}

        public long getLastHealthCheck() {
            return lastHealthCheck;
        }

        @Override
        public String toString() {
            return String.format("ChannelInfo{id=%s, local=%s, remote=%s, active=%d, healthy=%s}", channelId, localAddress, remoteAddress,
                    activeConnections.get(), isHealthy);
        }
    }

    /**

        }
        return buffer[bufferIndex] & 0xFF;
    }

    int type, length;

    /**
     * Writes the packet to the specified byte array in wire format.
     *
     * @param dst the destination byte array to write to
     * @param dstIndex the starting index in the destination array
     * @return the number of bytes written
     */

        bufferIndex += 8;
        this.fileAttributes = SMBUtil.readInt4(buffer, bufferIndex);
        bufferIndex += 4;

        if (log.isDebugEnabled()) {
            log.debug(String.format("Closed %s (%s)", Hexdump.toHexString(this.fileId), this.fileName));
        }

        return bufferIndex - start;
    }

| *Valid Range* | *Minimum value of 900. Maximum value of 31536000.* |
| *Required*    | *No*                                               |

### Policy

        accessResultData = accessResultDataAsOne;
        initializedData = true;
    }

    /**
     * Converts this access result to XContent format for OpenSearch indexing.
     *
     * @param builder The XContentBuilder to write to.
     * @param params Additional parameters for the conversion.
     * @return The XContentBuilder with the access result data.

running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized
in taking those formats, modifying them, and/or converting them to intermediate
formats that can be processed by TensorFlow.

These modifications and conversions are handled by a variety of libraries that

{* ../../docs_src/path_operation_advanced_configuration/tutorial006.py hl[19:36, 39:40] *}

En este ejemplo, no declaramos ningún modelo Pydantic. De hecho, el cuerpo del request ni siquiera se <abbr title="converted from some plain format, like bytes, into Python objects">parse</abbr> como JSON, se lee directamente como `bytes`, y la función `magic_data_reader()` sería la encargada de parsearlo de alguna manera.

    <example>${fe:replace(input, regex, replacement)}</example>
  </function>

  <function>
    <description>Format a content as code.</description>
    <name>formatCode</name>
    <function-class>org.codelibs.fess.taglib.FessFunctions</function-class>

# Parameter Path

"parameter" atau "variabel" path didefinisikan dengan sintaksis Python format string:

{* ../../docs_src/path_params/tutorial001.py hl[6:7] *}

Nilai parameter path `item_id` akan dikirim ke fungsi sebagai argument `item_id`:

Jika anda menjalankan contoh berikut dan kunjungi <a href="http://127.0.0.1:8000/items/foo" class="external-link" target="_blank">http://127.0.0.1:8000/items/foo</a>, anda akan melihat respon:

```JSON

		date    time.Time
		region  string
		service string
		request string
	}
}

// Return scope string.
func (c credentialHeader) getScope() string {
	return strings.Join([]string{
		c.scope.date.Format(yyyymmdd),
		c.scope.region,
		c.scope.service,
		c.scope.request,
	}, SlashSeparator)
}

func getReqAccessKeyV4(r *http.Request, region string, stype serviceType) (auth.Credentials, bool, APIErrorCode) {