// CHECK: %[[arg1_floor:.*]] = "tf.Floor"(%[[arg1_add]]
// CHECK: %[[arg1_clip:.*]] = "tf.ClipByValue"(%[[arg1_floor]]
// CHECK: %[[arg1_cast:.*]] = "tf.Cast"(%[[arg1_clip]]

// CHECK: %[[arg0_div:.*]] = "tf.Div"(%arg0
// CHECK: %[[arg0_add:.*]] = "tf.AddV2"(%[[arg0_div]]
// CHECK: %[[arg0_floor:.*]] = "tf.Floor"(%[[arg0_add]]
// CHECK: %[[arg0_clip:.*]] = "tf.ClipByValue"(%[[arg0_floor]]
// CHECK: %[[arg0_cast:.*]] = "tf.Cast"(%[[arg0_clip]]

                       std::vector<Output>* grad_outputs) {
  if (op.num_inputs() != 3) {
    return absl::InvalidArgumentError("ClipByValue requires 3 arguments");
  }
  if (grad_inputs.size() != 1) {
    return absl::InvalidArgumentError("ClipByValue grad requires 1 grad input");
  }

  Output x = op.input(0);
  Output min = op.input(1);
  Output max = op.input(2);

  // CHECK-DAG: [[VAL3:%.+]] = "tf.Const"() <{value = dense<0.00784313772> : tensor<f32>}>
  // CHECK-DAG: [[VAL4:%.+]] = "tf.Const"() <{value = dense<5.000000e-01> : tensor<f32>}>
  // CHECK-DAG: [[VAL5:%.+]] = "tf.ClipByValue"(%arg0, [[VAL2]], [[VAL1]])
  // CHECK-DAG: [[VAL6:%.+]] = "tf.Sub"([[VAL5]], [[VAL2]])
  // CHECK-DAG: [[VAL7:%.+]] = "tf.Mul"([[VAL6]], [[VAL0]])
  // CHECK-DAG: [[VAL8:%.+]] = "tf.AddV2"([[VAL7]], [[VAL4]])

            "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",

  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}

def TF_ClipByValueOp : TF_Op<"ClipByValue", [Pure, TF_SameOperandsAndResultElementTypeResolveRef]> {
  let summary = "Clips tensor values to a specified min and max.";

  let description = [{