}

// CHECK-LABEL: testDynamicBatchMatMulToV2
func.func @testDynamicBatchMatMulToV2(%arg0: tensor<2x3x5xf32>, %arg1: tensor<?x5x7xf32>) -> tensor<2x3x7xf32> {
  // CHECK: "tf.BatchMatMul"(%arg0, %arg1) <{adj_x = false, adj_y = false}> {device = "/job:localhost/replica:0/task:0/device:GPU:0"}

func.func @testBiasAdd(%arg0: tensor<2x3x5x7xf32>, %arg1: tensor<5x7xf32>) -> tensor<2x3x5x7xf32> {
  // expected-error @+1 {{requires bias operand to have rank exactly one}}
  %0 = "tf.BiasAdd"(%arg0, %arg1) {data_format = "NHWC"} : (tensor<2x3x5x7xf32>, tensor<5x7xf32>) -> tensor<2x3x5x7xf32>
  func.return %0 : tensor<2x3x5x7xf32>
}

// -----

func.func @testBiasAdd(%arg0: tensor<2x3x5x7xf32>, %arg1: tensor<5xf32>) -> tensor<2x3x5x7xf32> {

  // 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
}

func.func @testSelectV2WithWrongBroadcastableArguments(%cond : tensor<3x4xi1>, %arg0 : tensor<2x3x4xf32>, %arg1 : tensor<4x3xf32>) -> tensor<2x3x4xf32> {
  // expected-error @+1 {{'tfl.select_v2' op operands don't have broadcast-compatible shapes}}
  %0 = "tfl.select_v2"(%cond, %arg0, %arg1): (tensor<3x4xi1>, tensor<2x3x4xf32>, tensor<4x3xf32>) -> tensor<2x3x4xf32>
  func.return %0 : tensor<2x3x4xf32>
}

// -----

// CHECK-LABEL: topk

    // CHECK: tf.Reshape
    // CHECK-SAME: tensor<1x3x7xf32>
    %1 = "tf.Reshape"(%arg1, %0) : (tensor<3x7xf32>, tensor<3xi32>) -> tensor<*xf32>
    return %1 : tensor<*xf32>
  }

  // CHECK-LABEL: func @gather_concat_reshape
  func.func @gather_concat_reshape(%arg0: tensor<?x1x7xf32>, %arg1: tensor<?x7xf32>) -> tensor<*xf32> {
    %0 = "tf.Shape"(%arg0) : (tensor<?x1x7xf32>) -> tensor<3xi32>