set unit=%value:~-1%
rem assume the unit is specified
set conv=%value:~0,-1%

if "%unit%" == "k" goto kilo
if "%unit%" == "K" goto kilo
if "%unit%" == "m" goto mega
if "%unit%" == "M" goto mega
if "%unit%" == "g" goto giga
if "%unit%" == "G" goto giga

rem no unit found, must be bytes; consider the whole value
set conv=%value%
rem convert to KB
set /a conv=%conv% / 1024
:kilo
rem convert to MB

  ASSERT_EQ(status.message(),
            "'optimize_func' field in TP_Optimizer must be set.");
}

TEST(TF_GrapplerItem, NodesToPreserve) {
  GrapplerItem item;
  item.fetch = std::vector<string>{"Conv", "BiasAdd"};
  std::unordered_set<string> nodes_preserved = item.NodesToPreserve();
  TF_GrapplerItem* c_item = reinterpret_cast<TF_GrapplerItem*>(&item);

  int list_total_size = 0;

}

func mustGetLabels(t test.Failer, obj object.K8sObject, path string) map[string]string {
	t.Helper()
	got := mustGetPath(t, obj, path)
	conv, ok := got.(map[string]any)
	if !ok {
		t.Fatalf("could not convert %v", got)
	}
	ret := map[string]string{}
	for k, v := range conv {
		sv, ok := v.(string)
		if !ok {
			t.Fatalf("could not convert to string %v", v)
		}
		ret[k] = sv
	}
	return ret
}

			conv.getTypeIDs[n.Go[:len(n.Go)-9]] = true
		}
	}
	for i, n := range names {
		if types[i] == nil {
			continue
		}
		pos := f.NamePos[n]
		f, fok := types[i].(*dwarf.FuncType)
		if n.Kind != "type" && fok {
			n.Kind = "func"
			n.FuncType = conv.FuncType(f, pos)
		} else {
			n.Type = conv.Type(types[i], pos)
			switch n.Kind {
			case "iconst":

    # Due to a known MSVC compiler issue
    # https://github.com/tensorflow/tensorflow/issues/10521
    # Overriding eigen strong inline speeds up the compiling of
    # conv_grad_ops_3d.cc and conv_ops_3d.cc by 20 minutes,
    # but this also hurts the performance. Let users decide what they want.
    write_to_bazelrc('build --define=override_eigen_strong_inline=true')

# CROSS-COMPILE MACOS X86 PYCPP
build:cross_compile_macos_x86_pycpp_test --config=macos_x86_pycpp_test
build:cross_compile_macos_x86_pycpp_test -//tensorflow/core/kernels:quantized_conv_ops_test -//tensorflow/core/kernels:quantized_matmul_op_test -//tensorflow/python/ops:quantized_conv_ops_test -//tensorflow/tools/graph_transforms:transforms_test -//tensorflow/python/tools:aot_compiled_test
# END TF TEST SUITE OPTIONS

# START CROSS-COMPILE CONFIGS

    *   `tf.data.experimental.service.register_dataset` now accepts optional
        `compression` argument.

*   Keras:

    *   `tf.keras.layers.Conv` now includes a public `convolution_op` method.
        This method can be used to simplify the implementation of Conv
        subclasses. There are two primary ways to use this new method. The first
        is to use the method directly in your own `call` method: `python class

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Text","finalText","crossDomain","host","hasContent","ifModified","headers","beforeSend","success","send","nativeStatusText","responses","isSuccess","response","modified","ct","finalDataType","firstDataType","ajaxHandleResponses","conv2","current","conv","dataFilter","throws","ajaxConvert","getJSON","getScript","text script","wrapAll","firstElementChild","wrapInner","htmlIsFunction","unwrap","visible","xhr","XMLHttpRequest","xhrSuccessStatus","0","1223","xhrSupported","cors","errorCallback","open...