this.availableResources = available;
        this.requestedResources = requested;

        // Add context
        withContext("resourceType", resourceType);
        withContext("available", available);
        withContext("requested", requested);

        // Add recovery hint
        withRecoveryHint(generateRecoveryHint());
    }

    /**
     * Creates a resource exception with cause

export MC_HOST_myminio="http://minioadmin:minioadmin@localhost:9000/"

(minio server http://localhost:9000/tmp/xl/{1...10}/disk{0...1} 2>&1 >/dev/null) &
pid=$!

./mc ready myminio

./mc admin user add myminio/ minio123 minio123
./mc admin user add myminio/ minio12345 minio12345

./mc admin policy create myminio/ rw ./docs/distributed/rw.json
./mc admin policy create myminio/ lake ./docs/distributed/rw.json

## Configure Prometheus to use AlertManager

Add below section to your `prometheus.yml`
```yaml
alerting:
  alertmanagers:
  - static_configs:
    - targets: ['localhost:9093']
rule_files:
  - rules.yml
```
Here `rules.yml` is the file which should contain the alerting rules defined.

## Add rules for your deployment

    #   [true]
    #     Add schema to table SQL name. (The table name on query is SCHEMA.TABLE)
    #
    #; isAvailableAddingSchemaToTableSqlName = false
    # - - - - - - - - - -/

    # /- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    # o isAvailableAddingCatalogToTableSqlName: (NotRequired - Default false)
    #   [true]

fi
"$python" -m pip install *.whl $TFCI_PYTHON_VERIFY_PIP_INSTALL_ARGS
if [[ "$TFCI_WHL_IMPORT_TEST_ENABLE" == "1" ]]; then
  "$python" -c 'import tensorflow as tf; t1=tf.constant([1,2,3,4]); t2=tf.constant([5,6,7,8]); print(tf.add(t1,t2).shape)'
  "$python" -c 'import sys; import tensorflow as tf; sys.exit(0 if "keras" in tf.keras.__name__ else 1)'
fi
# Import tf nightly wheel built with numpy2 from PyPI in numpy1 env for testing.

* Add tolerations for Stackdriver Logging and Metadata Agents. ([#69737](https://github.com/kubernetes/kubernetes/pull/69737), [@qingling128](https://github.com/qingling128))
* Fix scheduler crashes when Prioritize Map function returns error. ([#69618](https://github.com/kubernetes/kubernetes/pull/69618), [@DylanBLE](https://github.com/DylanBLE))

Of course, you can also declare additional query parameters whenever you need, additional to any body parameters.

As, by default, singular values are interpreted as query parameters, you don't have to explicitly add a `Query`, you can just do:

```Python
q: Union[str, None] = None
```

Or in Python 3.10 and above:

```Python
q: str | None = None
```

For example:

        assertEquals("<strong>aaa</strong><strong>AAA</strong>", viewHelper.getContentTitle(document));

        querySet.add("BBB");

        document.put("title", "111AaA222bbb");
        assertEquals("111<strong>AaA</strong>222<strong>bbb</strong>", viewHelper.getContentTitle(document));

        querySet.add("on");
        document.put("title", "on 111 strong on aaaa");

    If the tests are to be run on the GPU:

    *   For TensorFlow versions starting from v.2.18.0: Add the `cuda` option
        flag.

        ```bash
        export flags="--config=linux --config=cuda -k"
        ```

    *   For TensorFlow versions prior v.2.18.0: Add CUDA paths to
        LD_LIBRARY_PATH and add the `cuda` option flag.

        ```bash

    }

    /**
     * Adds a filter to the query processing chain.
     * The filter chain is recreated after adding the filter.
     *
     * @param filter the filter to add
     */
    public void addFilter(final Filter filter) {
        filterList.add(filter);
        createFilterChain();
    }

    /**
     * Creates the filter chain by combining all registered filters.