"interval": "1m",
          "intervalFactor": 2,
          "legendFormat": "{{server}}",
          "refId": "A"
        }
      ],
      "title": "Avg. Transfer Rate (bytes/s)",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "${DS_PROMETHEUS}"
      },
      "fieldConfig": {
        "defaults": {

| `minio_node_replication_average_transfer_rate`          | Average replication transfer rate in bytes/sec                                                           |
| `minio_node_replication_max_transfer_rate`              | Maximum replication transfer rate in bytes/sec seen since server start                                   |

					throughputHighestPut = totalPut
					// let the client see lower value as well
					throughputHighestGet = totalGet
				}
				sendResult()
				break
			}

			// We break if we did not see 2.5% growth rate in total GET
			// requests, we have reached our peak at this point.
			doBreak := float64(totalGet-throughputHighestGet)/float64(totalGet) < 0.025

			throughputHighestGet = totalGet
			throughputHighestResults = results

        if (width < TABLET_WIDTH) {
            float ratio = (float) width / (float) TABLET_WIDTH;
            if (ratio < 0.5) {
                ratio = 0.5f;
            }
            highlightInfo.fragmentSize((int) (highlightInfo.getFragmentSize() * ratio));
        }
    }

    /**
     * Gets the URL link for a document with proper protocol handling.

                    handleChannelFailure(channel, e);
                }
            }

            // Check error rate
            if (channel.getErrorRate() > 0.1) {
                log.warn("High error rate on channel {}: {}", channel.getChannelId(), channel.getErrorRate());
            }
        }
    }

        // In practice, this would be more sophisticated
        int uniqueBytes = countUniqueBytes(data);
        double entropy = (double) uniqueBytes / 256.0;

        // Estimate compression ratio based on entropy
        // Lower entropy = better compression
        return 0.3 + (entropy * 0.6); // Range from 30% to 90%
    }

    @Override
    public int getMinCompressionSize() {

       <META http-equiv="Content-Type" content="text/html; charset=UTF-8">
       <title>Incubating APIs for $title</title>
       <link xmlns:xslthl="http://xslthl.sf.net" rel="stylesheet" href="https://fonts.googleapis.com/css?family=Lato:400,400i,700">
       <meta xmlns:xslthl="http://xslthl.sf.net" content="width=device-width, initial-scale=1" name="viewport">

        <directory>src/main/resources</directory>
      </resource>
    </resources>
    <pluginManagement>
      <plugins>
        <plugin>
          <groupId>org.apache.rat</groupId>
          <artifactId>apache-rat-plugin</artifactId>
          <configuration>
            <excludes combine.children="append">
              <exclude>lifecycle-executor.txt</exclude>
              <exclude>plugin-manager.txt</exclude>

Puedes usar el atributo `model_config` que toma un `dict` como se describe en [Documentación de Pydantic: Configuración](https://docs.pydantic.dev/latest/api/config/).

Puedes establecer `"json_schema_extra"` con un `dict` que contenga cualquier dato adicional que te gustaría que aparezca en el JSON Schema generado, incluyendo `examples`.

/// tip | Consejo

Podrías usar la misma técnica para extender el JSON Schema y añadir tu propia información extra personalizada.

        assertThat(new CopyOptions().findDefaultConverter(Integer.class), is(nullValue()));
    }

    /**
     * @author kato
     */
    public static class BeanNames {

        /**
         * CharSequenceを作成します。
         *
         * @param name
         * @return CharSequence
         */