class_docstr: Nested Multinomial Logit with Random Utility 2 parameterization
  
      
code: def __init__(self, endog, exog, tree, paramsind):
      self.endog = endog
      self.datadict = exog
      self.tree = tree
      self.paramsind = paramsind
  
      self.branchsum = ""
      self.probs = {}
  
file_path: statsmodels/statsmodels/sandbox/regression/runmnl.py
imports: import numpy
  import numpy.lib.recfunctions
  from scipy import optimize
label_desc: Use image node_id 1 to create a new RU2NMNL object  with example: obj = RU2NMNL(endog, exog, tree, paramsind)