# shadowing typing.Required.
    RequiredParam: Any = Ellipsis  # type: ignore[no-redef]
    from pydantic.fields import (  # type: ignore[no-redef,attr-defined]
        Undefined as Undefined,
    )
    from pydantic.fields import (  # type: ignore[no-redef, attr-defined]
        UndefinedType as UndefinedType,  # noqa: F401
    )
    from pydantic.schema import (
        field_schema,
        get_flat_models_from_fields,

  // RFC-6335 and https://www.iana.org/assignments/service-names).
  //
  // * Kubernetes-defined prefixed names:
  //   * 'kubernetes.io/h2c' - HTTP/2 over cleartext as described in https://www.rfc-editor.org/rfc/rfc7540
  //
  // * Other protocols should use implementation-defined prefixed names such as
  // mycompany.com/my-custom-protocol.
  // +optional
  optional string appProtocol = 4;
}

  ASSERT_EQ(TF_OK, code) << status_msg;
  TF_DeletePluggableDeviceLibraryHandle(lib);
#endif  // !defined(TENSORFLOW_NO_SHARED_OBJECTS)
#endif  // !defined(PLATFORM_WINDOWS)
}

TEST(CAPI_EXPERIMENTAL, LibraryNextPluggableDeviceLoadFunctions) {
  // TODO(penpornk): Enable this test on Windows.
#if !defined(PLATFORM_WINDOWS)
#if !defined(TENSORFLOW_NO_SHARED_OBJECTS)
  // Load the library.
  TF_Status* status = TF_NewStatus();

 * This interface allows users to define and configure various toolchains
 * that can be utilized by Maven during the build process. Toolchains can
 * include compilers, interpreters, and other tools that are necessary
 * for building a project in a specific environment.</p>
 *
 * <p>Toolchains are defined in the Maven toolchains.xml file and can be

  public static final MediaType WEBM_AUDIO = createConstant(AUDIO_TYPE, "webm");

  /**
   * L16 audio, as defined by <a href="https://tools.ietf.org/html/rfc2586">RFC 2586</a>.
   *
   * @since 24.1
   */
  public static final MediaType L16_AUDIO = createConstant(AUDIO_TYPE, "l16");

  /**
   * L24 audio, as defined by <a href="https://tools.ietf.org/html/rfc3190">RFC 3190</a>.
   *
   * @since 20.0
   */

    int64_t init_timeout_in_ms, int retries, TF_Status* status,
    bool clear_existing_contexts) {
#if defined(IS_MOBILE_PLATFORM)
  status->status = tensorflow::errors::Unimplemented(
      "TFE_ContextSetServerDef not supported on mobile");
#else   // !defined(IS_MOBILE_PLATFORM)
  tensorflow::ServerDef server_def;
  if (!server_def.ParseFromArray(proto, proto_len)) {

typedef struct TF_AbstractFunction TF_AbstractFunction;

// This allows the client to swap the implementation of the tracing engine.
// Any future call to TF_CreateFunction will use the implementation defined
// here.
void TF_SetTracingImplementation(const char* name, TF_Status*);

// Creates a new TensorFlow function. A Function is an execution context, and as

## Overview

MinIO supports two storage classes, Reduced Redundancy class and Standard class. These classes can be defined using environment variables
set before starting MinIO server. After the data and parity drives for each storage class are defined using environment variables,
you can set the storage class of an object via request metadata field `x-amz-storage-class`. MinIO server then honors the storage class by

- [SplitAfterSeq] returns an iterator
  over substrings of s split after each instance of sep.
- [FieldsSeq] returns an iterator over
  substrings of s split around runs of whitespace characters,
  as defined by unicode.IsSpace.
- [FieldsFuncSeq] returns an iterator

  }

  /**
   * Returns the number of times {@link Cache} lookup methods have returned either a cached or
   * uncached value. This is defined as {@code hitCount + missCount}.
   *
   * <p><b>Note:</b> the values of the metrics are undefined in case of overflow (though it is
   * guaranteed not to throw an exception). If you require specific handling, we recommend
   * implementing your own stats collector.
   */