class_docstr: Stub for VariableSpec to simplify tests in np/jax backends.
code: def __init__(
      self, shape, dtype=tf.float32, trainable=True, name=None
  ):
      super(VariableSpec, self).__init__(shape, dtype, name=name)
      self.trainable = trainable
  
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 1 to create a new VariableSpec object from inherited base classes: TensorSpec with example: obj = VariableSpec(shape, dtype, trainable, name)