// 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+"\"",

// 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`.

    checkState(
        !(runtimeType instanceof TypeVariable),
        "Cannot construct a TypeToken for a type variable.\n"
            + "You probably meant to call new TypeToken<%s>(getClass()) "
            + "that can resolve the type variable for you.\n"
            + "If you do need to create a TypeToken of a type variable, "
            + "please use TypeToken.of() instead.",
        runtimeType);
  }

  /**

# variables in it, OR may be left empty if the user has already exported the
# relevant variables in their environment. Because of 'set -o allexport' above
# (which is equivalent to "set -a"), every variable in the file is exported
# for other files to use.
#
# Separately, if TFCI is set *and* there are also additional TFCI_ variables

 *       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