REGISTER_NO_GRADIENT_OP("Equal");
REGISTER_NO_GRADIENT_OP("ApproximateEqual");
REGISTER_NO_GRADIENT_OP("NotEqual");
REGISTER_NO_GRADIENT_OP("LogicalAnd");
REGISTER_NO_GRADIENT_OP("LogicalOr");
REGISTER_NO_GRADIENT_OP("LogicalNot");
REGISTER_NO_GRADIENT_OP("Floor");

// Conjugate helper function returns the conjugate of an Output if it
// is complex valued.
Output ConjugateHelper(const Scope& scope, const Output& out) {

  // CHECK-DAG: %[[VAL19:.*]] = "tf.LogicalAnd"(%[[VAL13]], %[[VAL18]]) : (tensor<i1>, tensor<i1>) -> tensor<i1>
  // CHECK-DAG: %[[VAL20:.*]] = "tf.LogicalOr"(%[[VAL12]], %[[VAL19]]) : (tensor<i1>, tensor<i1>) -> tensor<i1>
  // CHECK-DAG: %[[VAL21:.*]] = "tf.AddV2"(%[[VAL10]], %[[VAL3]]) : (tensor<f32>, tensor<f32>) -> tensor<f32>

            "MulNoNan", "FloorDiv", "Xlogy", "Xlog1py", "Xdivy", "FloorMod",
            "BitwiseAnd", "BitwiseOr", "BitwiseXor", "LeftShift", "RightShift",
            "LogicalAnd", "LogicalOr", "Mod", "Maximum", "Minimum", "RealDiv",
            "ReciprocalGrad", "RsqrtGrad", "SqrtGrad", "TruncateDiv",
            "TruncateMod", "Equal", "NotEqual", "Greater", "GreaterEqual",

Placeholder for a device ordinal that depends on its tf_device.replicate ancestor.
  }];

  let description = [{
This op must have a tf_device.replicate ancestor. The ancestor replica_id and
logical_core attribute correspond to a TPU core. This op maps the TPU core to a
device_ordinal, where the device ordinal is the index of the core relative to
its host.

                      $input1,
                      $input2),
    (replaceWithValue $input2),
    [(HaveSameType $input2, $result),
     (AllElementsAreBool<"false"> $constant)]>;
  // select(logical_not(C), A, B) -> select(C, B, A)
  def Optimize#SelectOp#Not : Pat<
    (SelectOp (TFL_LogicalNotOp $condition), $input1, $input2),
    (SelectOp $condition, $input2, $input1)>;