// Create a variable handle (uninitialized to start) placed on the parallel
  // device.
  std::function<void(Variable*)> variable_deleter = [&](Variable* to_delete) {
    to_delete->Destroy(context, status.get());
    ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
    delete to_delete;
  };
  std::unique_ptr<Variable, decltype(variable_deleter)> variable(

namespace tensorflow {
namespace parallel_device {

// A helper for performing common operations on variables. A much more
// restricted stand-in for tf.Variable in Python.
class Variable {
 public:
  // Construct a Variable from a resource-dtype TFE_TensorHandle and an
  // indication of the dtype of the variable's value.
  //
  // Note that creating this resource-dtype handle can fail, so `Create` is a

   *
   * @param formal The type whose type variables or itself is mapped to other type(s). It's almost
   *     always a bug if {@code formal} isn't a type variable and contains no type variable. Make
   *     sure you are passing the two parameters in the right order.
   * @param actual The type that the formal type variable(s) are mapped to. It can be or contain yet

	RET
TEXT ·a8(SB), 0, $0-0
	CMPL runtime·writeBarrier(SB), $0
	ADDQ AX, R15 // ERROR "when dynamic linking, R15 is clobbered by a global variable access and is used here"
	RET
TEXT ·a9(SB), 0, $0-0
	CMPL runtime·writeBarrier(SB), $0
	ORQ R15, R15 // ERROR "when dynamic linking, R15 is clobbered by a global variable access and is used here"
	RET

		"Missing credential environment variable, \""+EnvRootUser+"\"",
		"Environment variable \""+EnvRootUser+"\" is missing",
		`Root user name (access key) and root password (secret key) are expected to be specified via environment variables MINIO_ROOT_USER and MINIO_ROOT_PASSWORD respectively`,
	)

	ErrMissingEnvCredentialRootPassword = newErrFn(
		"Missing credential environment variable, \""+EnvRootPassword+"\"",

  }

  @Test
  @TestMetadata("getClass_variable.kt")
  public void testGetClass_variable() {
    runTest("analysis/analysis-api/testData/components/typeInfoProvider/doubleColonReceiverType/getClass_variable.kt");
  }

  @Test
  @TestMetadata("methodReference_java.kt")
  public void testMethodReference_java() {

// Create a variable handle with name `variable_name` on a device with name
// `device_name`.
TFE_TensorHandle* CreateVarHandle(TFE_Context* ctx,
                                  const tensorflow::string& device_name,
                                  const tensorflow::string& variable_name);

// Create a variable with value `value` and name `variable_name` on a device
// with name `device_name`.

   * add the suppression to the whole method (which turns off checking for a large section of code),
   * or extract a variable, and put the suppression on that. However, a local variable typically
   * doesn't work: Because nullness analyses typically infer the nullness of local variables,
   * there's no way to assign a {@code @Nullable T} to a field {@code T foo;} and instruct the

 *       String}.
 * </ul>
 *
 * This is the same behavior as type-variable usages have to Kotlin and to the Checker Framework.
 * Contrast the method above to:
 *
 * <ul>
 *   <li>methods whose return type is a type variable but which can never return {@code null},
 *       typically because the type forbids nullable type arguments: For example, {@code

 *       String}.
 * </ul>
 *
 * This is the same behavior as type-variable usages have to Kotlin and to the Checker Framework.
 * Contrast the method above to:
 *
 * <ul>
 *   <li>methods whose return type is a type variable but which can never return {@code null},
 *       typically because the type forbids nullable type arguments: For example, {@code