code: def _runSamplingBenchmark(
      self,
      name,
      create_distribution,
      use_gpu,
      num_components,
      batch_size,
      num_features,
      sample_size,
  ):
      config = tf1.ConfigProto()
      config.allow_soft_placement = True
      np.random.seed(127)
      with tf1.Session(config=config, graph=tf.Graph()) as sess:
          tf.random.set_seed(0)
          with tf.device("/device:GPU:0" if use_gpu else "/cpu:0"):
              dist = create_distribution(
                  num_components=num_components,
                  batch_size=batch_size,
                  num_features=num_features,
              )
              sample_op = dist.sample(
                  sample_size, seed=test_util.test_seed()
              ).op
              sess.run(tf1.global_variables_initializer())
              reported = self.run_op_benchmark(
                  sess,
                  sample_op,
                  min_iters=10,
                  name=(
                      "%s_%s_components_%d_batch_%d_features_%d_sample_%d"
                      % (
                          name,
                          use_gpu,
                          num_components,
                          batch_size,
                          num_features,
                          sample_size,
                      )
                  ),
              )
              tf1.logging.vlog(
                  2,
                  "\t".join(["%s", "%d", "%d", "%d", "%d", "%g"])
                  % (
                      use_gpu,
                      num_components,
                      batch_size,
                      num_features,
                      sample_size,
                      reported["wall_time"],
                  ),
              )
  
imports: import contextlib
  import warnings
  import numpy
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import dirichlet
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import half_normal
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import mixture
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 1 for calling the MixtureBenchmark obj's underlying member method code with example usage: obj._runSamplingBenchmark(name, create_distribution, use_gpu, num_components, batch_size, num_features, sample_size) without return types