* mount (verb): montar
* statement (as in code statement): statement (do not translate to "declaración" or "sentencia")
* worker process: worker process (do not translate to "proceso trabajador" or "proceso de trabajo")
* worker processes: worker processes (do not translate to "procesos trabajadores" or "procesos de trabajo")
* worker: worker (do not translate to "trabajador")
* load balancer: load balancer (do not translate to "balanceador de carga")

        } catch (final IOException e) {
            throw new JobProcessingException("Crawler Process terminated.", e);
        }
    }

    /**
     * Destroys the process associated with the given session ID.
     *
     * @param sessionId unique identifier for the process session
     * @return exit code of the destroyed process, or -1 if the process was not found
     */

         *
         * @param query the query string to process
         * @param chain the next filter chain to invoke
         * @return the processed Query object
         */
        Query parse(final String query, final FilterChain chain);
    }

    /**
     * Interface for the filter chain that processes queries.
     * Each filter in the chain can invoke the next filter or terminate the chain.

/**
 * Job class for updating label information in the search index.
 * This job processes documents and updates their label fields based on URL pattern matching.
 */
public class UpdateLabelJob {

    private static final Logger logger = LogManager.getLogger(UpdateLabelJob.class);

    /**
     * Query builder for filtering documents to be processed.
     */
    protected QueryBuilder queryBuilder = null;

    /**

## 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

     * @param query the Lucene query to be processed
     * @param boost the boost factor to apply to the query
     * @return the processed OpenSearch QueryBuilder
     */
    public QueryBuilder execute(final QueryContext context, final Query query, final float boost) {
        return filterChain.execute(context, query, boost);
    }

    /**
     * Adds a query command to the processor.
     *

This is still true.

So, to be able to have **multiple processes** at the same time, there has to be a **single process listening on a port** that then transmits the communication to each worker process in some way.

### Memory per Process { #memory-per-process }

     * @param object the model object to process
     * @return the processed object (may be the same instance, a pooled instance, or a modified instance)
     * @throws IllegalArgumentException if the object cannot be processed
     */
    <T> T process(T object);

    /**
     * Process a model object using the first available processor implementation.
     *
     * <p>This method discovers processor implementations via ServiceLoader and

                }
            }
        }
    }

    /**
     * Processes Google on/off comment directives in the node.
     *
     * @param node the node to process
     * @param flag the flag indicating whether content should be included
     * @return the processed node
     */
    protected Node processGoogleOffOn(final Node node, final ValueHolder<Boolean> flag) {

        }
    }

    /**
     * Processes and normalizes a document title.
     * Applies text normalization using configured space characters and returns
     * a clean title suitable for indexing.
     *
     * @param responseData the response data from crawling (not currently used)
     * @param title the raw title text to process
     * @param dataMap additional data map (not currently used)