### `HeroPublic`으로 단일 Hero 조회하기

단일 hero을 **조회**할 수도 있습니다:

{* ../../docs_src/sql_databases/tutorial002_an_py310.py ln[75:80] hl[77] *}

### `HeroUpdate`로 Hero 수정하기

**hero를 수정**할 수도 있습니다. 이를 위해 HTTP `PATCH` 작업을 사용합니다.

코드에서는 클라이언트가 보낸 데이터를 딕셔너리 형태(`dict`)로 가져옵니다. 이는 **클라이언트가 보낸 데이터만 포함**하며, 기본값으로 들어가는 값은 제외합니다. 이를 위해 `exclude_unset=True`를 사용합니다. 이것이 주요 핵심입니다. 🪄

   * Returns a comparator that compares two {@code char} arrays <a
   * href="http://en.wikipedia.org/wiki/Lexicographical_order">lexicographically</a>; not advisable
   * for sorting user-visible strings as the ordering may not match the conventions of the user's
   * locale. That is, it compares, using {@link #compare(char, char)}), the first pair of values
   * that follow any common prefix, or when one array is a prefix of the other, treats the shorter

      // TODO(lowasser): could be potentially optimized, but only with
      // extensive testing
      try {
        return Float.parseFloat(string);
      } catch (NumberFormatException e) {
        // Float.parseFloat has changed specs several times, so fall through
        // gracefully
      }
    }
    return null;
  }

      int thisSize = 0;
      for (E e : this) {
        try {
          if (!that.contains(e)) {
            return false;
          }
        } catch (NullPointerException | ClassCastException ignored) {
          return false;
        }
        thisSize++;
      } // that.containsAll(this) so that.size() >= this.size()

      if (thisSize == thatMaxSize) {

    List<Object> resultArray = doConcurrentGet(cache, "bar", count, startSignal);

    assertThat(callCount.get()).isEqualTo(1);
    for (int i = 0; i < count; i++) {
      assertWithMessage("result(%s) didn't match expected", i)
          .that(resultArray.get(i))
          .isSameInstanceAs(result);
    }
  }

  /**
   * On a concurrent computation that returns null, all threads should get an

        """
        Add a *path operation* using an HTTP PATCH operation.

        ## Example

        ```python
        from fastapi import FastAPI
        from pydantic import BaseModel

        class Item(BaseModel):
            name: str
            description: str | None = None

        app = FastAPI()

        @app.patch("/items/")
        def update_item(item: Item):

That, plus the simple fact that Python is the main language for **Data Science**, Machine Learning and especially Deep Learning, make FastAPI a very good match for Data Science / Machine Learning web APIs and applications (among many others).

To see how to achieve this parallelism in production see the section about [Deployment](deployment/index.md){.internal-link target=_blank}.

    List<Object> resultArray = doConcurrentGet(cache, "bar", count, startSignal);

    assertThat(callCount.get()).isEqualTo(1);
    for (int i = 0; i < count; i++) {
      assertWithMessage("result(%s) didn't match expected", i)
          .that(resultArray.get(i))
          .isSameInstanceAs(result);
    }
  }

  /**
   * On a concurrent computation that returns null, all threads should get an

	var buf bytes.Buffer
	tw := NewWriter(&buf)
	if err := tw.AddFS(fsys); err != nil {
		t.Fatal(err)
	}
	if err := tw.Close(); err != nil {
		t.Fatal(err)
	}

	// Add subfolder into fsys to match what we'll read from the tar.
	fsys["subfolder"] = &fstest.MapFile{Mode: 0o555 | os.ModeDir}

	// Test that we can get the files back from the archive
	tr := NewReader(&buf)

    if (FLOATING_POINT_PATTERN.matcher(string).matches()) {
      // TODO(lowasser): could be potentially optimized, but only with
      // extensive testing
      try {
        return Double.parseDouble(string);
      } catch (NumberFormatException e) {
        // Double.parseDouble has changed specs several times, so fall through
        // gracefully
      }
    }
    return null;
  }