"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",
        "copy_to": ["content_minhash_bits"]
      },
      "content_minhash": {

    // Reactor Failure Mode
    // ----------------------------------------------------------------------

    String REACTOR_FAIL_FAST = "FAIL_FAST";

    String REACTOR_FAIL_AT_END = "FAIL_AT_END";

    String REACTOR_FAIL_NEVER = "FAIL_NEVER";

    // ----------------------------------------------------------------------
    // Reactor Make Mode

        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)>;

#include "tensorflow/c/experimental/grappler/grappler.h"

#include <algorithm>
#include <cstddef>
#include <cstring>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>

#include "absl/status/status.h"
#include "tensorflow/c/c_api_macros.h"
#include "tensorflow/c/experimental/grappler/grappler_internal.h"
#include "tensorflow/c/tf_buffer.h"
#include "tensorflow/c/tf_buffer_internal.h"

  }

  @J2ktIncompatible
  @GwtIncompatible // SerializableTester
  public void testOr_serializationNoArgs() {
    checkSerialization(Predicates.or());
  }

  public void testOr_applyOneArg() {
    assertEvalsToTrue(Predicates.or(TRUE));
    assertEvalsToFalse(Predicates.or(FALSE));
  }

  public void testOr_equalityOneArg() {
    new EqualsTester()

// 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.

                Map.of("_default", List.of("QUERY1")), //
                Set.of("QUERY1"), //

  private static final double DESIRED_LOAD_FACTOR = 0.5;

  /**
   * Returns an array size suitable for the backing array of a hash table that uses open addressing
   * with linear probing in its implementation. The returned size is the smallest power of two that
   * can hold setSize elements with the desired load factor.
   */
  @VisibleForTesting
  static int chooseTableSize(int setSize) {