class_docstr: Test that ResizeBilinearGrad operates reproducibly.
  
  Inheriting from test_base.ResizeBilinearOpTestBase ensures that regular op
  functionality is correct when the deterministic code-path is selected.
code: def _randomDataOp(self, shape, data_type):
      return constant_op.constant(
          self._randomNDArray(shape), dtype=data_type
      )
  
imports: import numpy
  from absl.testing import parameterized
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager import context
  from tensorflow.python.framework import config
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors_impl
  from tensorflow.python.framework import random_seed
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import image_grad_test_base
  from tensorflow.python.ops import image_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 2 for calling the ResizeBilinearOpDeterministicTest obj's underlying member method code with example usage: obj._randomDataOp(shape, data_type) and returns: constant_op