// CHECK:  return
}

func.func @pow(%arg0: tensor<2x1x3xf32>, %arg1: tensor<2x1x1xf32>) -> tensor<2x1x3xf32> {
  %0 = "tf.Pow"(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<2x1x1xf32>) -> tensor<2x1x3xf32>
  func.return %0 : tensor<2x1x3xf32>

  // CHECK-LABEL: pow
  // CHECK:  %[[pow:.*]] = tfl.pow(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<2x1x1xf32>) -> tensor<2x1x3xf32>
  // CHECK:  return
}

   "tfl.yield"(%4) : (tensor<128x128xf32>) -> ()
  }) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>

   "tfl.yield"(%4) : (tensor<128x128xf32>) -> ()
  }) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>

   "tfl.yield"(%4) : (tensor<128x128xf32>) -> ()
  }) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>

  %4 = "tfl.relu"(%3) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<128x128xf32>) -> tensor<128x128xf32>
  %5 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<128x128xf32>} : () -> tensor<128x128xf32>
  %6 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<128xf32>} : () -> tensor<128xf32>

  %cst = arith.constant dense<1> : tensor<1xi32>
  %0 = "tfl.exp"(%arg0) : (tensor<8x128xf32>) -> tensor<8x128xf32>
  %1 = "tfl.sum"(%0, %cst) {keep_dims = true} : (tensor<8x128xf32>, tensor<1xi32>) -> tensor<8x1xf32>
  %2 = "tfl.div"(%0, %1) {fused_activation_function = "NONE"} : (tensor<8x128xf32>, tensor<8x1xf32>) -> tensor<8x128xf32>
  func.return %2 : tensor<8x128xf32>

}

// -----

module {
func.func private @func_20_GPU_FLOAT(%arg0: tensor<128x128xf32>, %arg1: tensor<3xi32>) -> tensor<1x128x128xf32> attributes {tac.device = "GPU", tac.inference_type = "FLOAT", tac.interface_name = "func_20"} {
  %0 = "tfl.reshape"(%arg0, %arg1) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<128x128xf32>, tensor<3xi32>) -> tensor<1x128x128xf32>
  func.return %0 : tensor<1x128x128xf32>
}

func.func @testPadUnknownPaddings(tensor<2x1x3xf32>, tensor<*xi32>) -> tensor<? x f32> {
^bb0(%arg0: tensor<2x1x3xf32>, %arg1: tensor<*xi32>):
  %0 = "tfl.pad"(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<*xi32>) -> tensor<? x f32>
  func.return %0#0 : tensor<? x f32>

  // CHECK-LABEL: testPadUnknownPaddings
  // CHECK:  "tfl.pad"(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<*xi32>) -> tensor<?xf32>
  // CHECK:  return
}

// -----

func.func @QuantizePad2(tensor<2x1x3x!quant.uniform<u8:f32, 0.1>>, tensor<2x1x3xf32>, tensor<3x2xi32>) -> (tensor<?xf32>, tensor<?xf32>) {
^bb0(%arg0: tensor<2x1x3x!quant.uniform<u8:f32, 0.1>>, %arg1: tensor<2x1x3xf32>, %arg2: tensor<3x2xi32>):
  %0 = "tfl.dequantize"(%arg0) : (tensor<2x1x3x!quant.uniform<u8:f32, 0.1>>) -> tensor<2x1x3xf32>
  %1 = "tfl.pad"(%arg1, %arg2) : (tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<?xf32>

}

// CHECK-LABEL: testConvertPackToReshapeAxis1
func.func @testConvertPackToReshapeAxis1(%arg0: tensor<2x3xf32>) -> tensor<2x1x3xf32> {
  %0 = "tf.Pack"(%arg0) {axis = 1 : i64, device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<2x3xf32>) -> tensor<2x1x3xf32>
  func.return %0 : tensor<2x1x3xf32>
  // CHECK: %[[SHAPE:.*]] = "tf.Const"() <{value = dense<[2, 1, 3]> : tensor<3xi32>}> : () -> tensor<3xi32>