func.func @main(%arg0 : tensor<?xf32>) -> tensor<?xf32> {
      %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
      %zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>

      %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) {

# Background Tasks

You can define background tasks to be run *after* returning a response.

This is useful for operations that need to happen after a request, but that the client doesn't really have to be waiting for the operation to complete before receiving the response.

This includes, for example:

* Email notifications sent after performing an action:

// CHECK-NEXT: "tf.ReadVariableOp"
// CHECK-NEXT: "tf.StatefulPartitionedCall"
// CHECK-NEXT:  return 
//
module {
  func.func @serving_default() -> tensor<*xi32> {
    %cst = "tf.Const"() <{value = dense<"some_test.txt"> : tensor<!tf_type.string>}> : () -> tensor<!tf_type.string>
    %0 = "tf.VarHandleOp"() <{container = "", shared_name = "Variable"}> : () -> tensor<!tf_type.resource<tensor<!tf_type.string>>>

limitations under the License.
==============================================================================*/

#ifndef TENSORFLOW_COMPILER_MLIR_TENSORFLOW_UTILS_MLPROGRAM_UTIL_H_
#define TENSORFLOW_COMPILER_MLIR_TENSORFLOW_UTILS_MLPROGRAM_UTIL_H_

#include <memory>
#include <string>

#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "mlir/Support/LogicalResult.h"  // from @llvm-project

limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_COMPILER_MLIR_LITE_SPARSITY_SPARSIFY_MODEL_H_
#define TENSORFLOW_COMPILER_MLIR_LITE_SPARSITY_SPARSIFY_MODEL_H_

#include "absl/status/status.h"
#include "flatbuffers/flatbuffer_builder.h"  // from @flatbuffers
#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"

func.func @serving_default(%arg0: tensor<1x3x4x3xf32>) -> (tensor<1x2x2x2xf32>) attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_input_tensor:0", outputs = "PartitionedCall:0"}} {

// Copyright 2022 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.

#include "go_asm.h"
#include "textflag.h"

#define	REGCTXT	R29

// memequal(a, b unsafe.Pointer, size uintptr) bool
TEXT runtime·memequal<ABIInternal>(SB),NOSPLIT|NOFRAME,$0-25
	BEQ	R4, R5, eq
	ADDV	R4, R6, R7
	PCALIGN	$16
loop:
	BNE	R4, R7, test
	MOVV	$1, R4
	RET
test:

limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_COMPILER_MLIR_LITE_SCHEMA_SCHEMA_UTILS_H_
#define TENSORFLOW_COMPILER_MLIR_LITE_SCHEMA_SCHEMA_UTILS_H_

#include "flatbuffers/flatbuffers.h"
#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"

namespace tflite {

	}

	// resolve and type-check package AST
	importer := new(resolveTestImporter)
	conf := Config{Importer: importer}
	uses := make(map[*ast.Ident]Object)
	defs := make(map[*ast.Ident]Object)
	_, err := conf.Check("testResolveIdents", fset, files, &Info{Defs: defs, Uses: uses})
	if err != nil {
		t.Fatal(err)
	}

	// check that all packages were imported
	for _, name := range pkgnames {
		if !importer.imported[name] {

  // CHECK-SAME:      -> tensor<40x40xf32>
  // CHECK:         %[[stat:.*]] = "quantfork.stats"(%arg0) <{layerStats = dense
  // CHECK-SAME:      <[-1.000000e+00, 1.000000e+00]> : tensor<2xf32>}>
  // CHECK-SAME:      : (tensor<40x37xf32>) -> tensor<40x37xf32>
  // CHECK-NEXT:    %[[cst:.*]] = "tfl.pseudo_const"() <{value = dense<
  // CHECK-SAME:      1.000000e+00> : tensor<40xf32>}> : () -> tensor<40xf32>