: public std::function<Status(const std::vector<Gradient*>&,
                                  std::vector<Gradient*>*, bool)> {
 public:
  template <typename lambda_type>
  explicit ForwardFunction(lambda_type lambda)
      : std::function<Status(const std::vector<Gradient*>&,
                             std::vector<Gradient*>*, bool)>(lambda) {}
};

// Computes Jacobian-vector products using forward-mode automatic

                                                           double growth_factor,
                                                           int bucket_count) {
  return new TFE_MonitoringBuckets([scale, growth_factor, bucket_count]() {
    return tensorflow::monitoring::Buckets::Exponential(scale, growth_factor,
                                                        bucket_count);
  });
}

  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) {

    // Input tensors
    const std::vector<std::pair<string, Tensor>>& input_pairs,
    // Output tensors
    const std::vector<string>& output_tensor_names, TF_Tensor** c_outputs,
    // Target nodes
    const std::vector<string>& target_oper_names, TF_Buffer* run_metadata,
    TF_Status* status) {
  const int noutputs = output_tensor_names.size();
  std::vector<Tensor> outputs(noutputs);
  Status result;

  // Process inputs.
  std::vector<tensorflow::OutputTensor> input_tensors;
  std::unordered_map<const Node*, std::vector<int>> input_nodes;
  status->status = tensorflow::ProcessInputs(fn_body, fn_name, ninputs, inputs,
                                             &input_tensors, &input_nodes);
  if (TF_GetCode(status) != TF_OK) return nullptr;

  // Process outputs.
  std::vector<tensorflow::OutputTensor> output_tensors;

	}
}

// Sleep sleeps the specified time multiplied by the sleep factor.
// If the factor is updated the sleep will be done again with the new factor.
func (d *dynamicSleeper) Sleep(ctx context.Context, base time.Duration) {
	for {
		// Grab current values
		d.mu.RLock()
		minWait, maxWait := d.minSleep, d.maxSleep
		factor := d.factor
		cycle := d.cycle
		d.mu.RUnlock()

    EXPECT_TRUE(found);
    TF_DeleteBuffer(op_list_buffer);
  }
}

void TestEncodeDecode(int line, const std::vector<string>& data) {
  const int64_t n = data.size();
  Status status;
  for (const std::vector<int64_t>& dims :
       std::vector<std::vector<int64_t>>{{n}, {1, n}, {n, 1}, {n / 2, 2}}) {
    // Create C++ Tensor
    Tensor src(tensorflow::DT_STRING, TensorShape(dims));

    /**
     * Get the model from the reactor based on the groupId and artifactId when resolving reactor dependencies.
     *
     * @param from    the requiring model
     * @param groupId    the groupId
     * @param artifactId the artifactId
     * @return the model, otherwise {@code null}
     * @throws IllegalStateException if multiple versions of the same GA are part of the reactor
     */

# use single binary and filesystem storage for test
# do not use in production
loki:
  useTestSchema: true
  auth_enabled: false
  commonConfig:
    replication_factor: 1
  storage:
    type: 'filesystem'
test:
  enabled: false
lokiCanary:
  enabled: false
monitoring:
  dashboards:
    enabled: false
  rules:
    enabled: false
    alerting: false
  serviceMonitor:
    enabled: false
    metricsInstance:

import java.util.Set;
import java.util.concurrent.ConcurrentHashMap;

import org.apache.maven.api.model.Model;

/**
 * Holds all Models that are known to the reactor. This allows the project builder to resolve imported Models from the
 * reactor when building another project's effective model.
 *
 */
class ReactorModelPool {
    private final Map<GAKey, Set<Model>> modelsByGa = new ConcurrentHashMap<>();