code: def _build_test_model(
      self,
      num_timesteps=5,
      num_features=2,
      batch_shape=(),
      missing_prob=0,
      true_noise_scale=0.1,
      true_level_scale=0.04,
      dtype=tf.float32,
  ):
      strm = test_util.test_seed_stream()
      design_matrix = tf.random.normal(
          [num_timesteps, num_features], dtype=dtype, seed=strm()
      )
      weights = tf.random.normal(
          list(batch_shape) + [num_features], dtype=dtype, seed=strm()
      )
      regression = tf.linalg.matvec(design_matrix, weights)
      noise = (
          tf.random.normal(
              list(batch_shape) + [num_timesteps],
              dtype=dtype,
              seed=strm(),
          )
          * true_noise_scale
      )
      level = tf.cumsum(
          tf.random.normal(
              list(batch_shape) + [num_timesteps],
              dtype=dtype,
              seed=strm(),
          )
          * true_level_scale,
          axis=-1,
      )
      time_series = regression + noise + level
      is_missing = (
          tf.random.uniform(
              list(batch_shape) + [num_timesteps],
              dtype=dtype,
              seed=strm(),
          )
          < missing_prob
      )
  
      model = gibbs_sampler.build_model_for_gibbs_fitting(
          observed_time_series=missing_values_util.MaskedTimeSeries(
              time_series[..., tf.newaxis], is_missing
          ),
          design_matrix=design_matrix,
          weights_prior=normal.Normal(
              loc=tf.cast(0.0, dtype), scale=tf.cast(10.0, dtype)
          ),
          level_variance_prior=inverse_gamma.InverseGamma(
              concentration=tf.cast(0.01, dtype),
              scale=tf.cast(0.01 * 0.01, dtype),
          ),
          observation_noise_variance_prior=inverse_gamma.InverseGamma(
              concentration=tf.cast(0.01, dtype),
              scale=tf.cast(0.01 * 0.01, dtype),
          ),
      )
      return model, time_series, is_missing
  
file_path: probability/tensorflow_probability/python/experimental/sts_gibbs/benchmarks_test.py
imports: import time
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import inverse_gamma
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.experimental.sts_gibbs import gibbs_sampler
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.sts.internal import missing_values_util
label_desc: Use image node_id 1 for calling the XLABenchmarkTests obj's underlying member method code with example usage: obj._build_test_model(num_timesteps, num_features, batch_shape, missing_prob, true_noise_scale, true_level_scale, dtype) and returns: model,  time_series,  is_missing