// license that can be found in the LICENSE file.

package main

type TestSuite struct {
	Tests []int
}

var Suites = []TestSuite{
	Dicts,
}
var Dicts = TestSuite{
	Tests: []int{0},
}

func main() {
	if &Dicts.Tests[0] != &Suites[0].Tests[0] {
		panic("bad")
	}

type TestSuite struct {
	Tests []Test
}
type Test struct {
	Want interface{}
}
type Int struct {
	i int
}

func NewInt(v int) Int {
	return Int{i: v}
}

var Suites = []TestSuite{
	Dicts,
}
var Dicts = TestSuite{
	Tests: []Test{
		{
			Want: map[Int]bool{NewInt(1): true},
		},
		{
			Want: map[Int]string{
				NewInt(3): "3",
			},
		},
	},
}

func main() {

        final String response = checkMethodBase(searchBody).get("/api/admin/dict").asString();
        final List<Map<String, String>> dicts = JsonPath.from(response).getList("response.settings");

        for (Map<String, String> item : dicts) {
            assertTrue(item.containsKey("id"));
            assertTrue(item.containsKey("type"));
            if (getDictType().equals(item.get("type"))) {

        final int total = JsonPath.from(response).getInt("response.total");
        final List<Map<String, String>> dicts = JsonPath.from(response).getList("response.settings");
        final int status = JsonPath.from(response).getInt("response.status");
        assertEquals(total, dicts.size());
        assertEquals(0, status);
    }

    @Override
    protected void tearDown() {
        // do nothing
    }

  @tf.function(input_signature=[[
      tf.TensorSpec([], tf.float32),
      tf.TensorSpec([], tf.float32),
  ]])
  def f0001_list_2_elements(self, l):
    return

  # Check index paths for dicts.
  # Keys are linearized in sorted order, matching `tf.nest.flatten`.
  #
  # CHECK:      func {{@[a-zA-Z_0-9]+}}(
  # CHECK-SAME:   %arg0: tensor<1xf32> {tf._user_specified_name = "d", tf_saved_model.index_path = [0, "x"]},

    In particular, func's in SavedModel are called with a structured value, and
    return a structured value. A structured value consists of arbitrarily nested
    dicts/lists with tensors as leaves (composite tensors here mostly behave as
    just dicts holding other tensors).
    The arity of the Python-level function is modeled as an outer list.
    Additionally, any variables or constants used by the function are implicitly

  @tf.function(input_signature=[])
  def f0004_list_2_elements(self):
    return [[tf.constant(1.0, shape=[1]), tf.constant(1.0, shape=[2])]]

  # Check index paths for dicts.
  # Keys are linearized in sorted order, matching `tf.nest.flatten`.
  # More thorough testing of this is in structured_input.py. The underlying code
  # path for linearization is shared, so no need to replicate that testing here.

Wenn Sie nun die Dokumentation ansehen, werden dort alle zusätzlichen Metadaten angezeigt:

<img src="/img/tutorial/metadata/image02.png">

### Reihenfolge der Tags

Die Reihenfolge der Tag-Metadaten-Dicts definiert auch die Reihenfolge, in der diese in der Benutzeroberfläche der Dokumentation angezeigt werden.

Você não conseguiria este tipo de suporte de editor se estivesse trabalhando diretamente com `dict` em vez de modelos Pydantic.

Mas você também não precisa se preocupar com eles, os dicts de entrada são convertidos automaticamente e sua saída é convertida automaticamente para JSON também.

## Corpos de `dict`s arbitrários

<img src="/img/tutorial/body-nested-models/image01.png">

You couldn't get this kind of editor support if you were working directly with `dict` instead of Pydantic models.

But you don't have to worry about them either, incoming dicts are converted automatically and your output is converted automatically to JSON too.

## Bodies of arbitrary `dict`s

You can also declare a body as a `dict` with keys of some type and values of some other type.