class_docstr: Class to hold results from fitting a state space model.
  
  Parameters
  ----------
  model : MLEModel instance
      The fitted model instance
  params : ndarray
      Fitted parameters
  
  Attributes
  ----------
  model : Model instance
      A reference to the model that was fit.
  nobs : float
      The number of observations used to fit the model.
  params : ndarray
      The parameters of the model.
class_docstr_tok: ["Class","to","hold","results","from","fitting","a","state","space","model",".","Parameters","--","--","--","--","--","model",":","MLEModel","instance","The","fitted","model","instance","params",":","ndarray","Fitted","parameters","Attributes","--","--","--","--","--","model",":","Model","instance","A","reference","to","the","model","that","was","fit",".","nobs",":","float","The","number","of","observations","used","to","fit","the","model",".","params",":","ndarray","The","parameters","of","the","model","."]
code: def __init__(self, model, params, scale=1.0):
      self.data = model.data
      self.endog = model.data.orig_endog
  
      super().__init__(model, params, None, scale=scale)
  
      # Save the fixed parameters
      self._has_fixed_params = self.model._has_fixed_params
      self._fixed_params_index = self.model._fixed_params_index
      self._free_params_index = self.model._free_params_index
      # TODO: seems like maybe self.fixed_params should be the dictionary
      # itself, not just the keys?
      if self._has_fixed_params:
          self._fixed_params = self.model._fixed_params.copy()
          self.fixed_params = list(self._fixed_params.keys())
      else:
          self._fixed_params = None
          self.fixed_params = []
      self.param_names = [
          "%s (fixed)" % name if name in self.fixed_params else name
          for name in (self.data.param_names or [])
      ]
  
      # Dimensions
      self.nobs = self.model.nobs
      self.k_params = self.model.k_params
  
      self._rank = None
  
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 1 to create a new StateSpaceMLEResults object from inherited base classes: tsbase with example: obj = StateSpaceMLEResults(model, params, scale)