code: def proximal_huber(space, gamma):
      
  
      gamma = float(gamma)
  
      class ProximalHuber(Operator):
  
          """Proximal operator of Huber norm."""
  
          def __init__(self, sigma):
              """Initialize a new instance.
  
              Parameters
              ----------
              sigma : positive float
              """
              self.sigma = float(sigma)
              super(ProximalHuber, self).__init__(domain=space, range=space,
                                                  linear=False)
  
          def _call(self, x, out):
              """Return ``self(x, out=out)``."""
              if isinstance(self.domain, ProductSpace):
                  norm = PointwiseNorm(self.domain, 2)(x)
              else:
                  norm = x.ufuncs.absolute()
  
              mask = norm.ufuncs.less_equal(gamma + self.sigma)
              out[mask] = gamma / (gamma + self.sigma) * x[mask]
  
              mask.ufuncs.logical_not(out=mask)
              sign_x = x.ufuncs.sign()
              out[mask] = x[mask] - self.sigma * sign_x[mask]
  
              return out
  
      return ProximalHuber
docs: Proximal factory of the Huber norm.
  
      Parameters
      ----------
      space : `TensorSpace`
          The domain of the functional
      gamma : float
          The smoothing parameter of the Huber norm functional.
  
      Returns
      -------
      prox_factory : function
          Factory for the proximal operator to be initialized.
  
      See Also
      --------
      odl.solvers.default_functionals.Huber : the Huber norm functional
  
      Notes
      -----
      The proximal operator is given by given by the proximal operator of
      ``1/(2*gamma) * L2 norm`` in points that are ``<= gamma``, and by the
      proximal operator of the l1 norm in points that are ``> gamma``.