when:
        logger().error("{}", arg1)

        then:
        singleLogEvent().message("arg1").logLevel(ERROR)

        when:
        logger().log(ERROR, "{}", arg1)

        then:
        singleLogEvent().message("arg1").logLevel(ERROR)

        when:
        logger().error("{} {}", arg1, arg2)

        then:
        singleLogEvent().message("arg1 arg2").logLevel(ERROR)

        when:

  // CHECK-NEXT:  mhlo.minimum %arg0, %arg1 : tensor<4xf32>
  %0 = "tf.Minimum"(%arg0, %arg1) : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
  func.return %0 : tensor<4xf32>
}

// CHECK-LABEL: func @mod
// CHLO-LABEL: func @mod
func.func @mod(%arg0: tensor<4xf32>, %arg1: tensor<4xf32>) -> tensor<4xf32> {
  // CHECK-NEXT:  mhlo.remainder %arg0, %arg1 : tensor<4xf32>

}

func.func @einsum_basic(%arg0: tensor<3x4x5xf32>, %arg1: tensor<3x5x6xf32>) -> tensor<3x4x6xf32> {
  %0 = "tf.Einsum"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", equation = "ijk,ikm->ijm"}: (tensor<3x4x5xf32>, tensor<3x5x6xf32>) -> tensor<3x4x6xf32>
  func.return %0 : tensor<3x4x6xf32>
  // CHECK-LABEL: einsum_basic

// CHECK:}

func.func @add(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
  %0 = "vhlo.add_v1"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  func.return %0 : tensor<1xf32>
}

// CHECK:func.func private @add(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
// CHECK: %0 = "vhlo.add_v1"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

      // CHECK:      [[SEND_TOKEN:%.*]] = "mhlo.send"([[ARG1]], [[INIT_TOKEN]])
      "tf.XlaSendToHost"(%arg1) {key = "send_key"} : (tensor<f32>) -> ()

      // CHECK:      mhlo.return [[ARG1]], [[SEND_TOKEN]]
      "mhlo.return"(%arg1) : (tensor<f32>) -> ()

    // CHECK-NEXT: }, {
    },  {

      // CHECK:      mhlo.return [[ARG1]], [[INIT_TOKEN]]
      "mhlo.return"(%arg1) : (tensor<f32>) -> ()

// CHECK-LABEL: conv2DBiasAdd_noActivation
func.func @conv2DBiasAdd_noActivation(%arg0: tensor<128xf32>, %arg1: tensor<1x1x3x128xf32>, %arg2: tensor<8x32x32x3xf32>) -> (tensor<*xf32>) {

  (TF_LessEqualOp:$dest $arg0, $arg1), [], [(CopyAttrs $src, $dest)]>;

def LogicalNotOfGreaterEqual : Pat<
  (TF_LogicalNotOp:$src (TF_GreaterEqualOp $arg0, $arg1)),
  (TF_LessOp:$dest $arg0, $arg1), [], [(CopyAttrs $src, $dest)]>;

def LogicalNotOfLess : Pat<(
  TF_LogicalNotOp:$src (TF_LessOp $arg0, $arg1)),
  (TF_GreaterEqualOp:$dest $arg0, $arg1), [], [(CopyAttrs $src, $dest)]>;

// CHECK: func private @mul1(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> attributes {tf.entry_function = {inputs = "mul1_y:0,mul1_x:0", outputs = "PartitionedCall:0"}}
// CHECK:   %[[MUL_0:.*]] = "tf.Mul"(%arg1, %arg0) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
// CHECK:   return %[[MUL_0]] : tensor<1xf32>
// CHECK: }

  // CHECK: "tfl.conv_2d"(%arg0, %arg1, %arg2) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32}> : (tensor<1x112x80x128xf32>, tensor<128x3x3x128xf32>, tensor<128xf32>) -> tensor<1x56x40x128xf32>

    // "then_branch"/"else_branch".
    // "arg0" (T=DT_RESOURCE), "arg1" (T=DT_BOOL)
    // "ret0" = "arg1"
    // "ret1" = "arg0"
    tensorflow::Scope s = tensorflow::Scope::NewRootScope();
    Output arg0 = ops::_Arg(s.WithOpName("arg0"), DT_RESOURCE, 0);
    Output arg1 = ops::_Arg(s.WithOpName("arg1"), DT_BOOL, 1);
    auto ret0 = ops::_Retval(s.WithOpName("ret0"), arg1, 0);
    auto ret1 = ops::_Retval(s.WithOpName("ret1"), arg0, 1);