code: def _operator_norms(L):
      
      L_norms = []
      for Li in L:
          if np.isscalar(Li):
              L_norms.append(float(Li))
          elif isinstance(Li, Operator):
              L_norms.append(Li.norm(estimate=True))
          else:
              raise TypeError('invalid entry {!r} in `L`'.format(Li))
      return L_norms
docs: Get operator norms if needed.
  
      Parameters
      ----------
      L : sequence of `Operator` or float
          The operators or the norms of the operators that are used in the
          `douglas_rachford_pd` method. For `Operator` entries, the norm
          is computed with ``Operator.norm(estimate=True)``.