code: def _build_model_and_params(
      self, num_timesteps, param_batch_shape, num_posterior_draws=10
  ):
      seed = test_util.test_seed_stream()
      rng = test_util.test_np_rng(seed())
      observed_time_series = self._build_tensor(
          rng.randn(*(param_batch_shape + [num_timesteps]))
      )
  
      # Build an STS model with multiple components
      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",
      )
      model = sum_lib.Sum(
          components=[day_of_week, llt],
          observed_time_series=observed_time_series,
      )
  
      # Sample test params from the prior (faster than posterior samples).
      param_samples = [
          p.prior.sample([num_posterior_draws], seed=seed())
          for p in model.parameters
      ]
  
      return model, observed_time_series, param_samples
  
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.sts import decomposition
  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.forecast import forecast
  from tensorflow_probability.python.sts.internal import missing_values_util
label_desc: Use image node_id 1 for calling the _DecompositionTest obj's underlying member method code with example usage: obj._build_model_and_params(num_timesteps, param_batch_shape, num_posterior_draws) and returns: model,  observed_time_series,  param_samples