code: def __init__(self, endog):
      # Model order
      k_states = k_posdef = 2
  
      # Initialize the statespace
      super().__init__(
          endog,
          k_states=k_states,
          k_posdef=k_posdef,
          initialization="approximate_diffuse",
          loglikelihood_burn=k_states,
      )
  
      # Initialize the matrices
      self.ssm["design"] = np.array([1, 0])
      self.ssm["transition"] = np.array([[1, 1], [0, 1]])
      self.ssm["selection"] = np.eye(k_states)
  
      # Cache some indices
      self._state_cov_idx = ("state_cov",) + np.diag_indices(k_posdef)
  
file_path: statsmodels/examples/python/statespace_local_linear_trend.py
imports: import numpy
  import pandas
  from scipy.stats import norm
  import statsmodels.api
  import matplotlib.pyplot
  import requests
  from io import BytesIO
  from zipfile import ZipFile
label_desc: Use image node_id 1 to create a new LocalLinearTrend object from inherited base classes: sm.tsa.statespace with example: obj = LocalLinearTrend(endog)