class_docstr: Proximal operator of Slope penalization.
  This penalization is particularly relevant for feature selection, in
  generalized linear models, when features correlation is not too high.
  
  Parameters
  ----------
  strength : `float`
      Level of penalization
  
  fdr : `float`, default=0.6
      Desired False Discovery Rate for detection of non-zeros in
      the coefficients.
      Must be between 0 and 1.
  
  range : `tuple` of two `int`, default=`None`
      Range on which the prox is applied. If `None` then the prox is
      applied on the whole vector
  
  Attributes
  ----------
  weights : `np.array`, shape=(n_coeffs,)
      The weights used in the penalization. They are automatically
      setted, depending on the ``weights_type`` and ``fdr``
      parameters.
  
  dtype : `{'float64', 'float32'}`
      Type of the arrays used.
  
  Notes
  -----
  Uses the stack-based algorithm for FastProxL1 from
  
  * SLOPE--Adaptive Variable Selection via Convex Optimization, by
    Bogdan, M. and Berg, E. van den and Sabatti, C. and Su, W. and Candes, E. J.
    arXiv preprint arXiv:1407.3824, 2014
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docstr: Returns the value of the penalization at ``coeffs``
  
  Parameters
  ----------
  coeffs : `numpy.ndarray`, shape=(n_coeffs,)
      The value of the penalization is computed at this point
  
  Returns
  -------
  output : `float`
      Value of the penalization at ``coeffs``
docstr_tok: ["Returns","the","value","of","the","penalization","at","``","coeffs","``","Parameters","--","--","--","--","--","coeffs",":","`","numpy.ndarray","`",",","shape=","(","n_coeffs",",",")","The","value","of","the","penalization","is","computed","at","this","point","Returns","--","--","--","-","output",":","`","float","`","Value","of","the","penalization","at","``","coeffs","``"]
code: def value(self, coeffs: np.ndarray) -> float:
      """
      Returns the value of the penalization at ``coeffs``
  
      Parameters
      ----------
      coeffs : `numpy.ndarray`, shape=(n_coeffs,)
          The value of the penalization is computed at this point
  
      Returns
      -------
      output : `float`
          Value of the penalization at ``coeffs``
      """
      return self._prox.value(coeffs)
  
file_path: tick/tick/prox/prox_slope.py
imports: from tick.prox.base import Prox
  import numpy
  from .build.prox import ProxSlopeDouble
  from .build.prox import ProxSlopeFloat
label_desc: Use image node_id 3 for calling the ProxSlope obj's underlying member method code with example usage: obj.value(coeffs) and returns: self