lines(
			"#ifdef A",
			"#ifdef B",
			"#define C 1234",
			"#else",
			"#define C 5678",
			"#endif",
			"#endif",
			"C",
		),
		"C.\n",
	},
	{
		"nested #define",
		lines(
			"#define A #define B THIS",
			"A",
			"B",
		),
		"THIS.\n",
	},
	{
		"nested #define with args",
		lines(
			"#define A #define B(x) x",
			"A",
			"B(THIS)",
		),

Properly format optional additional arguments to MinIO binary
*/}}
{{- define "minio.extraArgs" -}}
{{- range .Values.extraArgs -}}
{{ " " }}{{ . }}
{{- end -}}
{{- end -}}

{{/*
Return the proper Docker Image Registry Secret Names
*/}}
{{- define "minio.imagePullSecrets" -}}
{{/*
Helm 2.11 supports the assignment of a value to a variable defined in a different scope,

#ifndef TENSORFLOW_C_C_API_MACROS_H_
#define TENSORFLOW_C_C_API_MACROS_H_

#ifdef SWIG
#define TF_CAPI_EXPORT
#else
#if defined(_WIN32)
#ifdef TF_COMPILE_LIBRARY
#define TF_CAPI_EXPORT __declspec(dllexport)
#else
#define TF_CAPI_EXPORT __declspec(dllimport)
#endif  // TF_COMPILE_LIBRARY
#else
#ifdef TF_CAPI_WEAK
#define TF_CAPI_EXPORT \
  __attribute__((visibility("default"))) __attribute((weak))

This is normally called a **webhook**.

## Webhooks steps { #webhooks-steps }

The process normally is that **you define** in your code what is the message that you will send, the **body of the request**.

You also define in some way at which **moments** your app will send those requests or events.

And **your users** define in some way (for example in a web dashboard somewhere) the **URL** where your app should send those requests.

common:mkl --define=build_with_mkl=true --define=enable_mkl=true
common:mkl --define=tensorflow_mkldnn_contraction_kernel=0
common:mkl --define=build_with_openmp=true
common:mkl -c opt

# config to build OneDNN backend with a user specified threadpool.
common:mkl_threadpool --define=build_with_mkl=true --define=enable_mkl=true
common:mkl_threadpool --define=tensorflow_mkldnn_contraction_kernel=0

};

TEST_F(CApiFunctionTest, OneOp_ZeroInputs_OneOutput) {
  /*
   *                constant
   *                   |
   *                   v
   */
  // Define
  TF_Operation* c = ScalarConst(10, func_graph_, s_, "scalar10");
  Define(-1, {}, {}, {c}, {});

  // Use, run, and verify
  TF_Operation* func_op = Use({});
  Run({}, func_op, 10);
  VerifyFDef({"scalar10_0"}, {}, {{"scalar10", DT_INT32}},

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#ifndef TENSORFLOW_C_C_OP_REQUIRES_H_
#define TENSORFLOW_C_C_OP_REQUIRES_H_

#include "tensorflow/core/platform/macros.h"

namespace tensorflow {

// Convenience macros for asserting and handling exceptional conditions, for

## Import `File` and `Form` { #import-file-and-form }

{* ../../docs_src/request_forms_and_files/tutorial001_an_py310.py hl[3] *}

## Define `File` and `Form` parameters { #define-file-and-form-parameters }

Create file and form parameters the same way you would for `Body` or `Query`:

{* ../../docs_src/request_forms_and_files/tutorial001_an_py310.py hl[10:12] *}

    inline semantics, unless -fgnu89-inline is used.  */
-#if (!defined __cplusplus || __GNUC_PREREQ (4,3)) && defined __GNUC__
+#if (!defined __cplusplus || __GNUC_PREREQ (4,3) || defined __clang__) && defined __GNUC__
 # if defined __GNUC_STDC_INLINE__ || defined __cplusplus
 #  define __extern_inline extern __inline __attribute__ ((__gnu_inline__))

	// We only get here if defining a macro inside a macro.
	// This imperfect implementation means we cannot tell the difference between
	//	#define A #define B(x) x
	// and
	//	#define A #define B (x) x
	// The first definition of B has an argument, the second doesn't. Because we let
	// text/scanner strip the blanks for us, this is extremely rare, hard to fix, and not worth it.
	return s.pos
}