in.Error("unquoting #line file name: ", err)
	}
	tok = in.Stack.Next()
	if tok != '\n' {
		in.Error("unexpected token at end of #line: ", tok)
	}
	pos := src.MakePos(in.Base(), uint(in.Line())+1, 1) // +1 because #line nnn means line nnn starts on next line
	in.Stack.SetBase(src.NewLinePragmaBase(pos, file, objabi.AbsFile(objabi.WorkingDir(), file, *flags.TrimPath), uint(line), 1))
}

// #undef processing

Потім нижче покращимо безпеку і гнучкість за допомогою кількох моделей. 🤓

### Створіть моделі { #create-models }

Імпортуйте `SQLModel` і створіть модель бази даних:

{* ../../docs_src/sql_databases/tutorial001_an_py310.py ln[1:11] hl[7:11] *}

Клас `Hero` дуже схожий на модель Pydantic (фактично, під капотом це і є модель Pydantic).

Є кілька відмінностей:

Éamonn McManus <******@****.***> 1773097141 -0700

### Modelleri Oluşturma { #create-models }

`SQLModel`’i import edin ve bir veritabanı modeli oluşturun:

{* ../../docs_src/sql_databases/tutorial001_an_py310.py ln[1:11] hl[7:11] *}

`Hero` sınıfı, bir Pydantic modeline çok benzer (hatta altta aslında *bir Pydantic modelidir*).

Birkaç fark var:

```java
if (!context.getConfig().isUseLeases() || !session.supports(SMB3_0)) {
    // Fall back to traditional oplock mechanism
    useOplockInstead();
}
```

## 11. Monitoring and Metrics

### 11.1 Lease Statistics
```java
public class LeaseStatistics {
    private final AtomicLong leasesRequested = new AtomicLong();
    private final AtomicLong leasesGranted = new AtomicLong();

### Criar Modelos { #create-models }

Importe o `SQLModel` e crie um modelo de banco de dados:

{* ../../docs_src/sql_databases/tutorial001_an_py310.py ln[1:11] hl[7:11] *}

A classe `Hero` é muito semelhante a um modelo Pydantic (na verdade, por baixo dos panos, ela *é um modelo Pydantic*).

Existem algumas diferenças:

    }
    throw new AssertionError();
  }

  @GwtIncompatible // TODO
  private static BigInteger sqrtFloor(BigInteger x) {
    /*
     * Adapted from Hacker's Delight, Figure 11-1.
     *
     * Using DoubleUtils.bigToDouble, getting a double approximation of x is extremely fast, and
     * then we can get a double approximation of the square root. Then, we iteratively improve this

  static final double OTHER_MANY_VALUES_MEAN = (1.11 - 2.22 + 33.3333 - 44.4444 + 555.555555) / 5;

  static final double MANY_VALUES_SUM_OF_PRODUCTS_OF_DELTAS =
      (1.1 - MANY_VALUES_MEAN) * (1.11 - OTHER_MANY_VALUES_MEAN)
          + (-44.44 - MANY_VALUES_MEAN) * (-2.22 - OTHER_MANY_VALUES_MEAN)
          + (33.33 - MANY_VALUES_MEAN) * (33.3333 - OTHER_MANY_VALUES_MEAN)

    rangeSet.add(Range.closed(3, 10));
    rangeSet.addAll(asList(Range.open(1, 3), Range.closed(5, 8), Range.closed(9, 11)));
    assertThat(rangeSet.asRanges()).containsExactly(Range.openClosed(1, 11)).inOrder();
  }

  public void testRemoveAll() {
    RangeSet<Integer> rangeSet = TreeRangeSet.create();
    rangeSet.add(Range.closed(3, 10));

  static final double OTHER_MANY_VALUES_MEAN = (1.11 - 2.22 + 33.3333 - 44.4444 + 555.555555) / 5;

  static final double MANY_VALUES_SUM_OF_PRODUCTS_OF_DELTAS =
      (1.1 - MANY_VALUES_MEAN) * (1.11 - OTHER_MANY_VALUES_MEAN)
          + (-44.44 - MANY_VALUES_MEAN) * (-2.22 - OTHER_MANY_VALUES_MEAN)
          + (33.33 - MANY_VALUES_MEAN) * (33.3333 - OTHER_MANY_VALUES_MEAN)