code: def testNumpyVariableRandomOps(self, data):
      # Hypothesis strategy:
      # Generate a random max stack depth and value shape
      # Deduce the batch size from the value shape
      # Make a random dtype
      # Generate a random initial value of that dtype and shape
      # Generate ops, some of which write random values of that dtype and shape
      max_stack_depth = data.draw(
          hps.integers(min_value=1, max_value=1000)
      )
      value_shape = data.draw(hpnp.array_shapes(min_dims=1))
      batch_size = value_shape[0]
      dtype = data.draw(hpnp.scalar_dtypes())
      masks = hpnp.arrays(dtype=np.bool_, shape=[batch_size])
      values = hpnp.arrays(dtype, value_shape)
      init_val = data.draw(values)
      ops = data.draw(
          hps.lists(
              hps.one_of(
                  hps.tuples(hps.just("update"), values, masks),
                  hps.tuples(hps.just("push"), masks),
                  hps.tuples(
                      hps.just("pop"), masks
                  ),  # preserve line break
                  hps.tuples(hps.just("read")),
              )
          )
      )
      self.check_same_results(
          (max_stack_depth, init_val), ops, var_init
      )
  
file_path: probability/tensorflow_probability/python/experimental/auto_batching/numpy_backend_test.py
imports: import hypothesis
  from hypothesis import strategies
  from hypothesis.extra import numpy
  import numpy
  from tensorflow_probability.python.experimental.auto_batching import backend_test_lib
  from tensorflow_probability.python.experimental.auto_batching import instructions
  from tensorflow_probability.python.experimental.auto_batching import numpy_backend
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 2 for calling the NumpyVariableTest obj's underlying member method code with example usage: obj.testNumpyVariableRandomOps(data) without return types