func.func @main() -> tensor<2x2xf32> {
  // CHECK: %[[cst:.*]] = "tfl.pseudo_const"() <{value = dense<2> : tensor<2xi32>}> : () -> tensor<2xi32>
  // CHECK: %{{.*}} = "tfl.reshape"(%{{.*}}, %[[cst]]) : (tensor<4xf32>, tensor<2xi32>) -> tensor<2x2xf32>
  %cst = arith.constant dense<[2, 2]> : tensor<2xi32>
  %0 = "tfl.pseudo_const" () {value = dense<1.0> : tensor<4xf32>} : () -> tensor<4xf32> loc("Const")

// CHECK-LABEL: import_stats_skip
func.func @import_stats_skip(%arg0: tensor<4xf32>, %cst: tensor<i32>) -> (tensor<2xf32>,tensor<2xf32>) {
  %0:2 = "tfl.split"(%cst, %arg0) {num_splits = 2 : i32} : (tensor<i32>, tensor<4xf32>) -> (tensor<2xf32>, tensor<2xf32>)
    loc(fused["skip1", "skip2.cc":10:8, callsite("op" at "skip3.cc":10:8)])

// CHECK-DAG: %[[CST:.*]] = "tf.Const"() {{.*}} : () -> tensor<2x3x3x3x2xf32>

// CHECK: %[[PARTITIONEDCALL_0:.*]] = "tf.PartitionedCall"(%arg0, %[[CST]])
// CHECK-SAME: f = @composite_conv3d_with_relu_fn_1}>
// CHECK-SAME: {_tfl_quant_trait = "fully_quantizable"

// CHECK: %[[PARTITIONEDCALL_1:.*]] = "tf.PartitionedCall"(%arg0, %[[CST]])
// CHECK-SAME: f = @composite_conv3d_with_relu6_fn_1

   (IsIntSplatValueEqual<"int32_t", "-128"> $min_value),
   (IsIntSplatValueEqual<"int32_t", "127"> $max_value)]>;

// This pattern optimizes:
//   (x + cst1) + cst2 -> x + cst
//   (x - cst1) - cst2 -> x - cst
// Where: cst = cst1 + cst2
foreach BinaryOp = [TF_AddV2Op, TF_SubOp] in {
  def OptimizeConsecutive#BinaryOp : Pat<
    (BinaryOp
      (BinaryOp $x, (TF_ConstOp:$cst1 $cst1_value)),

func.func @preparePrelu(%arg0: tensor<1x10x10x3xf32>) -> tensor<1x10x10x3xf32> {
  %cst = "arith.constant"() {value = dense<[[[1.66394591, 3.61694336, 2.0382936]]]> : tensor<1x1x3xf32>} : () -> tensor<1x1x3xf32>
  %prelu = "tfl.prelu"(%arg0, %cst) : (tensor<1x10x10x3xf32>, tensor<1x1x3xf32>) -> tensor<1x10x10x3xf32>
  func.return %prelu : tensor<1x10x10x3xf32>

  // CHECK:         [[cst:%.*]] = "tf.Const{{.*}} dense<{{\[}}2, 4]> : tensor<2xi32>
  // CHECK-NEXT:    return [[cst]] : tensor<2xi32>
  func.return %0: tensor<2xi32>
}

func.func @RemoveTrivialAdd(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
  %cst = arith.constant dense<0.0> : tensor<2x2xf32>
  %0 = "tf.Add"(%arg0, %cst) : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>

}

// CHECK-LABEL: @StridedSliceEllipsisMaskBefore
func.func @StridedSliceEllipsisMaskBefore(%arg0: tensor<21x15x7xf32>) -> tensor<21x15x2xf32> {
  %cst = arith.constant dense<0> : tensor<2xi32>
  %cst_0 = arith.constant dense<1> : tensor<2xi32>

  %1 = "tfl.add"(%0, %cst) {fused_activation_function = "NONE"} : (tensor<256x8x7x16xf16>, tensor<f16>) -> tensor<256x8x7x16xf16>
  func.return %1 : tensor<256x8x7x16xf16>

  %arg19: tensor<1x640xf32>,
  %arg20: tensor<1x2048xf32>
) -> tensor<1x640xf32> {
  %cst = "tfl.no_value"() {value = unit} : () -> none
  %zero = "tfl.pseudo_const"() {value = dense<0.0> : tensor<640xf32>} : () -> tensor<640xf32>
  %0 = "tfl.lstm"(%arg0, %arg1, %arg2, %arg3, %arg4, %arg5, %arg6, %arg7, %arg8, %cst, %cst, %cst, %arg9, %arg10, %arg11, %arg12, %arg13, %zero, %arg19, %arg20, %arg15, %arg16, %arg17, %arg18) ({}) {

//
// cst = value that is > 0
// rem = remainder(arg0, cst)
// for i in 0 to len(arg1):
//    if (rem[i] < 0 && rem[i] != 0)
//       rem[i] += cst
// return rem
def : Pat<(MHLO_SelectOp
            (MHLO_AndOp
              (MHLO_CompareOp:$rltz
                (MHLO_RemOp:$rem $arg, (MHLO_ConstantOp $cst)),
                (MHLO_ConstantOp $cst1),