// 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;

  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) {

  // 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;

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...

    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));

==============================================================================*/

#include "tensorflow/c/eager/c_api_experimental.h"

#include <cstdint>
#include <memory>
#include <utility>
#include <vector>

#include "absl/container/flat_hash_map.h"
#include "absl/strings/match.h"
#include "absl/time/time.h"
#include "tensorflow/c/c_api.h"
#include "tensorflow/c/eager/c_api_internal.h"

    assertEquals(3, (int) enumer.nextElement());
    assertFalse(enumer.hasMoreElements());
  }

  private static Enumeration<Integer> enumerate(int... ints) {
    Vector<Integer> vector = new Vector<>(Ints.asList(ints));
    return vector.elements();
  }

  public void testToString() {
    Iterator<String> iterator = Lists.newArrayList("yam", "bam", "jam", "ham").iterator();

	if err != nil {
		return 0, fmt.Errorf("query() failure for '%s': %v", query, err)
	}

	log.Debugf("executing query: %s  result:%s", query, val)

	switch v := val.(type) {
	case model.Vector:
		if v.Len() < 1 {
			log.Debugf("no values for query: %s", query)
			return 0, nil
		}

		return float64(v[0].Value), nil
	default:
		return 0, errors.New("bad metric value type returned for query")

---

## **Typer**, FastAPI의 CLI

<a href="https://typer.tiangolo.com" target="_blank"><img src="https://typer.tiangolo.com/img/logo-margin/logo-margin-vector.svg" style="width: 20%;"></a>

웹 API 대신 터미널에서 사용할 <abbr title="Command Line Interface">CLI</abbr> 앱을 만들고 있다면, <a href="https://typer.tiangolo.com/" class="external-link" target="_blank">**Typer**</a>를 확인해 보십시오.

---

## **Typer**，命令列中的 FastAPI

<a href="https://typer.tiangolo.com" target="_blank"><img src="https://typer.tiangolo.com/img/logo-margin/logo-margin-vector.svg" style="width: 20%;"></a>

如果你不是在開發網頁 API，而是正在開發一個在終端機中運行的<abbr title="Command Line Interface">命令列</abbr>應用程式，不妨嘗試 <a href="https://typer.tiangolo.com/" class="external-link" target="_blank">**Typer**</a>。