code: def CheckGradConfigsToTest():
      """Iterator for different convolution shapes, strides and paddings.
  
      compute_gradient_error() is very expensive. So the configs should be
      relatively small.
  
      Yields:
        Tuple (input_size, filter_size, out_size, stride, padding), the depthwise
        convolution parameters.
      """
      input_sizes = [
          [2, 5, 8, 1],
          [4, 5, 5, 1],
          [2, 4, 4, 2],
          [1, 15, 15, 2],
          [2, 15, 16, 1],
      ]
      filter_sizes = [
          [4, 4, 1, 2],
          [2, 2, 1, 2],
          [3, 1, 2, 2],
          [1, 3, 2, 1],
          [3, 3, 1, 2],
      ]
      out_sizes = [
          [2, 5, 8, 2],
          [4, 2, 2, 2],
          [2, 4, 4, 4],
          [1, 15, 15, 2],
          [2, 5, 5, 2],
      ]
      strides = [1, 2, 1, 1, 3]
      # pylint: disable=invalid-name
      VALID = "VALID"
      SAME = "SAME"
      # pylint: enable=invalid-name
      paddings = [SAME, VALID, SAME, SAME, VALID]
      for i, f, o, s, p in zip(
          input_sizes, filter_sizes, out_sizes, strides, paddings
      ):
          yield i, f, o, s, p
  
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 4 for calling a global function with example usage: CheckGradConfigsToTest() without return types