code: def test_op_backward_pass(dtype):
      np.random.seed(13)
      data_width = 5
      data_height = 4
      data_channels = 3
      warp_width = 2
      warp_height = 6
      batch_size = 3
  
      warp = _make_warp(batch_size, warp_height, warp_width, dtype)
      data_shape = (batch_size, data_height, data_width, data_channels)
      data = np.random.rand(*data_shape).astype(dtype)
      data_tensor = tf.constant(data)
      warp_tensor = tf.constant(warp)
      theoretical, _ = tf.test.compute_gradient(
          resampler_ops.resampler, [data_tensor, warp_tensor]
      )
      data_tensor_64 = tf.constant(data, dtype=tf.float64)
      warp_tensor_64 = tf.constant(warp, dtype=tf.float64)
      _, numerical_64 = tf.test.compute_gradient(
          resampler_ops.resampler, [data_tensor_64, warp_tensor_64]
      )
  
      for t, n in zip(theoretical, numerical_64):
          test_utils.assert_allclose_according_to_type(
              t, n, float_rtol=5e-5, float_atol=5e-5
          )
  
file_path: addons/tensorflow_addons/image/tests/resampler_ops_test.py
imports: import numpy
  import pytest
  import tensorflow
  from tensorflow_addons.image import resampler_ops
  from tensorflow_addons.utils import test_utils
label_desc: Use image node_id 5 for calling a global function with example usage: test_op_backward_pass(dtype) without return types