}
			case float64:
				field.ReflectValueOf(ctx, value).SetFloat(data)
			case float32:
				field.ReflectValueOf(ctx, value).SetFloat(float64(data))
			case int64:
				field.ReflectValueOf(ctx, value).SetFloat(float64(data))
			case int:
				field.ReflectValueOf(ctx, value).SetFloat(float64(data))
			case int8:
				field.ReflectValueOf(ctx, value).SetFloat(float64(data))
			case int16:

yields a typed constant.
</p>

<pre>
uint(iota)               // iota value of type uint
float32(2.718281828)     // 2.718281828 of type float32
complex128(1)            // 1.0 + 0.0i of type complex128
float32(0.49999999)      // 0.5 of type float32
float64(-1e-1000)        // 0.0 of type float64
string('x')              // "x" of type string
string(0x266c)           // "♬" of type string

    *   Layers now default to `float32`, and automatically cast their inputs to
        the layer's dtype. If you had a model that used `float64`, it will
        probably silently use `float32` in TensorFlow2, and a warning will be
        issued that starts with Layer "layer-name" is casting an input tensor
        from dtype float64 to the layer's dtype of float32. To fix, either set
        the default dtype to float64 with

		return globalVeeamForceSC
	}
	return s
}

type ordered interface {
	~int | ~int8 | ~int16 | ~int32 | ~int64 | ~uint | ~uint8 | ~uint16 | ~uint32 | ~uint64 | ~uintptr | ~float32 | ~float64 | string
}

// mapKeysSorted returns the map keys as a sorted slice.
func mapKeysSorted[Map ~map[K]V, K ordered, V any](m Map) []K {
	res := make([]K, 0, len(m))
	for k := range m {

		}
	case obj.TYPE_FCONST:
		switch sz {
		case 4:
			nameAddr.Sym.WriteFloat32(p.ctxt, nameAddr.Offset, float32(valueAddr.Val.(float64)))
		case 8:
			nameAddr.Sym.WriteFloat64(p.ctxt, nameAddr.Offset, valueAddr.Val.(float64))
		default:
			p.errorf("bad float size for DATA argument: %d", sz)
		}
	case obj.TYPE_SCONST:
		nameAddr.Sym.WriteString(p.ctxt, nameAddr.Offset, sz, valueAddr.Val.(string))

  ASSERT_EQ(TF_OK, TF_GetCode(s.get())) << TF_Message(s.get());
  ASSERT_NE(*func, nullptr);
}

REGISTER_OP("CustomOp")
    .Output("output: float32")
    .Attr("index: int")
    .SetShapeFn(tensorflow::shape_inference::UnknownShape);

void NodeWithPlaceholderAttrHelper(TF_Graph* graph, TF_Status* s,

   * to the JDK until JDK 7.
   *
   * @param a the first {@code float} to compare
   * @param b the second {@code float} to compare
   * @return the result of invoking {@link Float#compare(float, float)}
   */
  @InlineMe(replacement = "Float.compare(a, b)")
  public static int compare(float a, float b) {
    return Float.compare(a, b);
  }

  /**

			if intA > result {
				result = intA
			}
		}
		v.setInt(result)
		return nil
	}

	floatV, _ := v.ToFloat()
	floatA, _ := a.ToFloat()
	var result float64
	if !isMax {
		result = math.Min(floatV, floatA)
	} else {
		result = math.Max(floatV, floatA)
	}
	v.setFloat(result)
	return nil
}

func inferTypeAsTimestamp(v *Value) error {
	if s, ok := v.ToString(); ok {

		err = errors.New("entity too large")
		return partSize, err
	}

	configuredPartSize := minPartSize
	// Use floats for part size for all calculations to avoid
	// overflows during float64 to int64 conversions.
	partSizeFlt := float64(objectSize / maxPartsCount)
	partSizeFlt = math.Ceil(partSizeFlt/float64(configuredPartSize)) * float64(configuredPartSize)

	// Part size.
	partSize = int64(partSizeFlt)
	if partSize == 0 {

	}
}

func floatCast(v *Value) (float64, error) {
	switch x := v.value.(type) {
	case float64:
		return x, nil
	case int64:
		return float64(x), nil
	case string:
		f, err := strconv.ParseFloat(strings.TrimSpace(x), 64)
		if err != nil {
			return 0, errCastFailure("could not parse as float")
		}
		return f, nil
	case []byte: