// `tfl.dequantize`.

func.func @uniform_dequantize_op_return_f64(%arg: tensor<2x2x!quant.uniform<i8:f64, 1.000000e+0:8>>) -> tensor<2x2xf64> {
  %0 = stablehlo.uniform_dequantize %arg : (tensor<2x2x!quant.uniform<i8:f64, 1.000000e+0:8>>) -> tensor<2x2xf64>
  return %0 : tensor<2x2xf64>
}
// CHECK-LABEL: uniform_dequantize_op_return_f64
// CHECK: stablehlo.uniform_dequantize
// CHECK-NOT: tfl.dequantize

// -----

// -----

func.func @testDynamicStitch(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
  %indices = "tf.Const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
  %0 = "tf.DynamicStitch"(%indices, %arg0) : (tensor<2xi32>, tensor<2x2xf32>) -> tensor<2x2xf32>
  func.return %0 : tensor<2x2xf32>
}

// -----

func.func @testDynamicStitch() -> tensor<2x2xf32> {
  // expected-error @+1 {{requires attribute N with value >= 1}}

        %1 = "tf.OpB"() {_XlaSharding = "\08\03\1A\02\02\01\22\02\00\02"} : () -> tensor<2x2xi64>
        // expected-error @+1 {{should have the same sharding}}
        %2 = "tf.OpC"(%0, %1) {_xla_map_outside_compilation = "0", _xla_outside_compilation = "from_launch"} : (tensor<2x2xi64>, tensor<2x2xi64>) -> tensor<2x2xi64>
        "tf.OpD"(%2) : (tensor<2x2xi64>) -> ()
        tf_device.return

  %0 = "tf.Squeeze"(%arg0) : (tensor<1x2x2xf32>) -> tensor<2x2xf32>
  func.return %0 : tensor<2x2xf32>

// CHECK-LABEL:squeezeDefault
// CHECK:  "tfl.squeeze"(%arg0) <{squeeze_dims = []}> : (tensor<1x2x2xf32>) -> tensor<2x2xf32>
}

func.func @squeezeSingleAxis(%arg0: tensor<2x1x2xf32>) -> tensor<2x2xf32> {
  %0 = "tf.Squeeze"(%arg0) {squeeze_dims = [1]} : (tensor<2x1x2xf32>) -> tensor<2x2xf32>

// CHECK-LABEL: testAddWithI64Broadcasting
func.func @testAddWithI64Broadcasting(tensor< 2x3xi64>, tensor<3xi64>) -> tensor<2x3xi64> {
^bb0(%arg0: tensor<2x3xi64>, %arg1: tensor<3xi64>):
  // CHECK: tfl.add(%arg0, %arg1)
  %0 = tfl.add(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor< 2x3xi64>, tensor<3xi64>) -> tensor<2x3xi64>
  func.return %0#0 : tensor<2x3xi64>
}

// -----

// CHECK-LABEL: add_with_i32_five_dim_broadcasting

        %6 = "tf.TensorListSetItem"(%3, %4, %5) {device = ""} : (tensor<!tf_type.variant<tensor<*xf32>>>, tensor<i32>, tensor<2x2xf32>)-> tensor<*x!tf_type.variant>
        %7 = "tf.Const"() {device = "", value = dense<-1> : tensor<i32>} : () -> tensor<i32>

func.func @testBatchMatMulToMatMul(%arg0: tensor<2x3xf32>, %arg1: tensor<3x2xf32>) -> tensor<2x2xf32> {
  // CHECK: %0 = "tf.MatMul"(%arg0, %arg1) <{grad_a = false, grad_b = false, transpose_a = false, transpose_b = false}> {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<2x3xf32>, tensor<3x2xf32>) -> tensor<2x2xf32>

	0x2f37: 0x000c,
	// Block 0xbd, offset 0x2f40
	0x2f5d: 0x000c,
	0x2f5e: 0x000c, 0x2f5f: 0x000c, 0x2f62: 0x000c, 0x2f63: 0x000c,
	0x2f64: 0x000c, 0x2f65: 0x000c, 0x2f67: 0x000c, 0x2f68: 0x000c, 0x2f69: 0x000c,
	0x2f6a: 0x000c, 0x2f6b: 0x000c,
	// Block 0xbe, offset 0x2f80
	0x2faf: 0x000c,