listOf(
        "Parsing: <http://example\t.\norg> against <http://example.org/foo/bar>",
        "Parsing: <http://f:0/c> against <http://example.org/foo/bar>",
        "Parsing: <http://f:00000000000000/c> against <http://example.org/foo/bar>",
        "Parsing: <http://f:\n/c> against <http://example.org/foo/bar>",
        "Parsing: <http://f:999999/c> against <http://example.org/foo/bar>",

        responseData.setUrl("http://example.com/sitemap.xml");
        File file = ResourceUtil.getResourceAsFile("sitemaps/sitemap1.xml");
        responseData.setResponseBody(file, false);
        return responseData;
    }

    private ResponseData getTestData2_OK() {
        final ResponseData responseData = new ResponseData();
        responseData.setUrl("http://example.com/sitemap.xml.gz");

* The translation doesn't seem to change the original content, for example with obvious additional documentation sections.
* The translation doesn't use different Markdown structures, for example adding HTML tags when the original didn't have them.
* The "admonition" sections, like `tip`, `info`, etc. are not changed or translated. For example:

```
/// tip

This is a tip.

///

```

### Example Tools to Run at Startup { #example-tools-to-run-at-startup }

Some examples of the tools that can do this job are:

* Docker
* Kubernetes
* Docker Compose
* Docker in Swarm Mode
* Systemd

      },
      "pluginVersion": "10.4.0",
      "targets": [
        {
          "datasource": {
            "type": "prometheus",
            "uid": "${DS_PROMETHEUS}"
          },
          "exemplar": true,
          "expr": "sum by (server) (minio_cluster_replication_received_bytes{job=\"$scrape_jobs\"})",
          "interval": "1m",
          "intervalFactor": 2,
          "legendFormat": "{{server}}",

      "targets": [
        {
          "datasource": {
            "type": "prometheus",
            "uid": "${DS_PROMETHEUS}"
          },
          "editorMode": "code",
          "exemplar": true,
          "expr": "sum by (bucket,range) (minio_bucket_objects_size_distribution{job=~\"$scrape_jobs\"})",
          "format": "time_series",
          "instant": false,
          "interval": "",

*   [C/C++ license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op.cc#L1)
*   [Python license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/nn.py#L1)
*   [Java license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/java/src/main/java/org/tensorflow/Graph.java#L1)

* PostgreSQL
* MySQL
* SQLite
* Oracle
* Microsoft SQL Server, etc.

En este ejemplo, usaremos **SQLite**, porque utiliza un solo archivo y Python tiene soporte integrado. Así que puedes copiar este ejemplo y ejecutarlo tal cual.

Más adelante, para tu aplicación en producción, es posible que desees usar un servidor de base de datos como **PostgreSQL**.

/// tip | Consejo

      Route(
        factory.newAddress(uriHost = "example.com", uriPort = 1003),
        Proxy.NO_PROXY,
        InetSocketAddress(ipv4Address, 1003),
      ).toString(),
    ).isEqualTo("example.com at 1.2.3.4:1003")
    assertThat(
      Route(
        factory.newAddress(uriHost = "example.com", uriPort = 1003),
        Proxy(Proxy.Type.HTTP, InetSocketAddress.createUnresolved("proxy.example.com", 1003)),

        assertEquals("fileRule", rule.getRuleId());
    }

    public void test_getRule_sitemaps1() {
        final ResponseData responseData = new ResponseData();
        responseData.setUrl("http://www.example.com/sitemap1.xml");
        File file = ResourceUtil.getResourceAsFile("sitemaps/sitemap1.xml");
        responseData.setResponseBody(file, false);
        final Rule rule = ruleManager.getRule(responseData);