class_docstr: This class represents a parameter state for a mixed linear model.
  
  Parameters
  ----------
  k_fe : int
      The number of covariates with fixed effects.
  k_re : int
      The number of covariates with random coefficients (excluding
      variance components).
  k_vc : int
      The number of variance components parameters.
  
  Notes
  -----
  This object represents the parameter state for the model in which
  the scale parameter has been profiled out.
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docstr: Return the model parameters packed into a single vector.
  
  Parameters
  ----------
  use_sqrt : bool
      If True, the Cholesky square root of `cov_re` is
      included in the packed result.  Otherwise the
      lower triangle of `cov_re` is included.
  has_fe : bool
      If True, the fixed effects parameters are included
      in the packed result, otherwise they are omitted.
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code: def get_packed(self, use_sqrt, has_fe=False):
      """
      Return the model parameters packed into a single vector.
  
      Parameters
      ----------
      use_sqrt : bool
          If True, the Cholesky square root of `cov_re` is
          included in the packed result.  Otherwise the
          lower triangle of `cov_re` is included.
      has_fe : bool
          If True, the fixed effects parameters are included
          in the packed result, otherwise they are omitted.
      """
  
      if self.k_re > 0:
          if use_sqrt:
              try:
                  L = np.linalg.cholesky(self.cov_re)
              except np.linalg.LinAlgError:
                  L = np.diag(np.sqrt(np.diag(self.cov_re)))
              cpa = L[self._ix]
          else:
              cpa = self.cov_re[self._ix]
      else:
          cpa = np.zeros(0)
  
      if use_sqrt:
          vcomp = np.sqrt(self.vcomp)
      else:
          vcomp = self.vcomp
  
      if has_fe:
          pa = np.concatenate((self.fe_params, cpa, vcomp))
      else:
          pa = np.concatenate((cpa, vcomp))
  
      return pa
  
imports: import warnings
  import numpy
  import pandas
  import patsy
  from scipy import sparse
  from scipy.stats.distributions import norm
  from statsmodels.base._penalties import Penalty
  import statsmodels.base.model
  from statsmodels.tools import data
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.tools.sm_exceptions import ConvergenceWarning
label_desc: Use image node_id 5 for calling the MixedLMParams obj's underlying member method code with example usage: obj.get_packed(use_sqrt, has_fe) and returns: pa