code: def testMulGrad(self, use_mlir):
      if use_mlir:
          SetTracingImplementation("mlir")
  
      def model(a, b):
          with tape_lib.GradientTape() as tape:
              tape.watch(a)
              tape.watch(b)
              result = unified_math_ops.mul(a, b)
          grads = tape.gradient(result, [a, b])
          return grads
  
      with context_lib.set_default(get_immediate_execution_context()):
          a = TensorCastHelper(constant_op.constant([1.0, 2.0]))
          b = TensorCastHelper(constant_op.constant([3.0, 4.0]))
  
          func_outputs = def_function.function(model)(a, b)
          self.assertAllEqual(func_outputs[0].numpy(), [3.0, 4.0])
          self.assertAllEqual(func_outputs[1].numpy(), [1.0, 2.0])
  
          eager_outputs = model(a, b)
          self.assertAllEqual(eager_outputs[0].numpy(), [3.0, 4.0])
          self.assertAllEqual(eager_outputs[1].numpy(), [1.0, 2.0])
  
imports: import timeit
  from absl.testing import parameterized
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework.experimental import _unified_api
  from tensorflow.python.framework.experimental import context_stack
  from tensorflow.python.framework.experimental import def_function
  from tensorflow.python.framework.experimental import math_ops
  from tensorflow.python.framework.experimental import nn_ops
  from tensorflow.python.framework.experimental import tape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_grad
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 10 for calling the UnifiedApiTest obj's underlying member method code with example usage: obj.testMulGrad(use_mlir) and returns: grads