code: def testConv2dBackpropFilterGrad(self):
      x = array_ops.placeholder(
          dtype=dtypes.float32, shape=[1, 4, 4, 3], name="input"
      )
      f = constant_op.constant(
          [0.5], dtype=dtypes.float32, shape=[2, 2, 3, 2], name="filter"
      )
      strides = [1, 1, 1, 1]
      padding = "SAME"
      out = nn_impl.depthwise_conv2d(x, f, strides, padding)
  
      grad_wrt_input = gradients_impl.gradients(out, x)[0]
      self.run_test(f, grad_wrt_input)
  
      grad_wrt_filter = gradients_impl.gradients(out, f)[0]
      # todo(rocm):
      # investigate why ROCm 3.5 onwards needs a slightly elevated tolerance
      self.run_test(x, grad_wrt_filter, 2e-3)
  
imports: import numpy
  from tensorflow.python.eager import backprop
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_nn_ops
  from tensorflow.python.ops import gradient_checker
  from tensorflow.python.ops import gradient_checker_v2
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import nn_grad
  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 4 for calling the Conv2dOpTest obj's underlying member method code with example usage: obj.testConv2dBackpropFilterGrad() without return types