// 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 {

// 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

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"

/**
 * A utility similar to {@link IteratorTester} for testing a {@link ListIterator} against a known
 * good reference implementation. As with {@code IteratorTester}, a concrete subclass must provide
 * target iterators on demand. It also requires three additional constructor parameters: {@code
 * elementsToInsert}, the elements to be passed to {@code set()} and {@code add()} calls; {@code

func.func @dont_inline_tpu_partitioned_call(%arg0: tensor<2xi32> {tf._user_specified_name = "inputs_0"}, %arg1: tensor<2xi32> {tf._user_specified_name = "inputs_1"}) -> tensor<2xi32> {
  %graph = tf_executor.graph {
    %result:2 = tf_executor.island wraps "tf.TPUPartitionedCall"(%arg0, %arg1) {config = "", config_proto = "", executor_type = "", f = @simple_callee} : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
    tf_executor.fetch %result#0 : tensor<2xi32>
  }

        1 * cacheBuilder.withInitialLockMode(FileLockManager.LockMode.OnDemand) >> cacheBuilder
        1 * cacheBuilder.open() >> { cache }
        1 * cache.close()
    }

    def "creates JAR file on demand for identifier '#identifier'"(String identifier) {
        def cacheDir = tmpDir.testDirectory
        def jarFile = cacheDir.file("gradle-${identifier}-${gradleVersion}.jar")
        def cacheBuilder = Mock(CacheBuilder)

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/error_util.h"

#define DEBUG_TYPE "tf-executor-tpu-v1-island-inlining"

namespace mlir {
namespace tf_executor {

namespace {
constexpr llvm::StringRef kNestedModule = "_tpu_v1_compat_outlined";

#define GEN_PASS_DEF_EXECUTORTPUV1ISLANDINLININGPASS

# OpenAPI-Callbacks

Sie könnten eine API mit einer *Pfadoperation* erstellen, die einen Request an eine *externe API* auslösen könnte, welche von jemand anderem erstellt wurde (wahrscheinlich derselbe Entwickler, der Ihre API *verwenden* würde).

    }

    def "credentials(Class, Action) creates credentials on demand if required"() {
        Instantiator instantiator = Mock()
        AuthenticationContainer authenticationContainer = Stub()

        Action action = Mock()