GradleBuildStep(init)
        .apply(custom)
        .also {
            step(it)
        }

/**
 * Adds a [Gradle build step](https://confluence.jetbrains.com/display/TCDL/Gradle)
 * that runs with the Gradle wrapper.
 *
 * @see GradleBuildStep
 */
fun BuildSteps.gradleWrapper(buildType: BuildType? = null, init: GradleBuildStep.() -> Unit): GradleBuildStep =

## 他の検索プロバイダーからの移行

[MIGRATION.md](MIGRATION.md)を参照してください。

## データストア

現在、Fessは以下の[ストレージロケーションとAPI](https://fess.codelibs.org/14.17/admin/dataconfig-guide.html)のクロールをサポートしています：

 - [Confluence/Jira](https://github.com/codelibs/fess-ds-atlassian)
 - [Box](https://github.com/codelibs/fess-ds-box)
 - [CSV](https://github.com/codelibs/fess-ds-csv)
 - [Database](https://github.com/codelibs/fess-ds-db)

  }

  TF_Output output_;

  // For LLVM style RTTI.
  static bool classof(const AbstractTensorHandle* ptr) {
    return ptr->getKind() == kGraph;
  }

 private:
  TF_Graph* graph_;  // For shape inference.
};

// GraphOperation wraps and populates a TF_OperationDescription.
class GraphOperation : public TracingOperation {
 public:
  explicit GraphOperation(TF_Graph* g) : TracingOperation(kGraph), g_(g) {}

        }
      }
      entries[entryIndex] = effectiveEntry;
    }
    if (duplicates != null) {
      // Explicit type parameters needed here to avoid a problem with nullness inference.
      entries = RegularImmutableMap.<K, V>removeDuplicates(entries, n, n - dupCount, duplicates);
      int newTableSize = Hashing.closedTableSize(entries.length, MAX_LOAD_FACTOR);
      if (newTableSize != tableSize) {

    }
    status->status = desc->node_builder.Finalize(&desc->graph->graph, &ret,
                                                 /*consume=*/true);

    if (status->status.ok()) {
      // Run shape inference function for newly added node.
      status->status = desc->graph->refiner.AddNode(ret);
    }
    if (status->status.ok()) {
      // Add the node to the name-to-node mapping.

  }

  @SuppressWarnings("JUnitIncompatibleType")
  public void testWhere() {
    assertEquals(new TypeToken<Map<String, Integer>>() {}, mapOf(String.class, Integer.class));
    // Type inference is doomed here: int.class is the same as Integer.class, so this is comparing
    // TypeToken<int[]> and TypeToken<Integer[]>.
    assertEquals(new TypeToken<int[]>() {}, arrayOf(int.class));

TF_CAPI_EXPORT extern void TF_RemoveAllControlInputs(TF_Graph* graph,
                                                     TF_Operation* op);

// Set if `graph` requires shape inference functions.
TF_CAPI_EXPORT extern void TF_SetRequireShapeInferenceFns(TF_Graph* graph,
                                                          bool require);

  }

  @SuppressWarnings("JUnitIncompatibleType")
  public void testWhere() {
    assertEquals(new TypeToken<Map<String, Integer>>() {}, mapOf(String.class, Integer.class));
    // Type inference is doomed here: int.class is the same as Integer.class, so this is comparing
    // TypeToken<int[]> and TypeToken<Integer[]>.
    assertEquals(new TypeToken<int[]>() {}, arrayOf(int.class));

  CHECK(tensorflow::unwrap(concatOp)->OpDef());
  TFE_OpAddInput(concatOp, inputs[0], status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  EXPECT_FALSE(tensorflow::unwrap(concatOp)->OpDef())
      << "Inference context is still present";
  TFE_OpAddInput(concatOp, inputs[1], status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  tensorflow::AttrValueMap attr_values = ExtractAttrs(concatOp);

	// for each entry in names and then dereference the type we
	// learn for __cgo__i.
	var b bytes.Buffer
	b.WriteString(builtinProlog)
	b.WriteString(f.Preamble)
	b.WriteString("#line 1 \"cgo-dwarf-inference\"\n")
	for i, n := range names {
		fmt.Fprintf(&b, "__typeof__(%s) *__cgo__%d;\n", n.C, i)
		if n.Kind == "iconst" {
			fmt.Fprintf(&b, "enum { __cgo_enum__%d = %s };\n", i, n.C)
		}
	}