}

}

// CHECK-LABEL: @tfInplaceUpdate
// CHECK-DAG: %[[CST0:.*]] = stablehlo.constant dense<1> : tensor<i32>
// CHECK-DAG: %[[CST1:.*]] = stablehlo.constant dense<0> : tensor<i32>
// CHECK-DAG: %[[CST2:.*]] = stablehlo.constant dense<2.000000e+00> : tensor<1x1x2xf32>

}

// CHECK-LABEL: @splat_scalar_no_change
func.func @splat_scalar_no_change() -> (tensor<f32>, tensor<i32>) {
  // CHECK-NOT: mhlo.broadcast_in_dim
  %cst0 = mhlo.constant dense<0.000000e+00> : tensor<f32>
  %cst1 = mhlo.constant dense<0> : tensor<i32>
  func.return %cst0, %cst1 : tensor<f32>, tensor<i32>

  %cst0 = mhlo.constant dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf32>
  %cst1 = mhlo.constant dense<[[[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]]]]> : tensor<1x1x2x4xf32>
  %0 = "mhlo.broadcast_in_dim"(%cst0) <{broadcast_dimensions = dense<3> : tensor<1xi64>}> : (tensor<4xf32>) -> tensor<1x1x2x4xf32>
  %1 = mhlo.multiply %0, %cst1 : tensor<1x1x2x4xf32>
  // CHECK:      return %[[RES]] : tensor<1x1x2x4xf32>

  %1 = "tf.Mul"(%0, %cst2) : (tensor<1x28x23x2xf32>, tensor<23x2xf32>) -> tensor<1x28x23x2xf32>

  func.return %1 : tensor<1x28x23x2xf32>
  // CHECK: %cst_0 = arith.constant dense<3.000000e+00> : tensor<23x2xf32>
  // CHECK: %0 = "tf.Conv2D"(%arg0, %cst) <{data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]}> {T = "tfdtype$DT_FLOAT"}

  // CHECK:      %[[CST0:.+]] = arith.constant dense<0.000000e+00>
  // CHECK-NEXT: %[[CST1:.+]] = arith.constant dense<0.000000e+00>
  // CHECK-NEXT: "vhlo.add_v1"(%[[CST0]], %[[CST1]]) : (tensor<f32>, tensor<f32>) -> tensor<f32>
  %cst_0 = arith.constant dense<0.000000e+00> : tensor<f32>
  %cst_1 = arith.constant dense<0.000000e+00> : tensor<f32>
  %0 = stablehlo.add %cst_0, %cst_1 : tensor<f32>
  return %0 : tensor<f32>

  module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    func.func @main() -> () {
      %cst0 = "tf.Const"(){ value = dense<0> : tensor<3x5xi1>} : () -> tensor<3x5xi1>
      %0 = "tf.Where"(%cst0) : (tensor<3x5xi1>) -> tensor<?x2xi64>
      func.return
    }
  })";

  CellReader<int64_t> dynamism_counter(kDynamismOpCounterStreamzName);

  // CHECK-DAG: %[[cst0:.*]] = stablehlo.constant dense<0xFF800000> : tensor<f32
  // CHECK-DAG: %[[cst1:.*]] = stablehlo.constant dense<0x7FC00000> : tensor<f32>
  // CHECK-DAG: %[[cst2:.*]] = stablehlo.constant dense<6.000000e+00> : tensor<f32>
  // CHECK-DAG: %[[cst3:.*]] = stablehlo.constant dense<0.000000e+00> : tensor<f32>
  // CHECK-NOT: %[[q0:.*]] = "quantfork.qcast"(%[[cst0]])

// 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 $cst1_value)),
      (TF_ConstOp:$cst2 $cst2_value)),
    (BinaryOp
      $x, (TF_AddV2Op $cst1, $cst2))>;
}

// This pattern optimizes:

func.func @fuseScalarAddIntoConv2dHalf(%arg0: tensor<256x32x32x3xf16>, %arg1: tensor<16x3x3x3xf16>) -> tensor<256x8x7x16xf16> {
  %cst = arith.constant dense<1.5> : tensor<f16>
  %cst_0 = arith.constant dense<[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0]> : tensor<16xf16>

// CHECK-NEXT: %[[V0:.*]] = "tf.PartitionedCall"(%[[ARG1]], %[[CST_1]], %[[CST_0]]) <{config = "", config_proto = "", executor_type = "", f = @quantize_i8}> : (tensor<2x3x3x2xf32>, tensor<f32>, tensor<i32>) -> tensor<2x3x3x2xi8>