code: def _all_packetized(thing):
      def one_ok(shape):
          ans = tf.TensorShape(shape).num_elements() > 1
          for dim in tf.TensorShape(shape):
              ans &= dim % 4 == 0
          if ans:
              hp.note("Presuming shape {} is packetized".format(shape))
          else:
              hp.note(
                  "Not presuming shape {} is packetized".format(shape)
              )
          return ans
  
      return _all_ok(thing, one_ok)
  
imports: import collections
  from absl.testing import parameterized
  import hypothesis
  from hypothesis import strategies
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python import distributions
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.bijectors import expm1
  from tensorflow_probability.python.bijectors import normal_cdf
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import hypothesis_testlib
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math.psd_kernels import hypothesis_testlib
label_desc: Use image node_id 4 for calling a global function with example usage: _all_packetized(thing) and returns: _all_ok, ans