}

    private fun handleAsIncOrDecOperator(context: BindingContext, unaryExpression: KtUnaryExpression): KtCallInfo? {
        if (unaryExpression.operationToken !in KtTokens.INCREMENT_AND_DECREMENT) return null
        val operatorCall = unaryExpression.getResolvedCall(context) ?: return null
        val resolvedCalls = mutableListOf(operatorCall)

						sseCfgSet.Add(string(configData))
					}
				}
				ss, ok := info.StatsSummary[s.DeploymentID]
				if !ok {
					ss = madmin.SRSiteSummary{}
				}
				// increment total number of replicated buckets
				if len(slc) == numSites {
					ss.ReplicatedBuckets++
				}
				ss.TotalBucketsCount++
				if tagCount > 0 {
					ss.TotalTagsCount++
				}

Sus características principales son:

* **Rapidez**: Alto rendimiento, a la par con **NodeJS** y **Go** (gracias a Starlette y Pydantic). [Uno de los frameworks de Python más rápidos](#rendimiento).

* **Rápido de programar**: Incrementa la velocidad de desarrollo entre 200% y 300%. *
* **Menos errores**: Reduce los errores humanos (de programador) aproximadamente un 40%. *

            // for the initial task...
            // In order to work around that problem, we override the
            // invokeAll method, so that whenever the method is called,
            // the pool core size will be incremented before submitting
            // all the tasks, then the thread will block waiting for all
            // those subtasks to finish.
            // This ensures the number of running workers is no more than

	if dobj.VersionID != "" {
		prevStatus = replication.StatusType(dobj.VersionPurgeStatus())
		replicationStatus = replication.StatusType(rinfos.VersionPurgeStatus())
	}

	// to decrement pending count later.
	for _, rinfo := range rinfos.Targets {
		if rinfo.ReplicationStatus != rinfo.PrevReplicationStatus {
			globalReplicationStats.Update(dobj.Bucket, rinfo, replicationStatus,
				prevStatus)
		}

## Bug Fixes and Other Changes

*   Java: Support for loading models exported using the SavedModel API (courtesy
    @EronWright).
*   Go: Added support for incremental graph execution.
*   Fix a bug in the WALS solver when single-threaded.
*   Added support for integer sparse feature values in
    `tf.contrib.layers.sparse_column_with_keys`.