Pydantic によって、標準的な Python の型を使ってボディを宣言します。

```Python hl_lines="2  7-10 23-25"
from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: bool | None = None


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: str | None = None):

    Iterator<String> iterator = asList("one", "two", "three", "four", "five").iterator();
    IllegalArgumentException expected =
        assertThrows(IllegalArgumentException.class, () -> getOnlyElement(iterator));
    assertThat(expected)
        .hasMessageThat()
        .isEqualTo("expected one element but was: <one, two, three, four, five>");
  }

      this.elements = elements;
      this.duplicate = duplicate;
    }
  }

  /**
   * @return an array of the proper size with a duplicate element. The size must be at least three.
   */
  protected ArrayWithDuplicate<E> createArrayWithDuplicateElement() {
    E[] elements = createSamplesArray();
    E duplicate = elements[(elements.length / 2) - 1];

        testData.add(createRelatedContent("term2", "Content for default", ""));
        mockBhv.setTestData(testData);

        int count = relatedContentHelper.load();
        assertEquals(3, count); // Three virtual host keys: "host1", "host2", ""
    }

    @Test
    public void test_getHostKey_emptyVirtualHost() {
        RelatedContent entity = createRelatedContent("term", "content", "");

 */
enum BloomFilterStrategies implements BloomFilter.Strategy {
  /**
   * See "Less Hashing, Same Performance: Building a Better Bloom Filter" by Adam Kirsch and Michael
   * Mitzenmacher. The paper argues that this trick doesn't significantly deteriorate the
   * performance of a Bloom filter (yet only needs two 32bit hash functions).
   */
  MURMUR128_MITZ_32() {
    @Override
    public <T extends @Nullable Object> boolean put(

import org.jspecify.annotations.Nullable;

/**
 * A descriptor for a <i>discrete</i> {@code Comparable} domain such as all {@link Integer}
 * instances. A discrete domain is one that supports the three basic operations: {@link #next},
 * {@link #previous} and {@link #distance}, according to their specifications. The methods {@link
 * #minValue} and {@link #maxValue} should also be overridden for bounded types.
 *

* у [PyCharm](https://www.jetbrains.com/pycharm/):

![editor support](https://fastapi.tiangolo.com/img/pycharm-completion.png)

Ви отримаєте автодоповнення в коді, який раніше могли вважати навіть неможливим. Наприклад, для ключа `price` всередині JSON body (який міг бути вкладеним), що надходить із запиту.

            // While we could look up the domain controller/KDC we cannot really make the java kerberos implementation
            // use a KDC of our choice.
            // A potential workaround would be to try to get the server FQDN by reverse lookup, but this might have
            // security implications and also is not how Microsoft does it.

		FuncName:  event,
		Duration:  duration,
		Path:      pathJoin(oi.Bucket, oi.Name),
		Bytes:     sz,
		Error:     err,
		Message:   getSource(4),
		Custom:    metadata,
	}
}

func (sys *LifecycleSys) trace(oi ObjectInfo) func(event string, metadata map[string]string, err error) {
	startTime := time.Now()
	return func(event string, metadata map[string]string, err error) {
		duration := time.Since(startTime)

  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s)) << TF_Message(s);

  // Test for a scalar.
  TF_Operation* three = ScalarConst(3, graph, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  TF_Output three_out_0 = TF_Output{three, 0};

  num_dims = TF_GraphGetTensorNumDims(graph, three_out_0, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  EXPECT_EQ(0, num_dims);