code: def build_smooth_seasonal_transition_matrix(
      period, frequency_multipliers, dtype
  ):
      """Build the transition matrix for a SmoothSeasonalStateSpaceModel."""
  
      two_pi = tf.constant(2.0 * np.pi, dtype=dtype)
      frequencies = two_pi * frequency_multipliers / period
      num_frequencies = static_num_frequencies(frequency_multipliers)
  
      sin_frequencies = tf.sin(frequencies)
      cos_frequencies = tf.cos(frequencies)
  
      trigonometric_values = tf.stack(
          [
              cos_frequencies,
              sin_frequencies,
              -sin_frequencies,
              cos_frequencies,
          ],
          axis=-1,
      )
  
      transition_matrix = tf.linalg.LinearOperatorBlockDiag(
          [
              tf.linalg.LinearOperatorFullMatrix(
                  matrix=tf.reshape(trigonometric_values[i], [2, 2]),
                  is_square=True,
              )
              for i in range(num_frequencies)
          ]
      )
  
      return transition_matrix
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import softplus
  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.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: build_smooth_seasonal_transition_matrix(period, frequency_multipliers, dtype) and returns: transition_matrix