builder.setQuery(parameter.getQuery());
            stream(parameter.getSuggestFields()).of(stream -> stream.forEach(builder::addField));
            roleQueryHelper.build(SearchRequestType.SUGGEST).stream().forEach(builder::addRole);
            builder.setSize(parameter.getNum());
            stream(langs).of(stream -> stream.forEach(builder::addLang));

// variables.
//
// The returned TF_WhileParams will have all fields initialized except
// `cond_output`, `body_outputs`, and `name`. The `body_outputs` buffer will be
// allocated to size `ninputs`. The caller should build `cond_graph` and
// `body_graph` starting from the inputs, and store the final outputs in
// `cond_output` and `body_outputs`.
//
// If `status` is OK, the caller must call either TF_FinishWhile or

        final ObjectMapper mapper = new ObjectMapper();
        final AtomicBoolean resetJobs = new AtomicBoolean(false);
        try (CurlResponse response = ComponentUtil.getCurlHelper().post("/_bulk").onConnect((req, con) -> {
            con.setDoOutput(true);
            try (final BufferedReader br = new BufferedReader(new InputStreamReader(new FileInputStream(tempFile)));

* Fix failing load balancers in Azure ([#40405](https://github.com/kubernetes/kubernetes/pull/40405), [@codablock](https://github.com/codablock))
* Add a KUBERNETES_NODE_* section to build kubelet/kube-proxy for windows ([#38919](https://github.com/kubernetes/kubernetes/pull/38919), [@brendandburns](https://github.com/brendandburns))

      const std::vector<int64_t>& unneeded_gradients,
      gtl::ArraySlice<AbstractTensorHandle*> output_gradients,
      absl::Span<AbstractTensorHandle*> result) const override;

  // Builds a tensor filled with ones with the same shape and dtype as `t`.
  absl::Status BuildOnesLike(const TapeTensor& t,
                             AbstractTensorHandle** result) const override;

/// info

This requires installing `ujson` for example with `pip install ujson`.

///

/// warning

`ujson` is less careful than Python's built-in implementation in how it handles some edge-cases.

///

```Python hl_lines="2  7"
{!../../docs_src/custom_response/tutorial001.py!}
```

/// tip

### **4.2 WordCount**

WordCount is a simple program that counts how often a word occurs in a text file. The code builds a dataset of (String, Int) pairs called counts, and saves the dataset to a file.

The following example submits WordCount code to the Scala shell. Select an input file for the Spark WordCount example. We can use any text file as input.

                                // MavenMetadataSource.createArtifacts(..) step, BUT that would
                                // require resolving the POM from the repository very early on in
                                // the build.
                                continue;
                            }

                            // TODO might be better to have source.retrieve() throw a specific exception for this

        }
        ComponentUtil.getSsoManager().register(this);
        groupCache = CacheBuilder.newBuilder().expireAfterWrite(groupCacheExpiry, TimeUnit.SECONDS).build();
    }

    @Override
    public LoginCredential getLoginCredential() {
        return LaRequestUtil.getOptionalRequest().map(request -> {
            if (logger.isDebugEnabled()) {

  }

  @GwtIncompatible // NavigableSet
  public void testSubSet_unnaturalOrdering() {
    ImmutableSortedSet<Integer> set =
        ImmutableSortedSet.<Integer>reverseOrder().add(2, 4, 6, 8, 10).build();

    assertThrows(IllegalArgumentException.class, () -> Sets.subSet(set, Range.closed(4, 8)));

    // These results are all incorrect, but there's no way (short of iterating over the result)