code: def is_ref(x):
      """Evaluates if the object has reference semantics.
  
      An object is deemed "reference" if it is a `tf.Variable` instance or is
      derived from a `tf.Module` with `dtype` and `shape` properties.
  
      Args:
        x: Any object.
  
      Returns:
        is_ref: Python `bool` indicating input is has nonreference semantics, i.e.,
          is a `tf.Variable` or a `tf.Module` with `dtype` and `shape` properties.
      """
      return (
          # Note: we check that tf.Variable is a class because we might be using a
          # different backend other than TF.
          isinstance(x, variables_module.Variable)
          or (
              isinstance(x, module.Module)
              and hasattr(x, "dtype")
              and hasattr(x, "shape")
          )
      )
  
imports: import numpy
  from tensorflow_probability.python.internal.backend.numpy import dtype
  from tensorflow_probability.python.internal.backend.numpy import ops
  from tensorflow_probability.python.internal.backend.numpy import ops
  from tensorflow_probability.python.internal.backend.numpy import numpy_array
  from tensorflow_probability.python.internal.backend.numpy import debugging
  from tensorflow_probability.python.internal.backend.numpy import control_flow
  from tensorflow_probability.python.internal.backend.numpy import linalg_impl
  from tensorflow_probability.python.internal.backend.numpy import numpy_math
  from tensorflow_probability.python.internal.backend.numpy import ops
  from tensorflow_probability.python.internal.backend.numpy import nest
  import numpy
  from tensorflow_probability.python.internal.backend.numpy import linalg_impl
  from tensorflow_probability.python.internal.backend.numpy import ops
  from tensorflow_probability.python.internal.backend.numpy.gen import tensor_shape
  from tensorflow_probability.python.internal.backend.numpy import private
label_desc: Use image node_id 5 for calling a global function with example usage: is_ref(x) and returns: unknown