class_docstr: This is a temporary base model from ETS, here I just copy everything I need
  from statespace.mlemodel.MLEModel
class_docstr_tok: ["This","is","a","temporary","base","model","from","ETS",",","here","I","just","copy","everything","I","need","from","statespace.mlemodel.MLEModel"]
docstr: Fit the model with some parameters subject to equality constraints.
  
  Parameters
  ----------
  constraints : dict
      Dictionary of constraints, of the form `param_name: fixed_value`.
      See the `param_names` property for valid parameter names.
  start_params : array_like, optional
      Initial guess of the solution for the loglikelihood maximization.
      If None, the default is given by Model.start_params.
  **fit_kwds : keyword arguments
      fit_kwds are used in the optimization of the remaining parameters.
  
  Returns
  -------
  results : Results instance
  
  Examples
  --------
  >>> mod = sm.tsa.SARIMAX(endog, order=(1, 0, 1))
  >>> res = mod.fit_constrained({'ar.L1': 0.5})
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code: def fit_constrained(self, constraints, start_params=None, **fit_kwds):
      """
      Fit the model with some parameters subject to equality constraints.
  
      Parameters
      ----------
      constraints : dict
          Dictionary of constraints, of the form `param_name: fixed_value`.
          See the `param_names` property for valid parameter names.
      start_params : array_like, optional
          Initial guess of the solution for the loglikelihood maximization.
          If None, the default is given by Model.start_params.
      **fit_kwds : keyword arguments
          fit_kwds are used in the optimization of the remaining parameters.
  
      Returns
      -------
      results : Results instance
  
      Examples
      --------
      >>> mod = sm.tsa.SARIMAX(endog, order=(1, 0, 1))
      >>> res = mod.fit_constrained({'ar.L1': 0.5})
      """
      with self.fix_params(constraints):
          res = self.fit(start_params, **fit_kwds)
      return res
  
imports: from collections import OrderedDict
  import contextlib
  import warnings
  import numpy
  import pandas
  from scipy.stats import norm
  from statsmodels.base.data import PandasData
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.tools.eval_measures import aic, aicc, bic, hqic
  from statsmodels.tools.sm_exceptions import PrecisionWarning
  from statsmodels.tools.numdiff import _get_epsilon, approx_fprime, approx_fprime_cs, approx_hess_cs
  from statsmodels.tools.tools import pinv_extended
  import statsmodels.tsa.base.tsa_model
  from statsmodels.tsa.statespace.tools import _safe_cond
label_desc: Use image node_id 7 for calling the StateSpaceMLEModel obj's underlying member method code with example usage: obj.fit_constrained(constraints, start_params) and returns: res