code: def _build_model(self, observed_time_series):
      day_of_week = seasonal.Seasonal(
          num_seasons=7,
          observed_time_series=observed_time_series,
          name="day_of_week",
      )
      llt = local_linear_trend.LocalLinearTrend(
          observed_time_series=observed_time_series,
          name="local_linear_trend",
      )
      return sum_lib.Sum(
          components=[day_of_week, llt],
          observed_time_series=observed_time_series,
      )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.sts import fitting
  from tensorflow_probability.python.sts.components import local_linear_trend
  from tensorflow_probability.python.sts.components import seasonal
  from tensorflow_probability.python.sts.components import sum
  from tensorflow_probability.python.sts.internal import missing_values_util
  from tensorflow_probability.python.vi import csiszar_divergence
  from tensorflow_probability.python.vi import optimization
label_desc: Use image node_id 1 for calling the _VariationalInferenceTests obj's underlying member method code with example usage: obj._build_model(observed_time_series) and returns: sum_lib