code: def makeTBATSGMatrix(
      k_vector,
      alpha,
      adj_beta,
      beta,
      gamma_one_v,
      gamma_two_v,
      p,
      q,
      tau,
  ):
      # g = (alpha, beta, gamma_bold, 1, 0_{p-1}, 1, 0_{q-1})
      g = np.zeros((1 + adj_beta + 2 * np.sum(k_vector) + p + q, 1))
  
      g[0, 0] = alpha
  
      if beta is not None:
          g[1, 0] = beta
  
      gamma_bold = np.ones((1, 2 * np.sum(k_vector)))
      endPos = 0
      for k, gamma_one, gamma_two in zip(
          k_vector, gamma_one_v, gamma_two_v
      ):
          gamma_bold[0, endPos : endPos + k] = gamma_one
          gamma_bold[0, endPos + k : endPos + 2 * k] = gamma_two
          endPos += 2 * k
  
      g[
          1 + adj_beta : 1 + adj_beta + gamma_bold.shape[1], 0
      ] = gamma_bold
  
      if p != 0:
          g[1 + adj_beta + tau, 0] = 1
      if q != 0:
          g[1 + adj_beta + tau + p, 0] = 1
  
      return g, gamma_bold
  
imports: import warnings
  from functools import partial
  import numpy
  import pandas
  import scipy.linalg
  import statsmodels.api
  from numba import njit
  from numpy.polynomial.polynomial import Polynomial
  from scipy.special import inv_boxcox
  from scipy.stats import boxcox
  from scipy.optimize import minimize
  from statsmodels.tsa.stattools import adfuller
  from .arima import auto_arima_f
  from .utils import NOGIL, CACHE
label_desc: Use image node_id 6 for calling a global function with example usage: makeTBATSGMatrix(k_vector, alpha, adj_beta, beta, gamma_one_v, gamma_two_v, p, q, tau) and returns: g,  gamma_bold