code: def testConvertToNestedTensor(
      self,
      value,
      dtype=None,
      dtype_hint=None,
      name="c2t",
      convert_ref=True,
      expected=None,
  ):
      # Convert specs to tensors or variables.
      def maybe_spec_to_tensor(x):
          if isinstance(x, VariableSpec):
              return tf.Variable(
                  tf.zeros(x.shape, x.dtype),
                  trainable=x.trainable,
                  name=x.name,
              )
          if isinstance(x, TensorSpec):
              return tf.zeros(x.shape, x.dtype, name=x.name)
          return x
  
      value = nest.map_structure(maybe_spec_to_tensor, value)
  
      # Grab shape/dtype from convert_to_nested_tensor for comparison.
      def spec_from_value(x):
          if isinstance(x, tf.Variable):
              return VariableSpec.from_value(x)
          return TensorSpec.from_tensor(x)
  
      observed = nest.map_structure(
          spec_from_value,
          nest_util.convert_to_nested_tensor(
              value,
              dtype,
              dtype_hint,
              convert_ref=convert_ref,
              name=name,
          ),
      )
      self.assertAllEqualNested(observed, expected)
  
imports: import collections
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import deterministic
  from tensorflow_probability.python.distributions import half_cauchy
  from tensorflow_probability.python.distributions import joint_distribution_auto_batched
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import nest_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow.python.util import nest
label_desc: Use image node_id 9 for calling the NestUtilTest obj's underlying member method code with example usage: obj.testConvertToNestedTensor(value, dtype, dtype_hint, name, convert_ref, expected) and returns: x, TensorSpec, tf, tf, VariableSpec