func.func @generator_op(%str : tensor<!tf_type.string>, %arg0: tensor<*x!tf_type.string>, %arg1: tensor<!tf_type.string>, %arg2: tensor<*xi64>, %arg3: tensor<!tf_type.string>) {
  tf_executor.graph {
    tf_executor.island {
      // CHECK: %{{.*}}, %[[CONTROL:[^ ,]*]] = tf_executor.island wraps "tf.GeneratorDataset"
      %gen0 = "tf.GeneratorDataset"(%str, %arg0, %arg1, %arg2, %arg3) {
        finalize_func = @__finalize_func_790,

module {
  func.func @main(%arg0: tensor<100xf32>, %arg1: tensor<100xf32>, %arg2: tensor<100xf32>, %arg3: tensor<100xf32>) -> tensor<2x100xf32> {
    %0 = func.call @func_0_GPU_FLOAT(%arg0, %arg1, %arg2) {tac.device = "GPU", tac.inference_type = "FLOAT", tac.interface_name = "func_0"} : (tensor<100xf32>, tensor<100xf32>, tensor<100xf32>) -> tensor<100xf32>

  }
// 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: }

func.func @generator_op(%str : tensor<!tf_type.string>, %arg0: tensor<*x!tf_type.string>, %arg1: tensor<!tf_type.string>, %arg2: tensor<*xi64>, %arg3: tensor<!tf_type.string>) {
  tf_executor.graph {
    tf_executor.island {
      // CHECK: %{{.*}}, %[[CONTROL:[^ ,]*]] = tf_executor.island wraps "tf.GeneratorDataset"
      %gen0 = "tf.GeneratorDataset"(%str, %arg0, %arg1, %arg2, %arg3) {
        finalize_func = @__finalize_func_790,

// CHECK-SAME:  ([[ARG0:%.*]]: tensor<i1>, [[ARG1:%.*]]: tensor<f32>, [[ARG2:%.*]]: tensor<f32>)
func.func @if_true_branch(%arg0: tensor<i1>, %arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<f32> {
  // CHECK: [[INIT_TOKEN:%.*]] = mhlo.create_token

  // CHECK: [[IF:%.*]]:2 = "mhlo.if"([[ARG0]])
  %0 = "mhlo.if"(%arg0) ({
    // CHECK:      [[TRUE_SEND_TOKEN:%.*]] = "mhlo.send"([[ARG1]], [[INIT_TOKEN]])

        runner.pluginClasspath == originalClasspath

        when:
        originalArguments << 'arg5'
        originalJvmArguments << 'arg6'
        originalClasspath << new File('file:///Users/foo/bar/other.jar')

        then:
        runner.arguments == ['arg1', 'arg2']
        runner.jvmArguments == ['arg3', 'arg4']
        runner.pluginClasspath == [new File('/Users/foo/bar/test.jar').absoluteFile]
    }

module {
  func.func @simpleTest(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>, %arg3: tensor<1xf32>) -> tensor<2x1xf32> {
    %0 = func.call @func_0_GPU_FLOAT(%arg0, %arg1, %arg2) {tac.interface_name = "func_0"} : (tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
    %1 = func.call @func_1_GPU_FLOAT(%arg0, %arg3) {tac.interface_name = "func_1"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

		{name: "SUBD", argLength: 2, reg: fp21, asm: "SUBD"},                    // arg0 - arg1
		{name: "MULF", argLength: 2, reg: fp21, asm: "MULF", commutative: true}, // arg0 * arg1
		{name: "MULD", argLength: 2, reg: fp21, asm: "MULD", commutative: true}, // arg0 * arg1
		{name: "DIVF", argLength: 2, reg: fp21, asm: "DIVF"},                    // arg0 / arg1

		{name: "FMSUBS", argLength: 3, reg: fp31, asm: "FMSUBS", commutative: true, typ: "Float32"},                                         // (arg0 * arg1) - arg2
		{name: "FNMADDS", argLength: 3, reg: fp31, asm: "FNMADDS", commutative: true, typ: "Float32"},                                       // -(arg0 * arg1) + arg2

// CHECK-LABEL: func @replicate_result
func.func @replicate_result(%arg0: tensor<i1>, %arg1: tensor<i1>) {
  %0:4 = tf_executor.graph {
    %1:5 = tf_executor.island {
      %2:4 = tf_device.replicate([%arg0, %arg1] as %arg2: tensor<i1>) {n = 2 : i32} {
        %3 = "tf.opA"(%arg2) : (tensor<i1>) -> tensor<f32>
        %4 = "tf.opB"(%arg2) : (tensor<i1>) -> tensor<i32>
        tf_device.return %3, %4 : tensor<f32>, tensor<i32>