code: def prepare_trend_data(polynomial_trend, k_trend, nobs, offset=1):
      # Cache the arrays for calculating the intercept from the trend
      # components
      time_trend = np.arange(offset, nobs + offset)
      trend_data = np.zeros((nobs, k_trend))
      i = 0
      for k in polynomial_trend.nonzero()[0]:
          if k == 0:
              trend_data[:, i] = np.ones(
                  nobs,
              )
          else:
              trend_data[:, i] = time_trend**k
          i += 1
  
      return trend_data
  
imports: import numpy
  from scipy.linalg import solve_sylvester
  import pandas
  from statsmodels.compat.pandas import Appender
  from statsmodels.tools.data import _is_using_pandas
  from scipy.linalg.blas import find_best_blas_type
  from .None import _initialization, _representation, _kalman_filter, _kalman_smoother, _simulation_smoother, _cfa_simulation_smoother, _tools
label_desc: Use image node_id 30 for calling a global function with example usage: prepare_trend_data(polynomial_trend, k_trend, nobs, offset) and returns: trend_data