code: def testNegativeSeqValuesForGradOp(self):
      with self.assertRaisesRegex(
          errors.InvalidArgumentError,
          r"Row sequence tensor values must not be negative.*",
      ):
          y = nn_ops.gen_nn_ops.fractional_avg_pool_grad(
              orig_input_tensor_shape=[2, 2, 2, 2],
              out_backprop=[
                  [
                      [[1, 2], [3, 4], [5, 6]],
                      [[7, 8], [9, 10], [11, 12]],
                  ]
              ],
              row_pooling_sequence=[-10, 1, 2, 3],
              col_pooling_sequence=[1, 2, 3, 4],
              overlapping=True,
          )
  
          self.evaluate(y)
          with self.assertRaisesRegex(
              errors.InvalidArgumentError,
              r"Column sequence tensor values must not be negative.*",
          ):
              z = nn_ops.gen_nn_ops.fractional_avg_pool_grad(
                  orig_input_tensor_shape=[2, 2, 2, 2],
                  out_backprop=[
                      [
                          [[1, 2], [3, 4], [5, 6]],
                          [[7, 8], [9, 10], [11, 12]],
                      ]
                  ],
                  row_pooling_sequence=[10, 1, 2, 3],
                  col_pooling_sequence=[1, 2, -3, 4],
                  overlapping=True,
              )
  
              self.evaluate(z)
  
imports: import math
  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 gen_nn_ops
  from tensorflow.python.ops import gradient_checker
  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 12 for calling the FractionalAvgTest obj's underlying member method code with example usage: obj.testNegativeSeqValuesForGradOp() without return types