class_docstr: Computes lorentzian
      
docstr: generates a lorentzian model instance from a list of coefficients
code: def ForwardFactory(self, coeffs):
      """generates a lorentzian model instance from a list of coefficients"""
      a1, a2, a3, A0, E0, G0, n = coeffs
  
      def forward_lorentzian(evalpts):
          """a lorentzian peak over a 1D numpy array
  with (a1,a2,a3,A0,E0,G0,n) = (%s,%s,%s,%s,%s,%s,%s)""" % (
              a1,
              a2,
              a3,
              A0,
              E0,
              G0,
              n,
          )
          return self.evaluate((a1, a2, a3, A0, E0, G0, n), evalpts)
  
      return forward_lorentzian
  
file_path: mystic/mystic/models/lorentzian.py
imports: from .abstract_model import AbstractModel
  from numpy import sum
  from numpy import array, pi, asarray, arange
  import random
  from numpy import histogram
label_desc: Use image node_id 3 for calling the Lorentzian obj's underlying member method code with example usage: obj.ForwardFactory(coeffs) and returns: forward_lorentzian, self