const std::vector<Output>& grad_inputs,
                std::vector<Output>* grad_outputs) {
  // AddN doesn't support broadcasting, so all the inputs must be the
  // same shape.
  // Note:
  // dy/dx_k = d(x_1 + x_2 + ... + x_n)/dx_k = 1 for all x_k
  // hence dx_k = dy for all x_k
  // So the gradient for AddN just transfers the incoming gradient to
  // all outgoing gradients.

  if (gradient_tensors.size() == 1) {
    return gradient_tensors[0];
  }

  AbstractOperationPtr op(ctx_->CreateOperation());
  Status s = op->Reset("AddN", /*raw_device_name=*/nullptr);
  if (!s.ok()) {
    return nullptr;
  }
  s = op->AddInputList(gradient_tensors);
  if (!s.ok()) {
    return nullptr;
  }

  int num_outputs = 1;

  // CHECK: tf.AddN
  %0 = "tf.AddN"(%arg0, %arg1, %arg2) : (tensor<!tf_type.variant<tensor<2xf32>>>, tensor<!tf_type.variant<tensor<2xf32>>>, tensor<!tf_type.variant<tensor<2xf32>>>) -> tensor<!tf_type.variant<tensor<2xf32>>>
  func.return %0 : tensor<!tf_type.variant<tensor<2xf32>>>

  }

  // CHECK-LABEL: unsupported_dtype
  func.func @unsupported_dtype(%arg0: tensor<2x!tf_type.variant>) -> tensor<2x!tf_type.variant> {
    // CHECK: tf.AddN
    // expected-remark@+1 {{skipping legalization due to unsupported type 'tensor<2x!tf_type.variant>'}}
    %0 = "tf.AddN"(%arg0, %arg0) : (tensor<2x!tf_type.variant>, tensor<2x!tf_type.variant>) -> tensor<2x!tf_type.variant>

    func.return %0 : tensor<2x!tf_type.variant>
  }

    // Just one backprop edge.
    *grad = grads_to_keep[0];
  } else {
    // Otherwise, adds backprop-ed gradients.
    // TODO(andydavis) Use a better accumulator here.
    *grad = ops::AddN(scope_, grads_to_keep);
  }

  return absl::OkStatus();
}

bool SymbolicGradientBuilder::IsPrimitiveOpWithNoGrad(const string& opname) {
  ops::GradFunc grad_fn;

  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Test TF_Operation*() query functions.
  EXPECT_EQ(string("add"), string(TF_OperationName(add)));
  EXPECT_EQ(string("AddN"), string(TF_OperationOpType(add)));
  EXPECT_EQ(string(""), string(TF_OperationDevice(add)));
  EXPECT_EQ(1, TF_OperationNumOutputs(add));
  EXPECT_EQ(TF_INT32, TF_OperationOutputType(TF_Output{add, 0}));

}

func.func @addN(%arg0: tensor<2x3xi32>, %arg1: tensor<2x3xi32>, %arg2: tensor<2x3xi32>) -> tensor<2x3xi32> {
  %0 = "tf.AddN"(%arg0, %arg1, %arg2) : (tensor<2x3xi32>, tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
  func.return %0 : tensor<2x3xi32>

// CHECK-LABEL: addN

  TF_Operation* add = Add(feed1, feed2, func_graph_, s_);
  DefineT(-1, {}, {{feed1, 0}, {feed2, 0}}, {{add, 3}}, {}, true);
  EXPECT_EQ(TF_OUT_OF_RANGE, TF_GetCode(s_));
  EXPECT_EQ(string("Node 'add' (type: 'AddN', num of outputs: 1) does "
                   "not have output 3\n\tEncountered while processing "
                   "output 0 from function 'MyFunc'"),
            string(TF_Message(s_)));
}

}

Node* AddNLike(const std::vector<ops::NodeOut>& inputs,
               const GraphDefBuilder::Options& opts) {
  if (opts.HaveError()) return nullptr;
  NodeBuilder node_builder(opts.GetNameForOp("AddN"), "AddNLikeTest",
                           opts.op_registry());
  node_builder.Input(inputs);
  return opts.FinalizeBuilder(&node_builder);
}

            "Less", "LessEqual", "SigmoidGrad", "SoftplusGrad", "SoftsignGrad",
            "TanhGrad", "Pow", "SquaredDifference", "ApproximateEqual",
            // Others
            "AddN", "Bitcast", "Cast", "ClipByValue", "Const", "Empty",
            "Identity", "IdentityN", "Relu", "Relu6", "ReluGrad", "Relu6Grad",
            "LeakyReluGrad", "Elu", "EluGrad", "Selu", "SeluGrad", "Select",