layerStats = dense<[-1.28e-5, 1.27e-5]> : tensor<2xf32>
  } : (tensor<1x5x5x2xf32>) -> tensor<1x5x5x2xf32>
  %w = arith.constant dense<[[[[-1.0, 1.0]]], [[[1.0, 2.0]]], [[[-2.0, 1.0]]]]> : tensor<3x1x1x2xf32>
  %b = arith.constant dense<0.0> : tensor<3xf32>
  %b2 = arith.constant dense<[1.0e-2, 2.1473647e1, -2.1473647e2]> : tensor<3xf32>
  %conv = "tfl.conv_2d"(%0, %w, %b) {

func.func @foldDiv() -> tensor<2xf32> {
  %0 = stablehlo.constant dense<[2.0, 3.0]> : tensor<2xf32>
  %1 = stablehlo.constant dense<[4.0, 6.0]> : tensor<2xf32>
  %2 = stablehlo.divide %0, %1 : tensor<2xf32>
  return %2 : tensor<2xf32>
}

// CHECK-LABEL: foldDiv
// CHECK: stablehlo.constant dense<5.000000e-01> : tensor<2xf32>

// -----

func.func @foldDivLHSSplat() -> tensor<2xf32> {
  %0 = stablehlo.constant dense<2.0> : tensor<2xf32>

    %1 = stablehlo.constant dense<1.000000e+03> : tensor<1x1x1x1xf32>  // Input inverse scale.
    %2 = stablehlo.constant dense<-128> : tensor<1x1x1x1xi8>  // Input zero point.
    %3 = stablehlo.constant dense<1> : tensor<3x3x4x4xi8>  // Quantized filter tensor.
    %4 = stablehlo.constant dense<3.000000e+03> : tensor<1x1x1x4xf32>
    %5 = stablehlo.constant dense<4.000000e+03> : tensor<1x1x1x1xf32>  // Output inverse scale.

  %perm = arith.constant dense<[3, 0, 1, 2]> : tensor<4xi32>
  %0 = "tfl.transpose"(%arg0, %perm) : (tensor<2x3x4x5xf32>, tensor<4xi32>) -> tensor<5x2x3x4xf32>
  %cst = arith.constant dense<1.0> : tensor<5x2x3x4xf32>
  %1 = tfl.add %0, %cst { fused_activation_function = "NONE" } : tensor<5x2x3x4xf32>
  func.return %1 : tensor<5x2x3x4xf32>
}

// CHECK: %cst = arith.constant dense<[3, 0, 1, 2]> : tensor<4xi32>

func.func @dot_general_with_bias_same_shape_and_relu6_fn(%arg0: tensor<1x1x167xf32>) -> tensor<1x1x64xf32> {
  %0 = stablehlo.constant dense<2.000000e+00> : tensor<167x64xf32>
  %1 = stablehlo.constant dense<2.000000e+00> : tensor<1x1x64xf32>
  %2 = stablehlo.constant dense<0.000000e+00> : tensor<1x1x64xf32>
  %3 = stablehlo.constant dense<6.000000e+00> : tensor<1x1x64xf32>

  %cst = arith.constant dense<[1, 0]> : tensor<2xi32>
  %cst_0 = arith.constant dense<0> : tensor<i32>
  %cst_1 = arith.constant dense<-1> : tensor<i32>
  %cst_2 = arith.constant dense<1> : tensor<i32>
  %0 = "tf.AddV2"(%arg2, %cst_2) {T = i32, device = ""} : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>

  %cst = arith.constant dense<1.5> : tensor<32xf32>
  %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<16xf32>
  %cst_1 = arith.constant dense<[1, 64, 84, 32]> : tensor<4xi32>
  %cst_2 = arith.constant dense<1.0> : tensor<32x4x4x128xf32>

	}

	for range 1.1 /* ERROR "cannot range over 1.1 (untyped float constant)" */ {
	}
	for range 1i /* ERROR "cannot range over 1i (untyped complex constant (0 + 1i))" */ {
	}

	for i := range 10.0 /* ERROR "cannot range over 10.0 (untyped float constant 10)" */ {
		_ = i
	}
	for i := range 1e3 /* ERROR "cannot range over 1e3 (untyped float constant 1000)" */ {
		_ = i

  // CHECK: %[[CONSTANT_0:.*]] = stablehlo.constant dense<1.000000e+03> : tensor<1024x3xf32>
  // CHECK: %[[CONSTANT_1:.*]] = stablehlo.constant dense<1.000000e+03> : tensor<1x3xf32>
  // CHECK: return
  // CHECK: }

  // CHECK: func private @_stablehlo_main_0
  // CHECK: %[[CONSTANT_0:.*]] = stablehlo.constant dense<1.000000e+03> : tensor<3x64xf32>
  // CHECK: %[[CONSTANT_1:.*]] = stablehlo.constant dense<1.000000e+03> : tensor<1x64xf32>

    %cst_3 = arith.constant dense<1.0> : tensor<20x20xf32>
    %cst_7 = arith.constant dense<1.0> : tensor<20xf32>
    %recurrent_input = arith.constant dense<1.0> : tensor<1x20xf32>
    %recurrent_stats = "quantfork.stats"(%recurrent_input) {layerStats = dense<[-2.0, 1.0]> : tensor<2xf32>} : (tensor<1x20xf32>) -> tensor<1x20xf32>
    %cell_input = arith.constant dense<1.0> : tensor<1x20xf32>