code: def testGradient(self):
      x1 = (
          (1 + np.linspace(0, 5, np.prod([1, 3, 2])))
          .astype(np.float32)
          .reshape([1, 3, 2])
      )
      x2 = (
          (1 + np.linspace(0, 5, np.prod([1, 3, 2])))
          .astype(np.float32)
          .reshape([1, 3, 2])
      )
  
      def div_x1(x1):
          return math_ops.truediv(x1, x2) * math_ops.cast(
              1.1, dtype=x1.dtype
          )
  
      def div_x2(x2):
          return math_ops.truediv(x1, x2) * math_ops.cast(
              1.1, dtype=x2.dtype
          )
  
      with self.cached_session():
          err = gradient_checker_v2.max_error(
              *gradient_checker_v2.compute_gradient(div_x1, [x1])
          )
          self.assertLess(
              err, self._GRAD_TOL[dtypes.as_dtype(x1.dtype)]
          )
  
          err = gradient_checker_v2.max_error(
              *gradient_checker_v2.compute_gradient(div_x2, [x2])
          )
          self.assertLess(
              err, self._GRAD_TOL[dtypes.as_dtype(x2.dtype)]
          )
  
      self._compareGpu(x1, x2, np.true_divide, math_ops.truediv)
      self._compareGpu(x1, x2 + 0.1, np.floor_divide, math_ops.floordiv)
  
imports: import itertools
  import threading
  import numpy
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import random_seed
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradient_checker_v2
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.ops.gen_array_ops import broadcast_gradient_args
  from tensorflow.python.platform import test
label_desc: Use image node_id 4 for calling the BroadcastSimpleTest obj's underlying member method code with example usage: obj.testGradient() and returns: unknown, unknown