code: def testReproducible(self):
      u = dtc._TensorCoercible(
          uniform.Uniform(low=-100.0, high=100),
          distribution.Distribution.sample,
      )
      # Small scale means only the mean really matters.
      x = normal.Normal(loc=u, scale=0.0001)
      [u_, x1_, x2_] = self.evaluate(
          [tf.convert_to_tensor(x.loc), x.sample(), x.sample()]
      )
      self.assertNear(u_, x1_, err=0.01)
      self.assertNear(u_, x2_, err=0.01)
  
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 4 for calling the DistributionTensorConversionTest obj's underlying member method code with example usage: obj.testReproducible() without return types