func.func @testBatchMatMulV2InvalidBroadcastingBatchDimensionWithSameRank(%lhs: tensor<10x2x5x10xf32>, %rhs: tensor<10x10x10x10xf32>) {
  // expected-error @+1 {{found incompatible broadcast batch dimensions for lhs shape 'tensor<10x2x5x10xf32>' and rhs shape 'tensor<10x10x10x10xf32>'}}
  %0 = "tf.BatchMatMulV2"(%lhs, %rhs) : (tensor<10x2x5x10xf32>, tensor<10x10x10x10xf32>) -> tensor<10x10xf32>
}

// -----

}

// -----

func.func @testPReluWrongOutputRank(%arg0: tensor<10x10x10x10xf32>, %arg1: tensor<10x10x10x10xf32>) -> tensor<10x10xf32> {
  // expected-error @+1 {{'tfl.prelu' op result type '10x10' not broadcast compatible with broadcasted operands's shapes '10x10x10x10'}}
  %0 = "tfl.prelu"(%arg0, %arg1) : (tensor<10x10x10x10xf32>, tensor<10x10x10x10xf32>) -> tensor<10x10xf32>
  func.return %0 : tensor<10x10xf32>
}

// -----

    padding = "SAME", rhs_dilation = [1, 1], strides = [1, 1, 1, 1]
  } : (tensor<2x10x10x10xf32>, tensor<3x3x10x20xf32>) -> tensor<2x10x10x20xf32>
  return %0 : tensor<2x10x10x20xf32>
})mlir";
  constexpr absl::string_view kProgram = R"mlir(
func.func @main(%input: tensor<2x10x10x10xf32>, %filter: tensor<3x3x10x20xi8>) -> tensor<2x10x10x20xf32> {

}

// -----

func.func @rfft2d(%arg0: tensor<10x20x10x30xf32>, %arg1: tensor<2xi32>) -> tensor<10x20x10x30xcomplex<f32>> {
  %0 = "tf.RFFT2D"(%arg0, %arg1) : (tensor<10x20x10x30xf32>, tensor<2xi32>) -> tensor<10x20x10x30xcomplex<f32>>
  func.return %0 : tensor<10x20x10x30xcomplex<f32>>
  // CHECK-LABEL: rfft2d
  // CHECK: "tfl.rfft2d"(%arg0, %arg1) : (tensor<10x20x10x30xf32>, tensor<2xi32>) -> tensor<10x20x10x30xcomplex<f32>>
}