// CHECK-SAME:                        %[[VAL_0:.*]]: tensor<1xi1>,
// CHECK-SAME:                        %[[VAL_1:.*]]: tensor<1x2xi1>) -> tensor<1x2xi1> {
// CHECK:           %[[VAL_2:.*]] = "tf.LogicalAnd"(%[[VAL_0]], %[[VAL_1]]) : (tensor<1xi1>, tensor<1x2xi1>) -> tensor<1x2xi1>
// CHECK:           return %[[VAL_2]] : tensor<1x2xi1>
// CHECK:         }
func.func @and_broadcast(%arg0: tensor<1xi1>, %arg1: tensor<1x2xi1>) -> tensor<1x2xi1> {

// is ui16. ui16 storage type for quantized type is not compatible with
// `tfl.quantize`.

func.func @uniform_quantize_op_uint16_output(%arg: tensor<2x2xf32>) -> tensor<2x2x!quant.uniform<ui16:f32, 3.000000e+0:127>> {
  %0 = stablehlo.uniform_quantize %arg : (tensor<2x2xf32>) -> tensor<2x2x!quant.uniform<ui16:f32, 3.000000e+0:127>>
  return %0 : tensor<2x2x!quant.uniform<ui16:f32, 3.000000e+0:127>>
}

}

func.func @broadcast_to_i16_low_dim(%input: tensor<3xi16>, %shape: tensor<2xi32>) -> tensor<3x3xi16> {
  %0 = "tf.BroadcastTo"(%input, %shape) : (tensor<3xi16>, tensor<2xi32>) -> tensor<3x3xi16>
  func.return %0: tensor<3x3xi16>

// CHECK-LABEL: broadcast_to_i16_low_dim
// CHECK:    %0 = "tf.BroadcastTo"(%arg0, %arg1) : (tensor<3xi16>, tensor<2xi32>) -> tensor<3x3xi16>
// CHECK:    return %0 : tensor<3x3xi16>
}

}

func TestEmptyMapWithInterfaceKey(t *testing.T) {
	var (
		b    bool
		i    int
		i8   int8
		i16  int16
		i32  int32
		i64  int64
		ui   uint
		ui8  uint8
		ui16 uint16
		ui32 uint32
		ui64 uint64
		uipt uintptr
		f32  float32
		f64  float64
		c64  complex64
		c128 complex128
		a    [4]string
		s    string
		p    *int
		up   unsafe.Pointer

include "stablehlo/dialect/ChloOps.td"
include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td"
include "mhlo/IR/hlo_ops.td"

def SignedIntTensor : TensorOf<[I1, I8, I16, I32, I64]>;
def UnsignedIntTensor : TensorOf<[UI8, UI16, UI32, UI64]>;

// IEEE compliant floating point tensors.
def IEEEFloatTensor : TensorOf<[F16, F32, F64]>;

//===----------------------------------------------------------------------===//
// BatchNorm op patterns.

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

// -----

// CHECK-LABEL: testGatherNdI16Indices
func.func @testGatherNdI16Indices(%arg0 : tensor<?xf32>, %arg1 : tensor<?xi16>) -> tensor<?xf32> {
  %0 = "tfl.gather_nd"(%arg0, %arg1) {axis = 1 : i32}: (tensor<?xf32>,tensor<?xi16>) -> tensor<?xf32>
  func.return %0 : tensor<?xf32>
}

// -----

// CHECK-LABEL: testAbs
func.func @testAbs(tensor<? x f32>) -> tensor<? x f32> {

      ConversionPatternRewriter& rewriter) const final {
    RankedTensorType type =
        mlir::dyn_cast_or_null<RankedTensorType>(iota_op.getType());
    // TF::RangeOp doesn't support UI16.
    if (!type || type.getElementType().isUnsignedInteger(16)) return failure();

    const uint64_t dimension = iota_op.getIotaDimension();
    Type element_type = type.getElementType();

}

// -----

// CHECK-LABEL: func @selectv2_dynamic_ranked
func.func @selectv2_dynamic_ranked(%arg0: tensor<1xi1>, %arg1: tensor<2x?x8xi32>, %arg2: tensor<2x8x8xi32>) -> tensor<2x?x8xi32> {
  // CHECK: chlo.broadcast_select
  %0 = "tf.SelectV2"(%arg0, %arg1, %arg2) : (tensor<1xi1>, tensor<2x?x8xi32>, tensor<2x8x8xi32>) -> tensor<2x?x8xi32>
  func.return %0: tensor<2x?x8xi32>
}