ops::Placeholder(root.WithOpName("placeholder"), DT_FLOAT);
  Output const_0d = ops::Const(root.WithOpName("const_0d"), 42);

  Output const_2d = ops::Const(root.WithOpName("const_2d"), {{1, 2}, {4, 3}});

  EXPECT_THAT(const_0d.node(), NodeWith(ConstantValue(42)));
  EXPECT_THAT(const_0d.node(), NodeWith(ConstantValue(42), Name("const_0d")));

  EXPECT_THAT(const_2d.node(), NodeWith(ConstantValue({{1, 2}, {4, 3}})));

// CHECK-DAG: %[[CONST_2:.+]] = stablehlo.constant dense<3> : tensor<1xi32>
// CHECK-DAG: %[[CONST_3:.+]] = stablehlo.constant dense<4> : tensor<1xi32>
// CHECK-DAG: %[[CONST_4:.+]] = stablehlo.constant dense<2> : tensor<1xi32>

// CHECK-DAG: %[[CONST_0:.*]] = stablehlo.constant dense<[{{.*}}]> : tensor<2xf32>
// CHECK-DAG: %[[CONST_1:.*]] = stablehlo.constant dense<[{{.*}}]> : tensor<2x3x3x2xf32>
// CHECK-DAG: %[[CONV:.*]] = stablehlo.convolution(%[[ARG]], %[[CONST_1]]) {{.*}} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x2x2xf32>
// CHECK-DAG: %[[BROADCAST:.*]] = stablehlo.broadcast_in_dim %[[CONST_0]], dims = [3] : (tensor<2xf32>) -> tensor<1x3x2x2xf32>

// CHECK-DAG: %[[CONST_0:.*]] = "tf.Const"() <{value = dense<{{.*}}> : tensor<1x1x3x2xf32>}> : () -> tensor<1x1x3x2xf32>
// CHECK-DAG: %[[CONST_1:.*]] = "tf.Const"() <{value = dense<-1.000000e+00> : tensor<f32>}> : () -> tensor<f32>
// CHECK-DAG: %[[CONST_2:.*]] = "tf.Const"() <{value = dense<1.000000e+00> : tensor<f32>}> : () -> tensor<f32>

  %3 = stablehlo.add %2, %1 : tensor<1x3xf32>
  func.return %3: tensor<1x3xf32>
}
// CHECK: %[[CONST_0:.*]] = stablehlo.constant dense<2.000000e+00>
// CHECK: %[[CONST_1:.*]] = stablehlo.constant dense<2.000000e+00>
// CHECK: %[[XLA_CALL_MODULE:.*]] = "tf.XlaCallModule"(%arg0, %[[CONST_0]], %[[CONST_1]])
// CHECK: return %[[XLA_CALL_MODULE:.*]] : tensor<1x3xf32>
// CHECK: }

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include "tensorflow/cc/ops/const_op.h"
#include "tensorflow/core/framework/types.h"

namespace tensorflow {
namespace ops {

namespace {
template <typename T>
Output ConstHelper(const Scope& scope, const T& value, DataType dtype) {

#include <vector>

#include "tensorflow/cc/framework/ops.h"
#include "tensorflow/cc/framework/scope.h"
#include "tensorflow/core/graph/node_builder.h"

namespace tensorflow {
namespace ops {

/// @defgroup const_op Const Op
/// @{

Output Const(const Scope& scope, const Input::Initializer& val);

Output ConstFromProto(const Scope& scope, const TensorProto& proto);

  // Build the graph:
  // "add" = "const_0" + "const_1"
  // "identity" = "add"
  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
  Output const_0 = ops::Const(s.WithOpName("const_0"), 1, {2});
  Output const_1 = ops::Const(s.WithOpName("const_1"), 2, {2});
  Output add = ops::Add(s.WithOpName("add"), const_0, const_1);
  Output identity = ops::Identity(s.WithOpName("identity"), add);

  }

// PerTensor-LABEL: func @depthwise_conv
// PerTensor-DAG: %[[CONST_0:.*]] = arith.constant dense<0.000000e+00> : tensor<6xf32>
// PerTensor: %[[CONST_1:.*]] = arith.constant dense
// PerTensor-NOT: tensor<2x3x1x6xf32>
// PerTensor-SAME: tensor<2x3x3x2xf32>

      }
      experimental_debug_info {
        original_node_names: "const_2"
      }
    }
    ret {
      key: "bar"
      value: "const_2:output:0"
    }
  }
  function {
    signature {
      name: "foo"
      output_arg {
        name: "foo"
        type: DT_FLOAT
      }
    }
    node_def {
      name: "const_1"
      op: "Const"
      attr {
        key: "dtype"