}

    /**
     * Retrieves a setting value as a float.
     * @param key The key of the setting.
     * @param defaultValue The default value if the setting is not found.
     * @return The setting value as a float.
     */
    public float getAsFloat(final String key, final float defaultValue) {
        final Object obj = get(key);

        final float value;
        if (obj == null) {

# Zusätzliche Datentypen { #extra-data-types }

Bisher haben Sie gängige Datentypen verwendet, wie zum Beispiel:

* `int`
* `float`
* `str`
* `bool`

Sie können aber auch komplexere Datentypen verwenden.

Und Sie haben immer noch dieselbe Funktionalität wie bisher gesehen:

* Großartige Editor-Unterstützung.
* Datenkonvertierung bei eingehenden <abbr title="Request – Anfrage: Daten, die der Client zum Server sendet">Requests</abbr>.

    )

    class FakeNumpyArray:
        def __init__(self):
            self.data = [1.0, 2.0, 3.0]

    FakeNumpyArrayPydantic = Annotated[
        FakeNumpyArray,
        WithJsonSchema(TypeAdapter(list[float]).json_schema()),
        PlainSerializer(lambda v: v.data),
    ]

    class MyModel(BaseModel):
        model_config = ConfigDict(arbitrary_types_allowed=True)
        custom_field: FakeNumpyArrayPydantic

  @Override
  public int intValue() {
    return (int) sum();
  }

  /** Returns the {@link #sum} as a {@code float} after a widening primitive conversion. */
  @Override
  public float floatValue() {
    return (float) sum();
  }

  /** Returns the {@link #sum} as a {@code double} after a widening primitive conversion. */
  @Override
  public double doubleValue() {

	Timestamp1 *PrimaryTerm `parser:" @@ \",\" "`
	Timestamp2 *PrimaryTerm `parser:" @@ \")\" "`
}

// LitValue represents a literal value parsed from the sql
type LitValue struct {
	Float   *float64       `parser:"(  @Float"`
	Int     *float64       `parser:" | @Int"` // To avoid value out of range, use float64 instead
	String  *LiteralString `parser:" | @LitString"`
	Boolean *Boolean       `parser:" | @(\"TRUE\" | \"FALSE\")"`

  values[0] = v;
  return TF_NewTensor(TF_DOUBLE, nullptr, 0, values, num_bytes,
                      &DoubleDeallocator, nullptr);
}

TF_Tensor* FloatTensor(float v) {
  const int num_bytes = sizeof(float);
  float* values = new float[1];
  values[0] = v;
  return TF_NewTensor(TF_FLOAT, nullptr, 0, values, num_bytes,
                      &FloatDeallocator, nullptr);
}

	case simdjson.TypeFloat:
		v, err := iter.Float()
		if err != nil {
			return nil, err
		}
		return sql.FromFloat(v), nil
	case simdjson.TypeInt:
		v, err := iter.Int()
		if err != nil {
			return nil, err
		}
		return sql.FromInt(v), nil
	case simdjson.TypeUint:
		v, err := iter.Int()
		if err != nil {
			// Can't fit into int, convert to float.
			v, err := iter.Float()
			return sql.FromFloat(v), err
		}

from pydantic.v1 import BaseModel


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

    assertThat(primitives)
        .containsExactly(
            boolean.class,
            byte.class,
            char.class,
            double.class,
            float.class,
            int.class,
            long.class,
            short.class,
            void.class);

    assertThrows(UnsupportedOperationException.class, () -> primitives.remove(boolean.class));
  }

    public void setAvailable(Boolean value) {
        registerModifiedProperty("available");
        this.available = value;
    }

    public Float getBoost() {
        checkSpecifiedProperty("boost");
        return boost;
    }

    public void setBoost(Float value) {
        registerModifiedProperty("boost");
        this.boost = value;
    }

    public String getCreatedBy() {