func.func @cast_i1_to_i8() -> tensor<2xi8> {
  %cst = arith.constant dense<[false, true]> : tensor<2xi1>
  %0 = "tfl.cast"(%cst) : (tensor<2xi1>) -> tensor<2xi8>
  func.return %0 : tensor<2xi8>

// CHECK: %[[CST:.*]] = arith.constant dense<[0, 1]> : tensor<2xi8>
// CHECK:  return %[[CST]]
}

// CHECK-LABEL: @cast_i1_to_ui8
func.func @cast_i1_to_ui8() -> tensor<2xui8> {

  // CHECK: %[[CONVERT_1:.*]] = mhlo.bitcast_convert %[[QUANTIZE]] : (tensor<2x!quant.uniform<i8:f32, 1.000000e+00:3>>) -> tensor<2xi8>
  // CHECK: %[[CONVERT_2:.*]] = mhlo.bitcast_convert %[[CONVERT_1]] : (tensor<2xi8>) -> tensor<2x!quant.uniform<i8:f32, 1.000000e+00:3>>
  // CHECK: %[[DEQUANTIZE:.*]] = mhlo.uniform_dequantize %[[CONVERT_2]] : (tensor<2x!quant.uniform<i8:f32, 1.000000e+00:3>>) -> tensor<2xf32>

  func.return %0 : tensor<4xf64>
}

func.func @i8() -> tensor<4xi8> {
  // CHECK-LABEL: @i8
  // CHECK: value = dense<[1, 2, 3, 4]> : tensor<4xi8>
  %0 = "tfl.pseudo_const" () { value = dense<[1, 2, 3, 4]> : tensor<4xi8> } : () -> tensor<4xi8>
  func.return %0 : tensor<4xi8>
}

func.func @i16() -> tensor<4xi16> {
  // CHECK-LABEL: @i16

  func.return %0 : tensor<4xf64>
}

func.func @i8() -> tensor<4xi8> {
  // CHECK-LABEL: @i8
  // CHECK: value = dense<[1, 2, 3, 4]> : tensor<4xi8>
  %0 = "tfl.pseudo_const" () { value = dense<[1, 2, 3, 4]> : tensor<4xi8> } : () -> tensor<4xi8>
  func.return %0 : tensor<4xi8>
}

func.func @i16() -> tensor<4xi16> {
  // CHECK-LABEL: @i16

  func.return %11 : tensor<*xf32>
}

// CHECK: %[[maxpool_i8:.*]] = "tf.MaxPool"
// CHECK-SAME: (tensor<*xi8>) -> tensor<*xi8>
// CHECK: %[[reshape_i8:.*]] = "tf.Reshape"(%[[maxpool_i8]]
// CHECK-SAME: (tensor<*xi8>, tensor<2xi32>) -> tensor<*xi8>
// CHECK: %[[scast:.*]] = "quantfork.scast"(%[[reshape_i8]]
// CHECK: %[[matmul:.*]] = "tf.PartitionedCall"(%[[scast]]
// CHECK-SAME: f = @composite_matmul_fn_1

  // Argument has rank 2, but its dimensions are unknown.
  constexpr absl::string_view kArgumentWithUnknownDims = R"mlir(
    func.func @unknown_dims_arg(%arg: tensor<?x?xi8>) -> tensor<?x?xi8> {
      return %arg : tensor<?x?xi8>
    }
  )mlir";

  OwningOpRef<ModuleOp> module_op =
      ParseModuleOpString(kArgumentWithUnknownDims);
  ASSERT_TRUE(module_op);

// CHECK-LABEL:   func.func @convert_argmax_bool
func.func @convert_argmax_bool(%arg0: tensor<2xi1>) -> tensor<i32> {
  %0 = "mhlo.iota"() <{iota_dimension = 0 : i64}> : () -> tensor<2xi32>
  %1 = mhlo.constant dense<false> : tensor<i1>
  %2 = mhlo.constant dense<0> : tensor<i32>
  %3:2 = mhlo.reduce(%arg0 init: %1), (%0 init: %2) across dimensions = [0] : (tensor<2xi1>, tensor<2xi32>, tensor<i1>, tensor<i32>) -> (tensor<i1>, tensor<i32>)

    %11 = stablehlo.reshape %2 : (tensor<1x1x1x1xi8>) -> tensor<1xi8>
    %12 = stablehlo.broadcast_in_dim %11, dims = [0] : (tensor<1xi8>) -> tensor<1x3x3x4xi8>
    %13 = stablehlo.convert %12 : (tensor<1x3x3x4xi8>) -> tensor<1x3x3x4xf32>
    %14 = stablehlo.convert %3 : (tensor<3x3x4x4xi8>) -> tensor<3x3x4x4xf32>

    %cst_7 = "tfl.pseudo_qconst"() {qtype = tensor<20x!quant.uniform<i8:f32, 0.006:-34>>, value = dense<1> : tensor<20xi8>} : () -> tensor<20x!quant.uniform<i8:f32, 0.006:-34>>
    %cst_11 = "tfl.pseudo_qconst"() {qtype = tensor<20x28x!quant.uniform<i8:f32, 0.006:-34>>, value = dense<1> : tensor<20x28xi8>} : () -> tensor<20x28x!quant.uniform<i8:f32, 0.006:-34>>