Hoffmann, Tim Zaman, tomguluson92, Tongxuan Liu, Trent Lo, Trevor Morris,
TungJerry, Tyorden, Uday Bondhugula, v1incent, Vagif, Vasileios Lioutas,
vbvg2008, vcarpani, Vijay Ravichandran, Vikram Tiwari,Viktor Gal, Vishwak
Srinivasan, Vincent, Vishnuvardhan Janapati, Vitor-Alves, Vivek Suryamurthy,
wangsiyu, wateryzephyr, WeberXie, Wei Wang, WeijieSun, Wen-Heng (Jack) Chung,
wenxizhu, Will Battel, William D. Irons, winstonq, wyzhao, Xiaoming (Jason) Cui,

                                                           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);
  });
}

  // 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;

  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;

	}
}

// 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()

# 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:

  // Represents how tightly we can pack things, as a maximum.
  private static final double DESIRED_LOAD_FACTOR = 0.7;

  // If the set has this many elements, it will "max out" the table size
  private static final int CUTOFF = (int) (MAX_TABLE_SIZE * DESIRED_LOAD_FACTOR);

  /**
   * Returns an array size suitable for the backing array of a hash table that uses open addressing

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...

          command: [
            "/bin/sh",
            "-ce",