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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code: def __init__(self, k_fe, k_re, k_vc):
      self.k_fe = k_fe
      self.k_re = k_re
      self.k_re2 = k_re * (k_re + 1) // 2
      self.k_vc = k_vc
      self.k_tot = self.k_fe + self.k_re2 + self.k_vc
      self._ix = np.tril_indices(self.k_re)
  
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 1 to create a new MixedLMParams object  with example: obj = MixedLMParams(k_fe, k_re, k_vc)