case asmSlice:
		cc = append(cc, newComponent(suffix+"_base", asmKind(arch.ptrSize), "slice base", off, arch.ptrSize, suffix))
		cc = append(cc, newComponent(suffix+"_len", asmKind(arch.intSize), "slice len", off+arch.ptrSize, arch.intSize, suffix))

Übergeben Sie der Funktion `*` als ersten Parameter.

}

mlir::LogicalResult IslandOp::verify() {
  IslandOp island = *this;
  if (!island.GetBody().args_empty())
    return island.emitOpError() << "expects body without any arguments";

  Operation &yield = island.GetBody().back();
  if (!isa<YieldOp>(yield))
    return yield.emitOpError()
           << "invalid tf_executor.island terminator, yield expected";

	AFCLASSS

	// 12.3: Double-Precision Load and Store Instructions
	AFLD
	AFSD

	// 12.4: Double-Precision Floating-Point Computational Instructions
	AFADDD
	AFSUBD
	AFMULD
	AFDIVD
	AFMIND
	AFMAXD
	AFSQRTD
	AFMADDD
	AFMSUBD
	AFNMADDD
	AFNMSUBD

	// 12.5: Double-Precision Floating-Point Conversion and Move Instructions
	AFCVTWD
	AFCVTLD
	AFCVTDW
	AFCVTDL
	AFCVTWUD

        %0 = tf_executor.graph {
          %outputs, %control = tf_executor.island wraps "tf.XlaSharding"(%arg0) {_XlaSharding = "\08\03\1A\02\02\01\22\02\00\01", sharding = "\08\03\1A\02\02\01\22\02\00\01"} : (tensor<2x2xf32>) -> tensor<2x2xf32>

	}

	AssertAssociationCount(t, user2, "Team", 0, "after clear")
}

func TestHasManyAssociationForSlice(t *testing.T) {
	users := []User{
		*GetUser("slice-hasmany-1", Config{Pets: 2}),
		*GetUser("slice-hasmany-2", Config{Pets: 0}),
		*GetUser("slice-hasmany-3", Config{Pets: 4}),
	}

	DB.Create(&users)

	// Count
	AssertAssociationCount(t, users, "Pets", 6, "")

	// Find
	var pets []Pet

	ADARN
	AMADDHD
	AMADDHDU
	AMADDLD

	/* Vector */
	ALVEBX
	ALVEHX
	ALVEWX
	ALVX
	ALVXL
	ALVSL
	ALVSR
	ASTVEBX
	ASTVEHX
	ASTVEWX
	ASTVX
	ASTVXL
	AVAND
	AVANDC
	AVNAND
	AVOR
	AVORC
	AVNOR
	AVXOR
	AVEQV
	AVADDUM
	AVADDUBM
	AVADDUHM
	AVADDUWM
	AVADDUDM
	AVADDUQM
	AVADDCU
	AVADDCUQ
	AVADDCUW
	AVADDUS
	AVADDUBS

	case AFMADDQ:
		return &inst{0x43, 0x0, 0x0, 96, 0x3}
	case AFMADDS:
		return &inst{0x43, 0x0, 0x0, 0, 0x0}
	case AFMAXD:
		return &inst{0x53, 0x1, 0x0, 672, 0x15}
	case AFMAXQ:
		return &inst{0x53, 0x1, 0x0, 736, 0x17}
	case AFMAXS:
		return &inst{0x53, 0x1, 0x0, 640, 0x14}
	case AFMIND:
		return &inst{0x53, 0x0, 0x0, 672, 0x15}
	case AFMINQ:
		return &inst{0x53, 0x0, 0x0, 736, 0x17}
	case AFMINS:

    attribute, that attribute will be overwritten with the `tf_device.launch`
    device.

    For example:

    ```mlir
      %island:5 = tf_executor.island {
        %a = "tf.opA"() : () -> tensor<i1>
        %launch:2 = "tf_device.launch"() ( {
          %b = "tf.opB"() : () -> tensor<i32>
          %c = "tf.opC"() : () -> tensor<f32>

     *
     * <p>The closures will be “evaluated” on demand whenever the value is needed (e.g. at generation time)</p>
     *
     * @param toSign A closure that produces a File for the object to sign (non File return values will be used as the path to the file)
     * @param classifier A closure that produces the classifier to assign to the signature artifact on demand