
docstr: Compute the objective function
  
  Parameters
  ----------
  coeffs : `np.array`, shape=(n_coeffs,)
      Point where the objective is computed
  
  loss : `float`, default=`None`
      Gives the value of the loss if already known (allows to
      avoid its computation in some cases)
  
  Returns
  -------
  output : `float`
      Value of the objective at given ``coeffs``
docstr_tok: ["Compute","the","objective","function","Parameters","--","--","--","--","--","coeffs",":","`","np.array","`",",","shape=","(","n_coeffs",",",")","Point","where","the","objective","is","computed","loss",":","`","float","`",",","default=","`","None","`","Gives","the","value","of","the","loss","if","already","known","(","allows","to","avoid","its","computation","in","some","cases",")","Returns","--","--","--","-","output",":","`","float","`","Value","of","the","objective","at","given","``","coeffs","``"]
code: def objective(self, coeffs, loss: float = None):
      """Compute the objective function
  
      Parameters
      ----------
      coeffs : `np.array`, shape=(n_coeffs,)
          Point where the objective is computed
  
      loss : `float`, default=`None`
          Gives the value of the loss if already known (allows to
          avoid its computation in some cases)
  
      Returns
      -------
      output : `float`
          Value of the objective at given ``coeffs``
      """
      if self.prox is None:
          prox_value = 0
      else:
          prox_value = self.prox.value(coeffs)
  
      if loss is None:
          return self.model.loss(coeffs) + prox_value
      else:
          return loss + prox_value
  
file_path: tick/tick/solver/base/first_order.py
imports: import numpy
  from .None import Solver
  from tick.base_model import Model
  from tick.prox.base import Prox
label_desc: Use image node_id 8 for calling the SolverFirstOrder obj's underlying member method code with example usage: obj.objective(coeffs, loss) and returns: unknown, unknown