```

////

### Про `**user_in.dict()`

#### `.dict()` из Pydantic

`user_in` - это Pydantic-модель класса `UserIn`.

У Pydantic-моделей есть метод `.dict()`, который возвращает `dict` с данными модели.

Поэтому, если мы создадим Pydantic-объект `user_in` таким способом:

```Python
user_in = UserIn(username="john", password="secret", email="******@****.***")
```

и затем вызовем:

```Python

```

////

### Sobre `**user_in.dict()`

#### O `.dict()` do Pydantic

`user_in` é um modelo Pydantic da classe `UserIn`.

Os modelos Pydantic possuem um método `.dict()` que retorna um `dict` com os dados do modelo.

Então, se criarmos um objeto Pydantic `user_in` como:

```Python
user_in = UserIn(username="john", password="secret", email="******@****.***")
```

e depois chamarmos:

```Python

        That's a lot of work they are doing, but if more FastAPI users came to help others like them just a little bit more, it would be much less effort for them (and you and me 😅).

        By asking questions in a structured way (following this) it will be much easier to help you.

  # The TFCI Mac VM image seems to have uncommitted local changes to the Pyenv
  # repository so we have to discard them and reset the working directory before
  # we can pull in the latest changes.
  cd /Users/kbuilder/.pyenv/ && git reset --hard HEAD && git pull && cd -
fi

# "TFCI_MACOS_PYENV_INSTALL_ENABLE" controls whether to use Pyenv to install
# the Python version set in "TFCI_PYTHON_VERSION" and use it as default.

latency, reduced cpu/memory consumption (~4mb per monitored node).
  * Pods now have a security context which allows users to specify:
     * attributes which apply to the whole pod:
        * User ID
        * Whether all containers should be non-root
        * Supplemental Groups
        * FSGroup - a special supplemental group
        * SELinux options

    }

    protected void registerModifiedProperty(String propertyName) {
        __modifiedProperties.addPropertyName(propertyName);
        registerSpecifiedProperty(propertyName); // synchronize if exists, basically for user's manual call
    }

    public void modifiedToSpecified() {
        if (__modifiedProperties.isEmpty()) {
            return; // basically no way when called in Framework (because called when SpecifyColumn exists)

active return 0 } # checkUserExists ($username) # Check if the user exists, by using the exit code of `mc admin user info` checkUserExists() { USER=$1 CMD=$(${MC} admin user info myminio $USER > /dev/null 2>&1) return $? } # createUser ($username, $password, $policy) createUser() { USER=$1 PASS=$2 POLICY=$3 # Create the user if it does not exist if ! checkUserExists $USER ; then echo "Creating user '$USER'" ${MC} admin user add myminio $USER $PASS else echo "User '$USER' already exists." fi # set policy...

 */
package jcifs;


/**
 * An Access Control Entry (ACE) is an element in a security descriptor
 * such as those associated with files and directories. The Windows OS
 * determines which users have the necessary permissions to access objects
 * based on these entries.
 * <p>
 * To fully understand the information exposed by this class a description

	err := d.DecodeElement(&dateStr, &startElement)
	if err != nil {
		return err
	}
	// While AWS documentation mentions that the date specified
	// must be present in ISO 8601 format, in reality they allow
	// users to provide RFC 3339 compliant dates.
	expDate, err := time.Parse(time.RFC3339, dateStr)
	if err != nil {
		return errLifecycleInvalidDate
	}
	// Allow only date timestamp specifying midnight GMT

///

### Über `**user_in.dict()`

#### Pydantic's `.dict()`

`user_in` ist ein Pydantic-Modell der Klasse `UserIn`.

Pydantic-Modelle haben eine `.dict()`-Methode, die ein `dict` mit den Daten des Modells zurückgibt.

Wenn wir also ein Pydantic-Objekt `user_in` erstellen, etwa so:

```Python
user_in = UserIn(username="john", password="secret", email="******@****.***")
```

und wir rufen seine `.dict()`-Methode auf: