buf.append(json);
            buf.append('}');
        } else if (value instanceof Long || value instanceof Integer) {
            buf.append(((Number) value).longValue());
        } else if (value instanceof Number) {
            buf.append(((Number) value).doubleValue());
        } else {
            buf.append('"').append(StringEscapeUtils.escapeJson(value.toString())).append('"');
        }
        return buf;
    }

    protected String[] toStringArray(final Object value) {
        if (value instanceof String[]) {
            return (String[]) value;
        } else if (value instanceof List) {
            return ((List<?>) value).stream().map(v -> v.toString()).toArray(n -> new String[n]);
        }
        String str = DfTypeUtil.toString(value);
        if (str == null) {
            return null;
        }

// If this function succeeds:
//   * *status is set to an OK value,
//   * a TF_Operation is added to the graph,
//   * a non-null value pointing to the added operation is returned --
//     this value is valid until the underlying graph is deleted.
// Otherwise:
//   * *status is set to a non-OK value,
//   * the graph is not modified,
//   * a null value is returned.
// In either case, it deletes `desc`.

 *
 * {@snippet :
 * // Building a mutable value graph
 * MutableValueGraph<String, Double> graph =
 *     ValueGraphBuilder.undirected().allowsSelfLoops(true).build();
 * graph.putEdgeValue("San Francisco", "San Francisco", 0.0);
 * graph.putEdgeValue("San Jose", "San Jose", 0.0);
 * graph.putEdgeValue("San Francisco", "San Jose", 48.4);
 *
 * // Building an immutable value graph
 * ImmutableValueGraph<String, Double> immutableGraph =

```Python
old_dict = {
    "old key": "old value",
    "second old key": "second old value",
}
new_dict = {**old_dict, "new key": "new value"}
```

这里， new_dict 将包含来自 old_dict 的所有键值对加上新的键值对：
```python
{
    "old key": "old value",
    "second old key": "second old value",
    "new key": "new value",
}
```
您可以使用该技术在路径操作中重用一些预定义的响应，并将它们与其他自定义响应相结合。
**例如：**

						if value := reflect.ValueOf(values[idx]).Elem(); value.Kind() == reflect.Ptr && value.IsNil() {
							isNilPtrValue = true
							break
						}

						relValue.Set(reflect.New(relValue.Type().Elem()))
						joinedNestedSchemaMap[fullRelsName] = nil
					}
				}
				currentReflectValue = relValue
			}

			if !isNilPtrValue { // ignore if value is nil

    }

    @Test
    void testConstructorAndGetValue() {
        // Test that the constructor correctly initializes the value
        long expectedValue = 123456789012345L;
        NdrHyper ndrHyper = new NdrHyper(expectedValue);
        assertEquals(expectedValue, ndrHyper.value, "Constructor should set the correct value.");
    }

    @Test
    void testEncode() throws NdrException {
        // Test the encode method

        if (StringUtil.isNotBlank(name) && StringUtil.isNotBlank(value)) {
            searchFieldLogList.add(new Pair<>(name, value));
        }
    }

    public void addRequestHeaderValue(final String name, final String value) {
        if (StringUtil.isNotBlank(name) && StringUtil.isNotBlank(value)) {
            headerList.add(new Pair<>(name, value));
        }
    }

   * Padded variant of AtomicLong supporting only raw accesses plus CAS. The value field is placed
   * between pads, hoping that the JVM doesn't reorder them.
   *
   * <p>JVM intrinsics note: It would be possible to use a release-only form of CAS here, if it were
   * provided.
   */
  static final class Cell {
    volatile long p0, p1, p2, p3, p4, p5, p6;
    volatile long value;
    volatile long q0, q1, q2, q3, q4, q5, q6;

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