let summary = "Computes rectified linear 6 gradients for a Relu6 operation.";

  let arguments = (ins
    Arg<TF_IntOrFpTensor, [{The backpropagated gradients to the corresponding Relu6 operation.}]>:$gradients,
    Arg<TF_IntOrFpTensor, [{The features passed as input to the corresponding Relu6 operation, or
its output; using either one produces the same result.}]>:$features
  );

  %1 = "chlo.broadcast_maximum"(%0, %arg0) {broadcast_dimensions = array<i64>} : (tensor<i32>, tensor<?xi32>) -> tensor<?xi32>
  func.return %1 : tensor<?xi32>
}

// CHECK-LABEL:   func @relu6(
// CHECK-SAME:                %[[VAL_0:.*]]: tensor<1xi32>) -> tensor<1xi32> {
// CHECK-DAG:       %[[VAL_1:.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>

    "L2_POOL_2D",
    "LOCAL_RESPONSE_NORMALIZATION",
    "LOGISTIC",
    "LSH_PROJECTION",
    "LSTM",
    "MAX_POOL_2D",
    "MUL",
    "RELU",
    "RELU_N1_TO_1",
    "RELU6",
    "RESHAPE",
    "RESIZE_BILINEAR",
    "RNN",
    "SOFTMAX",
    "SPACE_TO_DEPTH",
    "SVDF",
    "TANH",
    "CONCAT_EMBEDDINGS",
    "SKIP_GRAM",
    "CALL",
    "CUSTOM",