code: def testNoJacobianCorrection(self, dist_name, data):
      # Disable validate args since transforming with Softplus and inverting
      # might make arguments not as close to integers.
      dist = data.draw(
          dhps.distributions(
              dist_name=dist_name,
              enable_vars=False,
              validate_args=False,
          )
      )
  
      # Ensure that these are distributions over floats so we can apply the
      # Softplus bijector.
      if "dtype" in dist.parameters:
          dist = dist.copy(dtype=tf.float32)
      bij = softplus.Softplus()
      transformed_dist = tfd.TransformedDistribution(dist, bijector=bij)
  
      seed = test_util.test_seed()
      samples = transformed_dist.sample(7, seed=seed)
      # Break bijector caching.
      samples = self.evaluate(
          samples + tf.constant(0.0, dtype=samples.dtype)
      )
  
      # Check that no jacobian correction is added for a discrete distribution.
      self.assertAllClose(
          self.evaluate(dist.log_prob(bij.inverse(samples))),
          self.evaluate(transformed_dist.log_prob(samples)),
      )
  
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 TestDiscreteDistributions obj's underlying member method code with example usage: obj.testNoJacobianCorrection(dist_name, data) without return types