log(logLevel, throwable, format, new Object[]{arg});
    }

    private void log(LogLevel logLevel, Throwable throwable, String format, Object arg1, Object arg2) {
        log(logLevel, throwable, format, new Object[]{arg1, arg2});
    }

    private void log(LogLevel logLevel, Throwable throwable, String format, Object[] args) {
        FormattingTuple tuple = MessageFormatter.arrayFormat(format, args);

// CHECK:         }
func.func @broadcast_add(%arg0: tensor<1x1xf32>, %arg1: tensor<1x1000xf32>) -> (tensor<1x1000xf32>, tensor<1x1000xf32>) {
  %0 = "mhlo.broadcast_in_dim"(%arg0) <{broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>}> : (tensor<1x1xf32>) -> tensor<1x1000xf32>
  %1 = mhlo.add %0, %arg1 : tensor<1x1000xf32>
  %2 = mhlo.add %arg1, %0 : tensor<1x1000xf32>
  func.return %1, %2 : tensor<1x1000xf32>, tensor<1x1000xf32>
}

		{name: "FMUL", argLength: 2, reg: fp21, asm: "FMUL", commutative: true},   // arg0*arg1
		{name: "FMULS", argLength: 2, reg: fp21, asm: "FMULS", commutative: true}, // arg0*arg1

		{name: "FMADD", argLength: 3, reg: fp31, asm: "FMADD"},   // arg0*arg1 + arg2
		{name: "FMADDS", argLength: 3, reg: fp31, asm: "FMADDS"}, // arg0*arg1 + arg2
		{name: "FMSUB", argLength: 3, reg: fp31, asm: "FMSUB"},   // arg0*arg1 - arg2

// CHECK-LABEL: bias_add
func.func @bias_add(%arg0: tensor<1x10x10x32xf32>, %arg1: tensor<32xf32>) -> tensor<1x10x10x32xf32> {
  %0 = "tf.BiasAdd"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NHWC"} : (tensor<1x10x10x32xf32>, tensor<32xf32>) -> tensor<1x10x10x32xf32>
  func.return %0 : tensor<1x10x10x32xf32>

  // CHECK-NOT:   func.call
  func.func @while_test_remove_unused_results(%arg0: tensor<i32>, %arg1: tensor<i32>) -> (tensor<i32>, tensor<i32>) {
    %0:2 = "tf.While"(%arg0, %arg1) { cond = @while_cond_lt9, body = @while_body_add2, is_stateless = false, parallel_iterations = 1} : (tensor<i32>, tensor<i32>) -> (tensor<i32>, tensor<i32>)
    %1:2 = func.call @while_body_add2(%arg0, %arg1) : (tensor<i32>, tensor<i32>) -> (tensor<i32>, tensor<i32>)

func.func @num_replicas_replicated(%arg0: tensor<i32>, %arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<i32>) -> (tensor<i32>, tensor<i32>) {
  %0:2 = tf_executor.graph {
    %control = tf_executor.island() wraps "tf.TPUReplicateMetadata"() {_tpu_replicate = "cluster", device = "/device:TPU:0", num_replicas = 2, topology = "topology"} : () -> ()

  }

  // CHECK-LABEL: func.func private @test_sdpa
  func.func private @test_sdpa(%arg0: tensor<1x100x32x4xf32>, %arg1: tensor<1x500x4x4xf32>, %arg2: tensor<1x500x4x4xf32>, %arg3: tensor<1x1x100x500xf32>, %arg4: tensor<f32>) -> tensor<1x100x32x4xf32> {

func.func @main(tensor<40x37xf32>, tensor<40x37xf32>) -> tensor<40x40xf32> {
^bb0(%arg0: tensor<40x37xf32>, %arg1: tensor<40x37xf32>):
  %cst = arith.constant dense<1.0> : tensor<40xf32>

  // CHECK-SAME: %[[ARG0:.*]]: tensor<1x2xf32>
  // CHECK-SAME: %[[ARG1:.*]]: tensor<2x3xf32>
  func.func private @same_scale_after_composite(%arg0: tensor<1x2xf32>, %arg1: tensor<2x3xf32>) -> tensor<3x1xf32> {
    // CHECK: %[[Q1:.*]] = "quantfork.qcast"(%[[ARG0]]) {volatile} : (tensor<1x2xf32>) -> tensor<1x2x!quant.uniform<i8:f32, 5.000000e-03>>

  // CHECK: %[[MIXED1:.*]] = tfl.logical_and %arg1, %[[CST_MIXED]] : tensor<2x3xi1>