code: def testGradient(self):
      with self.session():
          # Input: [batch, height, width, input_depth]
          x_shape = [2, 5, 6, 2]
          # Filter: [kernel_height, kernel_width, input_depth, output_depth]
          f_shape = [3, 3, 2, 2]
          # Output: [batch, height, width, output_depth]
          y_shape = [2, 5, 6, 2]
  
          np.random.seed(1)  # Make it reproducible.
          x_val = np.random.random_sample(x_shape).astype(np.float32)
          f_val = np.random.random_sample(f_shape).astype(np.float32)
          x = constant_op.constant(
              x_val, name="x", dtype=dtypes.float32
          )
          f = constant_op.constant(
              f_val, name="f", dtype=dtypes.float32
          )
  
          for rate in range(1, 4):
              output = nn_ops.atrous_conv2d(
                  x, f, rate=rate, padding="SAME"
              )
              err = gradient_checker.compute_gradient_error(
                  [x, f], [x_shape, f_shape], output, y_shape
              )
              print("atrous_conv2d gradient err = %g " % err)
              err_tolerance = (
                  4e-3 if test_util.is_xla_enabled() else 1e-3
              )
              self.assertLess(err, err_tolerance)
  
imports: import numpy
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradient_checker
  from tensorflow.python.ops import nn_impl
  from tensorflow.python.ops import nn_ops
  import tensorflow.python.ops.nn_grad
  from tensorflow.python.platform import test
label_desc: Use image node_id 3 for calling the AtrousConv2DTest obj's underlying member method code with example usage: obj.testGradient() without return types