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"} : () -> ()

// run arg1 arg2

// Copyright 2009 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

// Test os.Args.

package main

import "os"

func main() {
	if len(os.Args) != 3 {
		panic("argc")
	}
	if os.Args[1] != "arg1" {
		panic("arg1")
	}
	if os.Args[2] != "arg2" {
		panic("arg2")
	}

  func.return %0 : tensor<3xi64>
}

// -----

// CHECK-LABEL: func @testReshape(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>, %arg2: tensor<10000xf32>, %arg3: tensor<*xi32>)
func.func @testReshape(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>, %arg2: tensor<10000xf32>, %arg3: tensor<*xi32>) -> (tensor<100x100xf32>, tensor<*xf32>, tensor<100x100xf32>, tensor<100x100xf32>, tensor<*xf32>, tensor<*xf32>) {

		{name: "ADDF", argLength: 2, reg: fp21, asm: "ADDF", commutative: true}, // arg0 + arg1
		{name: "ADDD", argLength: 2, reg: fp21, asm: "ADDD", commutative: true}, // arg0 + arg1
		{name: "SUBF", argLength: 2, reg: fp21, asm: "SUBF"},                    // arg0 - arg1
		{name: "SUBD", argLength: 2, reg: fp21, asm: "SUBD"},                    // arg0 - arg1

func.func @main(%arg0: tensor<f32>, %arg1: tensor<f32>, %arg2: tensor<4xf32>, %arg3: tensor<4xf32>) -> (tensor<4xf32>, tensor<4xf32>) {
  %0:2 = tf_executor.graph {
    %outputs_2, %control_3 = tf_executor.island wraps "tf.Less"(%arg0, %arg1) : (tensor<f32>, tensor<f32>) -> tensor<i1>

// CHECK:           return %[[VAL_9]] : tensor<384x512xf32>
// CHECK:         }

// -----

func.func @padConcatTo4D(%arg0: tensor<384x384xf32>, %arg1: tensor<384x384xf32>, %arg2: tensor<384x384xf32>, %arg3: tensor<384x384xf32>) -> tensor<1536x384xf32> {

    def __init__(self, *args, **kwargs) -> None: ...
    def create(self, arg0: Location, arg1: list[Value]) -> Operation: ...

class Tf_AddV2Op:
    def __init__(self, *args, **kwargs) -> None: ...
    def create(self, arg0: Location, arg1: Value, arg2: Value) -> Operation: ...

class Tf_AnyOp:
    def __init__(self, *args, **kwargs) -> None: ...
    def create(self, arg0: Location, arg1: Value, arg2: Value, arg3: bool) -> Operation: ...

// CHECK-NEXT:  [[ADD_2:%.*]] = "tf.AddV2"(%arg0, [[CST]])
// CHECK-NEXT:  return [[ADD_2]], %arg1, [[ADD]], [[TENSOR_MAP_INSERT_RESULT]]

func.func @otherVariantWhileCond(%arg0: tensor<i32>, %arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<!tf_type.variant>) -> tensor<i1> {
  %0 = "tf.Const"() {value = dense<10> : tensor<i32>} : () -> tensor<i32>

// CHECK-NEXT: }

func.func @main(%arg0: tensor<i32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
  %0:2 = "tfl.while"(%arg0, %arg1) ({
  ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>):
    %1 = func.call @cond(%arg2, %arg3) : (tensor<*xi32>, tensor<*xf32>) -> tensor<i1>
    "tfl.yield"(%1) : (tensor<i1>) -> ()
  },  {
  ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>):

  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU", tac.device = "GPU"} : (tensor<10x10x10xf32>, tensor<f32>) -> tensor<10x10x10xf32>
  func.return %0 : tensor<10x10x10xf32>
}

// -----

// CHECK: tac.cost = 2.000000e+03