code: def testSampleLikeArgsGetDistDType(self):
      if self.dtype is np.float32:
          # Raw Python literals should always be interpreted as fp32.
          dist = weibull.Weibull(1.0, 2.0)
      elif self.dtype is np.float64:
          # The make_input function will cast them to self.dtype
          dist = weibull.Weibull(
              self.make_input(1.0), self.make_input(2.0)
          )
      self.assertEqual(self.dtype, dist.dtype)
      for method in ("log_prob", "prob", "log_cdf", "cdf"):
          self.assertEqual(self.dtype, getattr(dist, method)(1.0).dtype)
      for method in ("entropy", "mean", "variance", "stddev", "mode"):
          self.assertEqual(self.dtype, getattr(dist, method)().dtype)
  
imports: import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import weibull
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 12 for calling the _WeibullTest obj's underlying member method code with example usage: obj.testSampleLikeArgsGetDistDType() without return types