class_docstr: Proximal operator of the weighted L1 norm (weighted
  soft-thresholding)
  
  Parameters
  ----------
  strength : `float`
      Level of L1 penalization
  
  weights : `numpy.ndarray`, shape=(n_coeffs,)
      The weights to be used in the L1 penalization
  
  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
  
  positive : `bool`, default=`False`
      If True, apply L1 penalization together with a projection
      onto the set of vectors with non-negative entries
  
  Attributes
  ----------
  dtype : `{'float64', 'float32'}`
      Type of the arrays used.
class_docstr_tok: ["Proximal","operator","of","the","weighted","L1","norm","(","weighted","soft-thresholding",")","Parameters","--","--","--","--","--","strength",":","`","float","`","Level","of","L1","penalization","weights",":","`","numpy.ndarray","`",",","shape=","(","n_coeffs",",",")","The","weights","to","be","used","in","the","L1","penalization","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","positive",":","`","bool","`",",","default=","`","False","`","If","True",",","apply","L1","penalization","together","with","a","projection","onto","the","set","of","vectors","with","non-negative","entries","Attributes","--","--","--","--","--","dtype",":","`","{","'float64","'",",","'float32","'","}","`","Type","of","the","arrays","used","."]
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):
      """
      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_l1w.py
imports: import numpy
  from .base import Prox
  from .build.prox import ProxL1wDouble
  from .build.prox import ProxL1wFloat
label_desc: Use image node_id 3 for calling the ProxL1w obj's underlying member method code with example usage: obj.value(coeffs) and returns: self