// CHECK-DAG: ml_program.global public @vars.Variable_1([]) : tensor<!tf_type.string>
  func.func @lowers_string_ops(%arg0: tensor<128xi32>, %arg1: tensor<128xi32>, %arg2: tensor<128x1xi32>, %arg3: tensor<128x90xi32>, %arg4: tensor<128x90xi32>, %arg5: tensor<128x90xi32>, %arg6: tensor<128x90x64xf32>, %arg7: tensor<128x90x64xf32>) -> tensor<!tf_type.string> {
    // CHECK: %[[v0:.*]] = ml_program.global_load @vars.Variable_1 : tensor<!tf_type.string>

// CHECK-NEXT: z.b
// CHECK-NEXT: z.c

// -----

// CHECK-LABEL: @terminator
func.func @terminator(%arg0: f32) -> (f32) attributes {ignore_side_effects_for_testing} {
  func.call @terminator(%arg0) : (f32) -> (f32)
  "x.a"(%arg0) : (f32) -> ()
  "y.a"(%arg0) : (f32) -> ()
  "z.a"(%arg0) : (f32) -> ()
  func.return %arg0 : f32
}
// CHECK: x.a
// CHECK: y.a
// CHECK: z.a
// CHECK: return
// CHECK-NOT: call
// CHECK: }

// CHECK-LABEL: @IfOpWithoutCommunicationOps
func.func @IfOpWithoutCommunicationOps(%arg0: tensor<i1>, %arg1: tensor<f32>) -> (tensor<f32>, tensor<f32>) {
  // CHECK: tf.IfRegion
  %0 = "tf.IfRegion"(%arg0) ({
      %mul = "tf.Add"(%arg1, %arg1) : (tensor<f32>, tensor<f32>) -> tensor<f32>
      "tf.Yield"(%mul) : (tensor<f32>) -> ()
    }, {
      %add = "tf.Add"(%arg1, %arg1) : (tensor<f32>, tensor<f32>) -> tensor<f32>

  %7 = "tf.AddV2"(%arg4, %6) {T = f32, device = ""} : (tensor<*xf32>, tensor<?x?xf32>) -> tensor<*xf32>
  %8 = "tf.AddV2"(%arg0, %cst_2) {T = i32, device = ""} : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
  func.return %8, %arg1, %0, %arg3, %7 : tensor<*xi32>, tensor<*xi32>, tensor<*xi32>, tensor<*xf32>, tensor<*xf32>
}

		{{end}}

		// round 1
		{{range $i, $s := dup 4 .Shift1 -}}
			{{printf "arg0 = arg1 + bits.RotateLeft32((((arg2^arg3)&arg1)^arg3)+arg0+x%x+%#08x, %d)" (idx 1 $i) (index $.Table1 $i) $s | relabel}}
			{{rotate -}}
		{{end}}

		// round 2
		{{range $i, $s := dup 4 .Shift2 -}}
			{{printf "arg0 = arg1 + bits.RotateLeft32((((arg1^arg2)&arg3)^arg2)+arg0+x%x+%#08x, %d)" (idx 2 $i) (index $.Table2 $i) $s | relabel}}
			{{rotate -}}

}

func.func private @identity(%arg0: tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>> {
  return %arg0 : tensor<!tf_type.variant<tensor<f32>>>
}

// -----

// CHECK-LABEL: @leaves_remote_calls_alone
func.func @leaves_remote_calls_alone(%arg0: tensor<!tf_type.string>, %arg1: tensor<!tf_type.string>, %arg2: tensor<i64>) {
  // CHECK: RemoteCall

  func.func @__forward_call_369(%arg0: tensor<16x224x224x3xf32> {tf._user_specified_name = "inputs"}, %arg1: tensor<*x!tf_type.resource>, %arg2: tensor<*x!tf_type.resource>, %arg3: tensor<*x!tf_type.resource>, %arg4: tensor<*x!tf_type.resource>)...

// CHECK-LABEL: @add_f32(%arg0: tensor<3x3xbf16>, %arg1: tensor<3x3xbf16>) -> tensor<3x3xbf16>
func.func @add_f32(%arg0: tensor<3x3xf32>, %arg1: tensor<3x3xf32>) -> tensor<3x3xf32> {
  // CHECK-NOT: f32
  // CHECK: stablehlo.add
  %0 = stablehlo.add %arg0, %arg1: (tensor<3x3xf32>, tensor<3x3xf32>) -> tensor<3x3xf32>
  return %0 : tensor<3x3xf32>
}

// -----

  // CHECK: func @f(%arg0: tensor<!tf_type.resource<tensor<f32>>>  {tf_saved_model.index_path = [0]})
  func.func @f(%arg0: tensor<!tf_type.resource<tensor<f32>>>  {tf_saved_model.index_path = [0]})
  attributes {tf_saved_model.exported_names = ["f"]} {
    %val = "tf.ReadVariableOp"(%arg0) : (tensor<!tf_type.resource<tensor<f32>>>) -> tensor<f32>

func.func @FuseTransposeFCRhsToBatchMatmul(%arg0: tensor<16x1024xf32>, %arg1: tensor<1024x128xf32>, %arg2: none) -> tensor<16x128xf32> {
  %cst = arith.constant dense<[1, 0]> : tensor<2xi32>
  %0 = "tfl.transpose"(%arg1, %cst) : (tensor<1024x128xf32>, tensor<2xi32>) -> tensor<128x1024xf32>
  // CHECK: "tfl.batch_matmul"(%arg0, %arg1)