// original framework of destruction, and this context will be deleted also.
struct TfDlManagedTensorCtx {
  TensorReference reference;
  std::vector<int64_t> shape;
  std::vector<int64_t> strides;
  DLManagedTensor tensor;

  explicit TfDlManagedTensorCtx(const TensorReference& ref) : reference(ref) {}
};

// Gets tensor from eager tensor handle.

using tensorflow::TF_StatusPtr;

Status IdentityNModel(AbstractContext* ctx,
                      absl::Span<AbstractTensorHandle* const> inputs,
                      absl::Span<AbstractTensorHandle*> outputs) {
  std::vector<AbstractTensorHandle*> temp_outputs(2);
  TF_RETURN_IF_ERROR(
      ops::IdentityN(ctx, inputs, absl::MakeSpan(temp_outputs), "IdentityN"));
  // Although, `ops::IdentityN` returns 2 tensors, the first tensor isn't needed

java.io.File basedir; protected String[] includes; protected String[] excludes; protected java.util.Vector filesIncluded; protected java.util.Vector filesNotIncluded; protected java.util.Vector filesExcluded; protected java.util.Vector dirsIncluded; protected java.util.Vector dirsNotIncluded; protected java.util.Vector dirsExcluded; protected java.util.Vector filesDeselected; protected java.util.Vector dirsDeselected; protected boolean haveSlowResults; protected boolean isCaseSensitive; private boolean...

                        "@type": "type.googleapis.com/envoy.config.endpoint.v3.ClusterLoadAssignment",
                        "cluster_name": "zipkin",
                        "policy": {
                            "overprovisioning_factor": 140
                        }
                    }
                },
                {
                    "endpoint_config": {

        "analyzer": "standard_analyzer",
        "search_analyzer": "standard_search_analyzer",
        "term_vector": "with_positions_offsets"
      },
      "content": {
        "type": "text",
        "analyzer": "standard_analyzer",
        "search_analyzer": "standard_search_analyzer",
        "term_vector": "with_positions_offsets"
      },
      "content_minhash": {
        "type": "keyword",
        "index": false

        "analyzer": "standard_analyzer",
        "search_analyzer": "standard_search_analyzer",
        "term_vector": "with_positions_offsets"
      },
      "content": {
        "type": "text",
        "analyzer": "standard_analyzer",
        "search_analyzer": "standard_search_analyzer",
        "term_vector": "with_positions_offsets"
      },
      "content_minhash": {
        "type": "keyword",
        "index": false

  public void testOr_T_present() {
    assertEquals("a", Optional.of("a").or("default"));
  }

  public void testOr_T_absent() {
    assertEquals("default", Optional.absent().or("default"));
  }

  @SuppressWarnings("OptionalOfRedundantMethod") // Unit tests for Optional
  public void testOr_supplier_present() {

        String makeBehavior = request.getMakeBehavior();
        boolean makeBoth = MavenExecutionRequest.REACTOR_MAKE_BOTH.equals(makeBehavior);

        boolean makeUpstream = makeBoth || MavenExecutionRequest.REACTOR_MAKE_UPSTREAM.equals(makeBehavior);
        boolean makeDownstream = makeBoth || MavenExecutionRequest.REACTOR_MAKE_DOWNSTREAM.equals(makeBehavior);

// gradient registerer to instantiate a GradientFunction.
struct ForwardOperation {
 public:
  string op_name;
  std::vector<AbstractTensorHandle*> inputs;
  std::vector<AbstractTensorHandle*> outputs;
  std::vector<int64_t> skip_input_indices;
  AttrBuilder attrs;
};

using GradientFunctionFactory =
    std::function<GradientFunction*(const ForwardOperation& op)>;

        "analyzer": "standard_analyzer",
        "search_analyzer": "standard_search_analyzer",
        "term_vector": "with_positions_offsets"
      },
      "content": {
        "type": "text",
        "analyzer": "standard_analyzer",
        "search_analyzer": "standard_search_analyzer",
        "term_vector": "with_positions_offsets",
        "copy_to": ["content_minhash_bits"]
      },
      "content_minhash": {