if (!status->status.ok()) return;

  // Initialize a input_tensor vector with `nullptr` values.
  std::vector<const Tensor*> input_tensors_vector(num_inputs, nullptr);
  // A vector to keep track of newly created `tf::Tensor` objects.
  std::vector<Tensor> all_input_tensors;
  // Update the vector with information from `input_tensors` if provided.
  if (input_tensors != nullptr) {

				&prometheus_model.Sample{Value: 0.04},
			},
			"sum(rate(istio_requests_total{destination_workload=~\"details.*\", destination_workload_namespace=~\".*\",reporter=\"destination\",response_code=~\"[45][0-9]{2}\"}[1m0s]))": prometheus_model.Vector{}, // nolint: lll

        sink.writeUtf8("-------\n");
        for (int i = 2; i <= 997; i++) {
          sink.writeUtf8(String.format(" * %s = %s\n", i, factor(i)));
        }
      }

      private String factor(int n) {
        for (int i = 2; i < n; i++) {
          int x = n / i;
          if (x * i == n) return factor(x) + " × " + i;
        }
        return Integer.toString(n);
      }
    };

    Request request = new Request.Builder()

#include "tensorflow/core/platform/errors.h"

using std::vector;
using tensorflow::ops::BiasAddGrad;
using tensorflow::ops::Mul;
using tensorflow::ops::ReluGrad;

namespace tensorflow {
namespace gradients {
namespace {

class ReluGradientFunction : public GradientFunction {
 public:
  explicit ReluGradientFunction(vector<AbstractTensorHandle*> f_outputs)
      : forward_outputs_(f_outputs) {

 private:
  void DeleteInputValues();
  void ResetOutputValues();

  TF_Session* session_;
  std::vector<TF_Output> inputs_;
  std::vector<TF_Tensor*> input_values_;
  std::vector<TF_Output> outputs_;
  std::vector<TF_Tensor*> output_values_;
  std::vector<TF_Operation*> targets_;
};

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

    }

    /**
     * {@link Vector}の新しいインスタンスを作成して返します。
     *
     * @param <E>
     *            {@link Vector}の要素型
     * @return {@link Vector}の新しいインスタンス
     * @see Vector#Vector()
     */
    public static <E> Vector<E> newVector() {
        return new Vector<>();
    }

    /**
     * {@link Vector}の新しいインスタンスを作成して返します。
     *
     * @param <E>

  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) {

            sink.writeUtf8(String.format(" * %s = %s\n", i, factor(i)));
          }
          sink.close();
        } catch (IOException | InterruptedException e) {
          e.printStackTrace();
        }
      }

      private String factor(int n) {
        for (int i = 2; i < n; i++) {
          int x = n / i;
          if (x * i == n) return factor(x) + " × " + i;
        }
        return Integer.toString(n);

            if ( mechs != null ) {
                ASN1EncodableVector vector = new ASN1EncodableVector();
                for ( int i = 0; i < mechs.length; i++ ) {
                    vector.add(mechs[ i ]);
                }
                fields.add(new DERTaggedObject(true, 0, new DERSequence(vector)));
            }
            int ctxFlags = getContextFlags();
            if ( ctxFlags != 0 ) {