code: def testDepthwiseConv2dNative(self, padding):
      np.random.seed(123)
      x_in = np.random.normal(0.0, 1.0, 8 * 9 * 9).reshape([8, 9, 9, 1])
  
      kernel_in = np.array(
          [
              [[[2, 0.1]], [[3, 0.2]]],
              [[[0, 0.3]], [[1, 0.4]]],
          ]
      )
  
      inputs = constant_op.constant(x_in, dtype=dtypes.float32)
      kernel = constant_op.constant(kernel_in, dtype=dtypes.float32)
      expected_result = nn_impl.depthwise_conv2d_v2(
          inputs, kernel, strides=[1, 1, 1, 1], padding=padding
      )
  
      layout = self.batch_layout_4d
  
      x = numpy_util.pack_numpy(inputs, layout)
      kernel = numpy_util.pack_numpy(kernel, self.replicated_layout_4d)
      got = nn_impl.depthwise_conv2d_v2(
          x, kernel, strides=[1, 1, 1, 1], padding=padding
      )
  
      self.assertDTensorEqual(expected_result, layout, got)
  
imports: import itertools
  from absl.testing import parameterized
  import numpy
  from tensorflow.dtensor.python import api
  from tensorflow.dtensor.python import layout
  from tensorflow.dtensor.python import numpy_util
  from tensorflow.dtensor.python.tests import test_util
  from tensorflow.dtensor.python.tests import test_util_ops
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager import context
  from tensorflow.python.eager.polymorphic_function import polymorphic_function
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_array_ops
  from tensorflow.python.ops import gen_image_ops
  from tensorflow.python.ops import gen_linalg_ops
  from tensorflow.python.ops import nn_impl
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 7 for calling the DTensorBatchParallelSPMDTest obj's underlying member method code with example usage: obj.testDepthwiseConv2dNative(padding) without return types