// Check that there are no extra "tf.Const"s existing in this function.
// CHECK-NOT: "tf.Const"

// Check that the usages of %[[CST]] and %[[CST_0]] are untouched.
// CHECK: %[[ADD:.*]] = "tf.AddV2"(%[[CST]], %[[CST_0]])
// CHECK: "tf.Max"({{.*}}, %[[ADD]])

// -----

// CHECK-LABEL: @recursively_duplicate_constants
func.func private @recursively_duplicate_constants(%arg0: tensor<1x2x3xf32>) -> tensor<1x3xf32> {

          do not modify the License. You may add Your own attribution
          notices within Derivative Works that You distribute, alongside
          or as an addendum to the NOTICE text from the Work, provided
          that such additional attribution notices cannot be construed
          as modifying the License.

      You may add Your own copyright statement to Your modifications and

  %read = "tf.ReadVariableOp"(%id0) : (!tf_res) -> tensor<32xf32>
  %cst = arith.constant dense<3.0> : tensor<32xf32>
  %add = "tf.AddV2"(%read, %cst) : (tensor<32xf32>, tensor<32xf32>) -> tensor<32xf32>
  "tf.AssignVariableOp"(%arg0, %add) : (!tf_res, tensor<32xf32>) -> ()
  func.return
}

// CHECK-LABEL: func @propagate_through_calls
func.func @propagate_through_calls(

// limitations under the License.

// Revision History
// Version 0: Initial version.
// Version 1: Add subgraphs to schema.
// Version 2: Rename operators to conform to NN API.
// Version 3: Move buffer data from Model.Subgraph.Tensors to Model.Buffers.
// Version 3a: Add new builtin op code field. Has backward compatibility with
//             version 3.

// CHECK:           %[[VAL_3:.*]] = tfl.mul(%[[VAL_1]], %[[VAL_2]]) <{fused_activation_function = "NONE"}> : (tensor<3xf32>, tensor<f32>) -> tensor<3xf32>
// CHECK:           %[[VAL_4:.*]] = tfl.add(%[[VAL_0]], %[[VAL_3]]) <{fused_activation_function = "NONE"}> : (tensor<1x384x384x3xf32>, tensor<3xf32>) -> tensor<1x384x384x3xf32>
// CHECK:           return %[[VAL_4]] : tensor<1x384x384x3xf32>
// CHECK:         }

// -----

def CreateTensorScatterNdOp : NativeCodeCall<
    "$_builder.create<TF::ScatterNdOp>($0.getLoc(), $0.getType(), $1, $2, $3)">;

//===----------------------------------------------------------------------===//
// Add op patterns.
//===----------------------------------------------------------------------===//

// We can lower AddOp to AddV2Op, except for string type. AddOp is defined for

        "tf_device.cluster"() ({
          %group = "tf.Const"() <{value = dense<[[0, 1]]> : tensor<1x2xi32>}> : () -> tensor<1x2xi32>
          %arg1_reduced = "tf.XlaAllReduce"(%arg1_id, %group) <{mode = "CrossReplica", reduce_op = "Add"}> : (tensor<f32>, tensor<1x2xi32>) -> tensor<f32>
          "tf.OpA"(%arg1_reduced) : (tensor<f32>) -> ()
          tf_device.return
        }) : () -> ()
        tf_device.return
      }
      return
    }