code: def testPropagatedAttributes(self):
      d = normal.Normal([0.0, -1.0], 1.0)
      tc = dtc._TensorCoercible(d)
  
      # Assert `sample` and `log_prob` work correctly.
      s = self.evaluate(tc.sample())
      self.assertAllEqual(d.log_prob(s), tc.log_prob(s))
  
      # Assert that other properties/methods of the underlying `Normal` are passed
      # through correctly.
      self.assertAllEqual(d.loc, tc.loc)
      self.assertAllEqual(
          d.batch_shape_tensor(), tc.batch_shape_tensor()
      )
      self.assertAllEqual(d.batch_shape, tc.batch_shape)
  
imports: import gc
  import operator
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.layers import distribution_layer
  from tensorflow_probability.python.layers.internal import distribution_tensor_coercible
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 17 for calling the DistributionTensorConversionTest obj's underlying member method code with example usage: obj.testPropagatedAttributes() without return types