code: def _CompareBackpropInputWithDilation(
      self,
      input_sizes,
      filter_sizes,
      output_sizes,
      stride,
      dilation,
      padding,
  ):
      x1 = np.random.rand(*filter_sizes).astype(np.float32)
      x2 = np.random.rand(*output_sizes).astype(np.float32)
  
      def _GetVal(use_xla):
          with self.session():
              t1 = array_ops.placeholder(np.float32, shape=filter_sizes)
              t2 = array_ops.placeholder(np.float32, shape=output_sizes)
              if use_xla:
                  with self.test_scope():
                      t0 = constant_op.constant(
                          input_sizes, shape=[len(input_sizes)]
                      )
                      backprop = (
                          nn_ops.depthwise_conv2d_native_backprop_input(
                              t0,
                              t1,
                              t2,
                              strides=[1, stride, stride, 1],
                              dilations=[1, dilation, dilation, 1],
                              padding=padding,
                          )
                      )
              else:
                  # TODO(wangtao): figure out gradient with stride > 1.
                  # depthwise_conv2d_native_backprop_input on CPU doesn't support
                  # dilation.
                  t3 = array_ops.space_to_batch(
                      t2, block_size=dilation, paddings=[[0, 0], [0, 0]]
                  )
                  input_sizes_transform = [
                      input_sizes[0] * dilation * dilation,
                      input_sizes[1] // dilation,
                      input_sizes[2] // dilation,
                      input_sizes[3],
                  ]
                  t0 = constant_op.constant(
                      input_sizes_transform, shape=[len(input_sizes)]
                  )
                  backprop_naive = (
                      nn_ops.depthwise_conv2d_native_backprop_input(
                          t0,
                          t1,
                          t3,
                          strides=[1, stride, stride, 1],
                          padding=padding,
                      )
                  )
                  backprop = array_ops.batch_to_space(
                      backprop_naive,
                      [[0, 0], [0, 0]],
                      block_size=dilation,
                  )
  
              ret = backprop.eval({t1: x1, t2: x2})
              self.assertShapeEqual(ret, backprop)
              return ret
  
      gpu_value = _GetVal(use_xla=True)
      cpu_value = _GetVal(use_xla=False)
  
      # TODO (b/64210055): Tolerance for TPU is high.
      self.assertAllClose(cpu_value, gpu_value, rtol=1e-2, atol=1e-3)
  
imports: import numpy
  from tensorflow.compiler.tests import xla_test
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  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 14 for calling the DepthwiseConv2DTest obj's underlying member method code with example usage: obj._CompareBackpropInputWithDilation(input_sizes, filter_sizes, output_sizes, stride, dilation, padding) and returns: ret