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[![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/tensorflow/tensorflow/badge)](https://securityscorecards.dev/viewer/?uri=github.com/tensorflow/tensorflow)

import org.opensearch.search.SearchHit;
import org.opensearch.search.sort.SortBuilder;
import org.opensearch.transport.client.Client;

/**
 * <p>
 * {@link ESSourceReader} reads documents from Elasticsearch using the scroll API.
 * It implements the {@link DocumentReader} interface to provide a way to iterate over documents
 * in a large index without loading all of them into memory at once.
 * </p>
 *
 * <p>

    local data="$2"

    local response=$(curl -X POST \
        -H "Authorization: token $GITHUB_TOKEN" \
        -H "Accept: application/vnd.github.v3+json" \
        -H "Content-Type: application/json" \
        "https://api.github.com/repos/gradle/gradle$endpoint" \
        -d "$data" \
        -w "\n%{http_code}" \
        2>/dev/null)

    local http_code=$(echo "$response" | tail -n1)
    local body=$(echo "$response" | head -n -1)

  if (enable) {
    optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::ON_1);

    // These XLA flags are needed to trigger XLA properly from C (more generally
    // non-Python) clients. If this API is called again with `enable` set to
    // false, it is safe to keep these flag values as is.
    tensorflow::MarkForCompilationPassFlags* flags =
        tensorflow::GetMarkForCompilationPassFlags();

          (android_ndk_home_path, ndk_version, _SUPPORTED_ANDROID_NDK_VERSIONS))
  write_action_env_to_bazelrc('ANDROID_NDK_VERSION', ndk_version)

  # Now grab the NDK API level to use. Note that this is different from the
  # SDK API level, as the NDK API level is effectively the *min* target SDK
  # version.
  meta = open(os.path.join(android_ndk_home_path, 'meta/platforms.json'))
  platforms = json.load(meta)
  meta.close()

          <option value="io.reactivex.annotations.Beta" />
          <option value="io.reactivex.annotations.Experimental" />
          <option value="org.apache.http.annotation.Beta" />
          <option value="org.gradle.api.Incubating" />
          <option value="org.jetbrains.annotations.ApiStatus.Experimental" />
          <option value="rx.annotations.Beta" />
          <option value="rx.annotations.Experimental" />
        </set>

			t.Errorf("Opening %q with fs.FS API succeeded", f.Name)
		}
	}
	if !slices.Equal(names, entryNames) {
		t.Errorf("Unexpected file entries: %q", names)
	}
	if _, err := r.Open(""); err == nil {
		t.Errorf("Opening %q with fs.FS API succeeded", "")
	}
	if _, err := r.Open("test.txt"); err != nil {
		t.Errorf("Error opening %q with fs.FS API: %v", "test.txt", err)
	}

     */
    public static RequestDataContext newRequestData() {
        return new RequestDataContext();
    }

    /**
     * Context class for building RequestData instances using a fluent API.
     */
    public static class RequestDataContext {
        private final RequestData data;

        private RequestDataContext() {
            data = new RequestData();
        }

        /**

```

#### Option B: Paste your code here or provide a link to a custom end-to-end colab

```
(You can paste links or attach files by dragging & dropping them below)
- Include code to invoke the TFLite Converter Python API and the errors.
- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.
```

### 3. Failure after conversion
If the conversion is successful, but the generated model is wrong, then state what is wrong:

    - name: Update the RBE Configs
      run: |
        function map() {
          # The "digest" that allows us to pull an image is not the digest as
          # returned by the API, but a sha256sum of the entire chunk of image
          # metadata. gcr.io helpfully includes it in the header of the response
          # as docker-content-digest: sha256:[digest]. Note we use egrep to