class_docstr: Two-Stage Least Squares for Simultaneous equations
  
  Parameters
  ----------
  sys : list
      [endog1, exog1, endog2, exog2,...] It will be of length 2 x M,
      where M is the number of equations endog = exog.
  indep_endog : dict
      A dictionary mapping the equation to the column numbers of the
      the independent endogenous regressors in each equation.
      It is assumed that the system is entered as broken up into
      LHS and RHS. For now, the values of the dict have to be sequences.
      Note that the keys for the equations should be zero-indexed.
  instruments : ndarray
      Array of the exogenous independent variables.
  
  Notes
  -----
  This is unfinished, and the design should be refactored.
  Estimation is done by brute force and there is no exploitation of
  the structure of the system.
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docstr_tok: []
code: def fit(self):
      """ """
      delta = []
      wexog = self.wexog
      endog = self.endog
      for j in range(self._M):
          delta.append(GLS(endog[j], wexog[j]).fit().params)
      return delta
  
file_path: statsmodels/statsmodels/sandbox/sysreg.py
imports: from statsmodels.regression.linear_model import GLS
  import numpy
  from statsmodels.base.model import LikelihoodModelResults
  from scipy import sparse
label_desc: Use image node_id 3 for calling the Sem2SLS obj's underlying member method code with example usage: obj.fit() and returns: delta