```JSON
{
    "name": "Foo",
    "description": "An optional description",
    "price": 45.2,
    "tax": 3.5
}
```

...поскольку `description` и `tax` являются необязательными (с `None` в качестве значения по умолчанию), вот такой JSON "объект" также подходит:

```JSON
{
    "name": "Foo",
    "price": 45.2
}
```

## Объявление как параметра функции

_major_api_version = 1

# Add module aliases from Keras to TF.
# Some tf endpoints actually lives under Keras.
_current_module.layers = _KerasLazyLoader(
    globals(),
    submodule="__internal__.legacy.layers",
    name="layers",
    mode="v1")
if _tf_uses_legacy_keras:
  _module_dir = _module_util.get_parent_dir_for_name(
      "tf_keras.api._v1.keras.__internal__.legacy.layers")
else:

    }

    /**
     * Adds click count information to the document.
     *
     * @param doc the document to update
     * @param url the URL to get click count for
     * @param clickCountField the field name to store click count
     */
    protected void addClickCountField(final Map<String, Object> doc, final String url, final String clickCountField) {
        final SearchLogHelper searchLogHelper = ComponentUtil.getSearchLogHelper();

    public void testReadBytesWireFormatWithoutExtendedSecurity() throws UnsupportedEncodingException {
        response.getServerData().scapabilities = 0;
        response.getServerData().encryptionKeyLength = 8;
        // Domain name in OEM encoding (ASCII) with null terminator
        byte[] domainBytes = "DOMAIN\0".getBytes("US-ASCII");
        // Use reflection to set protected byteCount field
        int byteCountValue = 8 + domainBytes.length;

[Prometheus](https://prometheus.io) is a cloud-native monitoring platform. Prometheus offers a multi-dimensional data model with time series data identified by metric name and key/value pairs. The data collection happens via a pull model over HTTP/HTTPS. Users looking to monitor their MinIO instances can point Prometheus configuration to scrape data from following endpoints. 

import org.dbflute.Entity;
import org.dbflute.dbmeta.AbstractDBMeta;
import org.dbflute.dbmeta.info.ColumnInfo;
import org.dbflute.dbmeta.info.UniqueInfo;
import org.dbflute.dbmeta.name.TableSqlName;
import org.dbflute.dbmeta.property.PropertyGateway;
import org.dbflute.dbway.DBDef;
import org.dbflute.util.DfTypeUtil;

/**
 * @author ESFlute (using FreeGen)
 */

1. Click **Management**.
1. Click **Index Patterns**.
1. Click **Create index pattern** button
1. Input "fess\_log\*" to the textbox of **index pattern**.
1. Click **Next step**.
1. Set "requestedAt" to the **Time Filter field name**.
1. Click **Create index pattern**.
1. Click **Saved Objects**.
1. Click **Import** and select "fess\_log.ndjson" to import example settings.
1. Click **Dashboard**.
1. Select "fess\_log" dashboard.

        prepTask = branch.prepNightlyTaskName(),
        triggerName = "QuickFeedback",
        cleanCheckout = false,
    ) {
    init {
        id("Promotion_SnapshotFromQuickFeedbackStepPromote")
        name = "Nightly Snapshot (from QuickFeedback) - Promote"
        description =

import org.dbflute.Entity;
import org.dbflute.dbmeta.AbstractDBMeta;
import org.dbflute.dbmeta.info.ColumnInfo;
import org.dbflute.dbmeta.info.UniqueInfo;
import org.dbflute.dbmeta.name.TableSqlName;
import org.dbflute.dbmeta.property.PropertyGateway;
import org.dbflute.dbway.DBDef;
import org.dbflute.util.DfTypeUtil;

/**
 * The DBM of KeyMatch.
 *
 * @author ESFlute (using FreeGen)
 */

	histBuckets := []float64{0.05, 0.1, 0.25, 0.5, 0.75}
	labels := []string{"GetObject", "PutObject", "CopyObject", "CompleteMultipartUpload"}
	ttfbHist := prometheus.NewHistogramVec(
		prometheus.HistogramOpts{
			Name:    "s3_ttfb_seconds",
			Help:    "Time taken by requests served by current MinIO server instance",
			Buckets: histBuckets,
		},
		[]string{"api"},
	)
	observations := []struct {
		val   float64
		label string