code: def testDistribution(self, dist_name, data):
      dist = data.draw(
          dhps.distributions(dist_name=dist_name, enable_vars=False)
      )
      seed = test_util.test_seed()
      with tfp_hps.no_tf_rank_errors():
          s1 = self.evaluate(dist.sample(50, seed=seed))
      if tf.executing_eagerly():
          tf.random.set_seed(seed)
      with tfp_hps.no_tf_rank_errors():
          s2 = self.evaluate(dist.sample(50, seed=seed))
      self.assertAllEqual(s1, s2)
  
      seed = test_util.test_seed(sampler_type="stateless")
      with tfp_hps.no_tf_rank_errors():
          s1 = self.evaluate(dist.sample(50, seed=seed))
          s2 = self.evaluate(dist.sample(50, seed=seed))
      self.assertAllEqual(s1, s2)
  
imports: import collections
  from absl.testing import parameterized
  import hypothesis
  from hypothesis import strategies
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python import distributions
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.bijectors import expm1
  from tensorflow_probability.python.bijectors import normal_cdf
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import hypothesis_testlib
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math.psd_kernels import hypothesis_testlib
label_desc: Use image node_id 1 for calling the ReproducibilityTest obj's underlying member method code with example usage: obj.testDistribution(dist_name, data) without return types