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: Create a MixedLMParams object from each parameter component.
  
  Parameters
  ----------
  fe_params : array_like
      The fixed effects parameter (a 1-dimensional array).  If
      None, there are no fixed effects.
  cov_re : array_like
      The random effects covariance matrix (a square, symmetric
      2-dimensional array).
  cov_re_sqrt : array_like
      The Cholesky (lower triangular) square root of the random
      effects covariance matrix.
  vcomp : array_like
      The variance component parameters.  If None, there are no
      variance components.
  
  Returns
  -------
  A MixedLMParams object.
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code: def from_components(
      fe_params=None, cov_re=None, cov_re_sqrt=None, vcomp=None
  ):
      """
      Create a MixedLMParams object from each parameter component.
  
      Parameters
      ----------
      fe_params : array_like
          The fixed effects parameter (a 1-dimensional array).  If
          None, there are no fixed effects.
      cov_re : array_like
          The random effects covariance matrix (a square, symmetric
          2-dimensional array).
      cov_re_sqrt : array_like
          The Cholesky (lower triangular) square root of the random
          effects covariance matrix.
      vcomp : array_like
          The variance component parameters.  If None, there are no
          variance components.
  
      Returns
      -------
      A MixedLMParams object.
      """
  
      if vcomp is None:
          vcomp = np.empty(0)
      if fe_params is None:
          fe_params = np.empty(0)
      if cov_re is None and cov_re_sqrt is None:
          cov_re = np.empty((0, 0))
  
      k_fe = len(fe_params)
      k_vc = len(vcomp)
      k_re = (
          cov_re.shape[0]
          if cov_re is not None
          else cov_re_sqrt.shape[0]
      )
  
      pa = MixedLMParams(k_fe, k_re, k_vc)
      pa.fe_params = fe_params
      if cov_re_sqrt is not None:
          pa.cov_re = np.dot(cov_re_sqrt, cov_re_sqrt.T)
      elif cov_re is not None:
          pa.cov_re = cov_re
  
      pa.vcomp = 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 3 for calling the MixedLMParams obj's underlying member method code with example usage: obj.from_components(fe_params, cov_re, cov_re_sqrt, vcomp) and returns: pa