assert lhs_ty, rhs_ty
    if isinstance(op, ast.Sub):
      code = 'arith.sub'
    elif isinstance(op, ast.Add):
      code = 'arith.add'
    elif isinstance(op, ast.Mult):
      code = 'arith.mul'
    elif isinstance(op, ast.Div):
      code = 'arith.div'
    else:
      raise NotImplementedError('BinOp operator not recognized' + op)

func.func @modified(%arg0: tensor<1x224x224x3xf32>) -> tensor<1x401408xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "input", outputs = "output"}} {
  %cst = arith.constant dense<[1, 401408]> : tensor<2xi32>
  %0 = "tfl.quantize"(%arg0) {qtype = tensor<1x224x224x3x!quant.uniform<i8:f32, 7.812500e-03>>} : (tensor<1x224x224x3xf32>) -> tensor<1x224x224x3x!quant.uniform<i8:f32, 7.812500e-03>>

# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "unranked"
# CHECK-SAME:  outputs = "unranked,static,static_10"
# CHECK-DAG:         [[VAL_2:%.*]] = arith.constant dense<0> : tensor<10xi32>
# CHECK-DAG:         [[VAL_1:%.*]] = arith.constant dense<0> : tensor<i32>
# CHECK:         return [[VAL_0]], [[VAL_1]], [[VAL_2]] : tensor<1x8x8x2xi32>, tensor<i32>, tensor<10xi32>

include "mlir/IR/OpBase.td"
include "mlir/IR/PatternBase.td"
include "mlir/Dialect/Func/IR/FuncOps.td"
include "mlir/Dialect/Arith/IR/ArithOps.td"
include "tensorflow/compiler/mlir/quantization/common/attrs_and_constraints.td"
include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td"

// Converts tf.Const to arith.constant for statically shaped, non-opaque constants.
// Needed for QuantizationDriver to recognize constants.

#include <cstdint>
#include <memory>
#include <optional>
#include <utility>

#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/Support/Casting.h"
#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/BuiltinAttributes.h"  // from @llvm-project
#include "mlir/IR/BuiltinTypes.h"  // from @llvm-project

  // CHECK-SAME: tf_saved_model.initializer_type = "restore_op"
  // CHECK: %[[VAR:.*]] = "tf.VarHandleOp"
  // CHECK-SAME: "var1"
  // CHECK: %[[CST:.*]] = arith.constant dense<> : tensor<0xf32>
  // CHECK: "tf.AssignVariableOp"(%[[VAR]], %[[CST]])
}

// -----

// Test with an existing session init function.

	for _, p := range parities {
		occMap[p]++
	}

	var maxOcc, cparity int
	for parity, occ := range occMap {
		if parity == -1 {
			// Ignore non defined parity
			continue
		}

		readQuorum := N - parity
		if defaultParityCount > 0 && parity == 0 {
			// In this case, parity == 0 implies that this object version is a
			// delete marker
			readQuorum = N/2 + 1
		}

  func.func @main() {
// CHECK: arith.constant
// CHECK: TPUCompile
// CHECK: TPUExecute
// CHECK-NOT: func @_func
    tf_executor.graph {
      %outputs, %control = tf_executor.island wraps "arith.constant"() {value = dense<2.000000e+00> : tensor<f32>} : () -> tensor<f32>
      %outputs_0, %control_1 = tf_executor.island wraps "arith.constant"() {value = dense<3.000000e+00> : tensor<f32>} : () -> tensor<f32>

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

#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/Dialect/Quant/QuantTypes.h"  // from @llvm-project
#include "mlir/IR/BuiltinAttributeInterfaces.h"  // from @llvm-project

}

func.func @cond(%arg0: tensor<*xi32>, %arg1: tensor<*xf32>) -> tensor<i1> {
  %cst = arith.constant dense<0> : tensor<i32> loc("Const")
  %0 = "tfl.greater"(%arg0, %cst) : (tensor<*xi32>, tensor<i32>) -> tensor<i1>
  func.return %0 : tensor<i1>
}

func.func @body(%arg0: tensor<*xi32>, %arg1: tensor<*xf32>) -> (tensor<*xi32>, tensor<*xf32>) {
  %cst = arith.constant dense<1> : tensor<i32> loc("Const")