code: def semilocal_linear_trend_transition_matrix(autoregressive_coef):
      """Build the transition matrix for a semi-local linear trend model."""
      # We want to write the following 2 x 2 matrix:
      #  [[1., 1., ],    # level(t+1) = level(t) + slope(t)
      #   [0., ar_coef], # slope(t+1) = ar_coef * slope(t)
      # but it's slightly tricky to properly incorporate the batch shape of
      # autoregressive_coef. E.g., if autoregressive_coef has shape [4,6], we want
      # to return shape [4, 6, 2, 2]. We do this by breaking the matrix into its
      # fixed entries, written explicitly, and then the autoregressive_coef part
      # which we add in after using a mask to broadcast to the correct matrix shape.
  
      fixed_entries = tf.constant(
          [[1.0, 1.0], [0.0, 0.0]], dtype=autoregressive_coef.dtype
      )
  
      autoregressive_coef_mask = tf.constant(
          [[0.0, 0.0], [0.0, 1.0]], dtype=autoregressive_coef.dtype
      )
      bottom_right_entry = (
          autoregressive_coef[..., tf.newaxis, tf.newaxis]
          * autoregressive_coef_mask
      )
      return tf.linalg.LinearOperatorFullMatrix(
          fixed_entries + bottom_right_entry
      )
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.bijectors import tanh
  from tensorflow_probability.python.distributions import linear_gaussian_ssm
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.sts.internal import util
  from tensorflow_probability.python.sts.structural_time_series import Parameter
  from tensorflow_probability.python.sts.structural_time_series import StructuralTimeSeries
label_desc: Use image node_id 1 for calling a global function with example usage: semilocal_linear_trend_transition_matrix(autoregressive_coef) and returns: tf