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

    }

    /**
     * The base directory for all reports
     * <p>
     * This value can be changed, so any files derived from this should be calculated on demand.
     *
     * @return The base directory for all reports
     */
    public File getBaseDir() {
        return baseDirectory.getAsFile().get();
    }

    /**

// RUN: tf-opt %s -tf-executor-tpu-v1-island-outlining | FileCheck %s

// CHECK: func @control_input
// CHECK-NOT: func @
// CHECK-LABEL: module @_tpu_v1_compat_outlined
// CHECK: @_tpu_v1_compat_outlined_func0
// CHECK: func @while_body_with_cluster_attr
// CHECK: func @while_cond_with_cluster_attr
// CHECK: func @while_body_without_cluster_attr
// CHECK: func @while_cond_without_cluster_attr
// CHECK: func @callee_func
module {

	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:

// RUN: tf-mlir-translate -mlir-to-graphdef %s -o - | FileCheck %s

func.func @main() {
  tf_executor.graph {
    tf_executor.island wraps "tf.NoOp"() {} : () -> () loc("noop")
    tf_executor.fetch
  }
  func.return
}

// CHECK: node {
// CHECK-NEXT:  name: "noop"
// CHECK-NEXT:  op: "NoOp"
// CHECK-NEXT:  experimental_debug_info {
// CHECK-NEXT:  }

// RUN: not tf-mlir-translate -mlir-to-graphdef %s -o - 2>&1 | FileCheck %s

// CHECK: entry function `main` must be present

func.func @const() {
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Const"() {device = "TPU:0", name = "const", dtype = "tfdtype$DT_INT32", value = dense<[1, 2]> : tensor<2xi32>} : () -> tensor<2xi32>
    tf_executor.fetch
  }
  func.return

    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>

Sie können mehrere Pfad-Parameter und Query-Parameter gleichzeitig deklarieren, **FastAPI** weiß, was welches ist.

Und Sie müssen sie auch nicht in einer spezifischen Reihenfolge deklarieren.

Parameter werden anhand ihres Namens erkannt:

=== "Python 3.10+"

    ```Python hl_lines="6  8"
    {!> ../../../docs_src/query_params/tutorial004_py310.py!}
    ```

=== "Python 3.8+"

    ```Python hl_lines="8  10"

     *
     * <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

// These resources cannot be looked up on-demand.
//
// In some cases, we do not know the IP address of a Service. These services cannot be used for matching
// outbound traffic, as we only have L4 attributes to route based on. However,
// they can be used for Gateways.
// In this case, the key format will be "network/hostname".
// These resources cannot be looked up on-demand.
message Address {
  oneof type {