func.return %0 : tensor<4xf32>
}

func.func @f64() -> tensor<4xf64> {
  // CHECK-LABEL: @f64
  // CHECK: value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00]> : tensor<4xf64>
  %0 = "tfl.pseudo_const"() { value = dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf64> } : () -> tensor<4xf64>
  func.return %0 : tensor<4xf64>
}

func.func @i8() -> tensor<4xi8> {
  // CHECK-LABEL: @i8

  func.return %0 : tensor<4xf32>
}

func.func @f64() -> tensor<4xf64> {
  // CHECK-LABEL: @f64
  // CHECK: value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00]> : tensor<4xf64>
  %0 = "tfl.pseudo_const"() { value = dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf64> } : () -> tensor<4xf64>
  func.return %0 : tensor<4xf64>
}

func.func @i8() -> tensor<4xi8> {
  // CHECK-LABEL: @i8

// -----

func.func @foldDivF64() -> tensor<2xf64> {
  %0 = stablehlo.constant dense<[2.0, 3.0]> : tensor<2xf64>
  %1 = stablehlo.constant dense<[4.0, 6.0]> : tensor<2xf64>
  %2 = stablehlo.divide %0, %1 : tensor<2xf64>
  return %2 : tensor<2xf64>
}

// CHECK-LABEL: foldDivF64
// CHECK: stablehlo.constant dense<5.000000e-01> : tensor<2xf64>

// -----

func.func @foldDivI32() -> tensor<2xi32> {

  // CHECK:  %cst_2 = "tf.Const"() <{value = dense<0> : tensor<4xi64>}> : () -> tensor<4xi64>
  // CHECK-DAG:  %cst_3 = "tf.Const"() <{value = dense<[8, 8, 1, 1]> : tensor<4xi64>}> : () -> tensor<4xi64>
  // CHECK:  %cst_4 = "tf.Const"() <{value = dense<1> : tensor<4xi64>}> : () -> tensor<4xi64>

  func.func @init_all_tables()
  attributes {tf_saved_model.exported_names = ["__tf_saved_model_session_initializer"]} {
    %cst = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi64>
    %cst_0 = arith.constant dense<["a", "b", "c", "d"]> : tensor<4x!tf_type.string>

func.func @typeNotSupportedNotLegalized(%arg0: tensor<!tf_type.variant<tensor<*xf64>>>, %arg1: tensor<i32>, %arg2: tensor<*xf64>) -> tensor<!tf_type.variant<tensor<*xf64>>> {
  %0 = "tf.TensorListSetItem"(%arg0, %arg1, %arg2) : (tensor<!tf_type.variant<tensor<*xf64>>>, tensor<i32>, tensor<*xf64>) -> tensor<!tf_type.variant<tensor<*xf64>>>
  // CHECK-NOT: "tfl.custom"

  %1 = mhlo.constant dense<0.000000e+00> : tensor<f32>
  %2 = "mhlo.pad"(%arg0, %1) {edge_padding_high = dense<[0, 0, 1, 1]> : tensor<4xi64>, edge_padding_low = dense<[0, 0, 1, 1]> : tensor<4xi64>, interior_padding = dense<0> : tensor<4xi64>} : (tensor<1x3x6x6xf32>, tensor<f32>) -> tensor<1x3x8x8xf32>
  %3 = "mhlo.reduce_window"(%2, %1) ({
  ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>):

  // CHECK-LABEL: init_all_tables
  func.func @init_all_tables()
  attributes {tf_saved_model.exported_names = ["__tf_saved_model_session_initializer"]} {
    %cst = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi64>
    %cst_0 = arith.constant dense<["a", "b", "c", "d"]> : tensor<4x!tf_type.string>

// CHECK-LABEL: broadcast_to_to_reshape_i64
func.func @broadcast_to_to_reshape_i64(%arg0: tensor<4x4x4xf32>, %arg1 : tensor<4xi64>) -> tensor<1x4x4x4xf32> {
  %0 = "tfl.broadcast_to"(%arg0, %arg1) : (tensor<4x4x4xf32>, tensor<4xi64>) -> tensor<1x4x4x4xf32>
  // CHECK: "tfl.cast"
  // CHECK-SAME: (tensor<4xi64>) -> tensor<4xi32>
  // CHECK-NEXT: "tfl.reshape"
  // CHECK-SAME: (tensor<4x4x4xf32>, tensor<4xi32>) -> tensor<1x4x4x4xf32>

  func.return %2 : tensor<?x?x?x?xf32>

// CHECK-DAG: %[[CST:.*]] = arith.constant dense<0> : tensor<4xi64>
// CHECK-DAG: %[[CST0:.*]] = arith.constant dense<[1, 9, 23, 768]> : tensor<4xi64>
// CHECK-DAG: %[[CST1:.*]] = arith.constant dense<1> : tensor<4xi64>