[TAC workflow](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/compiler/mlir/lite/experimental/tac/README.md#tac-workflow)
section

## How to use

Once you have a converted TfLite model ready, you can use the following command
to use TAC to optimize for your model:

```
bazel run -c opt //tensorflow/compiler/mlir/lite/experimental/tac:tac-translate -- <PATH_TO_YOUR_MODEL> -o=<OUTPUT_PATH> -device-specs=<HARDWARE_BACKENDS>

 * </ul>
 *
 * <p><b>Note:</b> In general, {@code ByteSource} is intended to be used for "file-like" sources
 * that provide streams that are:
 *
 * <ul>
 *   <li><b>Finite:</b> Many operations, such as {@link #size()} and {@link #read()}, will either
 *       block indefinitely or fail if the source creates an infinite stream.

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s

	case *aliases.Alias:
		return h.shallowHash(aliases.Unalias(t))

	case *types.Signature:
		var hash uint32 = 604171
		if t.Variadic() {
			hash *= 971767
		}
		// The Signature/Tuple recursion is always finite
		// and invariably shallow.
		return hash + 1062599*h.shallowHash(t.Params()) + 1282529*h.shallowHash(t.Results())

	case *types.Tuple:
		n := t.Len()
		hash := 9137 + 2*uint32(n)
		for i := 0; i < n; i++ {

	Z := mkPS()
	go func() {
		put(c, Z)
		copy(U, Z)
	}()
	return Z
}

// simple pole at 1: 1/(1-x) = 1 1 1 1 1 ...

// Convert array of coefficients, constant term first
// to a (finite) power series

/*
func Poly(a []rat) PS {
	Z:=mkPS()
	begin func(a []rat, Z PS) {
		j:=0
		done:=0
		for j=len(a); !done&&j>0; j=j-1)
			if(a[j-1].num!=0) done=1
		i:=0

        "//tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo_conversions:custom_call",
        "//tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo_conversions:dot_general",
        "//tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo_conversions:reduce",
        "//tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo_conversions:util",

#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/tfe_context_internal.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/compiler/mlir/lite/flatbuffer_import.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/quantization/stablehlo/passes/bridge/passes.h"
#include "tensorflow/compiler/mlir/tensorflow/dialect_registration.h"

Gunicorn würde sich bei Bedarf auch um die Verwaltung **beendeter Prozesse** und den **Neustart** von Prozessen kümmern, um die Anzahl der Worker aufrechtzuerhalten. Das hilft also teilweise beim **Neustarts**-Konzept aus der obigen Liste.

			if n > 0 {
				// SetWriteDeadline specifies a time “after which I/O operations fail
				// instead of blocking”. However, the kernel's send buffer is not yet
				// full, we may be able to write some arbitrary (but finite) number of
				// bytes to it without blocking.
				t.Logf("Wrote %d bytes into send buffer; retrying until buffer is full", n)
				if d <= maxDynamicTimeout/2 {

		t.Errorf("got %s; want %s", toJSONString(got), toJSONString(want))
	}
}

func TestManyToManyPreloadWithMultiPrimaryKeys(t *testing.T) {
	if name := DB.Dialector.Name(); name == "sqlite" || name == "sqlserver" {
		t.Skip("skip sqlite, sqlserver due to it doesn't support multiple primary keys with auto increment")
	}

	type (
		Level1 struct {
			ID           uint   `gorm:"primary_key;"`