}

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>
}

// Test invalid tf.ToBool
func.func @testInvalidToBool(%arg0: tensor<i32>) -> tensor<1xi1> {
  // expected-error @+2 {{'tf.ToBool' op failed to infer returned types}}
  // expected-error @+1 {{op inferred type(s) 'tensor<i1>' are incompatible with return type(s) of operation 'tensor<1xi1>'}}
  %0 = "tf.ToBool"(%arg0) : (tensor<i32>) -> tensor<1xi1>
  func.return %0 : tensor<1xi1>
}

// -----

  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> {

}

// -----

// 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>
}