Oder Sie verfügen möglicherweise über eine native Mobile-Anwendung, die direkt in nativem Code mit Ihrem WebSocket-Backend kommuniziert.

Oder Sie haben andere Möglichkeiten, mit dem WebSocket-Endpunkt zu kommunizieren.

---

Für dieses Beispiel verwenden wir jedoch ein sehr einfaches HTML-Dokument mit etwas JavaScript, alles in einem langen String.

```Python
UserInDB(
    username = user_dict["username"],
    password = user_dict["password"],
    email = user_dict["email"],
    full_name = user_dict["full_name"],
)
```

#### Ein Pydantic-Modell aus dem Inhalt eines anderen { #a-pydantic-model-from-the-contents-of-another }

Da wir im obigen Beispiel `user_dict` von `user_in.model_dump()` bekommen haben, wäre dieser Code:

```Python
user_dict = user_in.model_dump()

* die Wildcard
* zurückgeben
* validieren

////

//// tab | Info

// Information about the shape of a Tensor and its type.
struct TF_ShapeAndType {
  // Number of dimensions. -1 indicates unknown rank.
  int num_dims;
  // Array of dimensions. -1 indicates unknown dim.
  int64_t* dims;
  // The data type. May be 0 to denote unknown type.
  TF_DataType dtype;
};

typedef struct TF_ShapeAndType TF_ShapeAndType;

// A list of TF_ShapeAndType elements..

      shape.dims = nullptr;
      continue;
    }
    shape.dims = new int64_t[shape.num_dims];
    for (size_t j = 0; j < shape.num_dims; ++j) {
      shape.dims[j] = c.Value(c.Dim(shape_handle, j));
    }
  }
  if (output_shapes != nullptr) *output_shapes = output_shapes_result;

  // TODO(bgogul): Set output_resource_shapes_and_types.
}

    return;
  }

  // Rank is greater than 0, so fill in the values, if known, and
  // -1 for unknown values.
  for (int i = 0; i < num_dims; ++i) {
    auto dim = ic->Dim(shape, i);
    int64_t value = -1;
    if (ic->ValueKnown(dim)) {
      value = ic->Value(dim);
    }
    dims[i] = value;
  }
}

// TF_OperationDescription functions ------------------------------------------

extern "C" {

#undef EXPECT_TF_META

TEST(CAPI, TestTensorAligned) {
  int64_t dim = 7;
  size_t tensor_size_bytes = dim * TF_DataTypeSize(TF_FLOAT);
  TF_Tensor* a = TF_AllocateTensor(
      /*dtype=*/TF_FLOAT, /*dims=*/&dim, /*num_dims=*/1,
      /*len=*/tensor_size_bytes);
  float* data = reinterpret_cast<float*>(TF_TensorData(a));
  for (int i = 0; i < dim; ++i) {
    data[i] = 0;
  }
  if (TF_TensorDefaultAlignment() > 0) {

pkg encoding/base64, var RawURLEncoding *Encoding
pkg encoding/json, method (*Decoder) More() bool
pkg encoding/json, method (*Decoder) Token() (Token, error)
pkg encoding/json, method (Delim) String() string
pkg encoding/json, type Delim int32
pkg encoding/json, type Token interface {}
pkg encoding/json, type UnmarshalTypeError struct, Offset int64
pkg flag, func UnquoteUsage(*Flag) (string, string)

t-Samhain","An D\xf9bhlachd"],monthsShort:["Faoi","Gear","M\xe0rt","Gibl","C\xe8it","\xd2gmh","Iuch","L\xf9n","Sult","D\xe0mh","Samh","D\xf9bh"],monthsParseExact:!0,weekdays:["Did\xf2mhnaich","Diluain","Dim\xe0irt","Diciadain","Diardaoin","Dihaoine","Disathairne"],weekdaysShort:["Did","Dil","Dim","Dic","Dia","Dih","Dis"],weekdaysMin:["D\xf2","Lu","M\xe0","Ci","Ar","Ha","Sa"],longDateFormat:{LT:"HH:mm",LTS:"HH:mm:ss",L:"DD/MM/YYYY",LL:"D MMMM YYYY",LLL:"D MMMM YYYY HH:mm",LLLL:"dddd, D MMMM YYYY ...

labels.data_crawling_button_create=Erstellen
labels.data_crawling_button_create_job=Neuen Job erstellen
labels.wizard_title_configuration=Konfigurationsassistent
labels.wizard_start_title=Einfache Einrichtung
labels.wizard_start_desc=Mit dem Konfigurationsassistenten können Sie ganz einfach eine Crawling-Einstellung erstellen.
labels.wizard_start_button=Einrichtung starten
labels.wizard_button_cancel=Abbrechen
labels.wizard_crawling_config_title=Crawl-Konfiguration