code: def testFullEagerTPU(self, distribution):
      dataset = get_dataset_from_tensor_slices(
          [5.0, 6.0, 7.0, 8.0]
      ).batch(2)
  
      def train_step(data):
          return math_ops.square(data)
  
      input_iterator = iter(
          distribution.experimental_distribute_dataset(dataset)
      )
  
      with self.assertRaisesRegex(
          NotImplementedError, "does not support pure eager execution"
      ):
          distribution.run(train_step, args=(next(input_iterator),))
  
imports: from absl.testing import parameterized
  from tensorflow.python import tf2
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.distribute import combinations
  from tensorflow.python.distribute import device_util
  from tensorflow.python.distribute import distribute_lib
  from tensorflow.python.distribute import reduce_util
  from tensorflow.python.distribute import strategy_combinations
  from tensorflow.python.distribute import test_util
  from tensorflow.python.eager import def_function
  from tensorflow.python.eager import test
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_assert
  from tensorflow.python.ops import map_fn
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.ops.losses import losses
  from tensorflow.python.tpu import tpu
  from tensorflow.python.util import nest
label_desc: Use image node_id 6 for calling the InputIterationTest obj's underlying member method code with example usage: obj.testFullEagerTPU(distribution) and returns: math_ops