# Klassen als Abhängigkeiten

Bevor wir tiefer in das **Dependency Injection** System eintauchen, lassen Sie uns das vorherige Beispiel verbessern.

## Ein `dict` aus dem vorherigen Beispiel

Im vorherigen Beispiel haben wir ein `dict` von unserer Abhängigkeit („Dependable“) zurückgegeben:

//// tab | Python 3.10+

```Python hl_lines="9"
{!> ../../docs_src/dependencies/tutorial001_an_py310.py!}
```

////

//// tab | Python 3.9+

The MinIO server runs a key-derivation algorithm to generate the KEK using a pseudo-random function ([PRF](#prf)):
`KEK := PRF(EK, IV, context_values)` where:

def main(user_id: str):
    return user_id


# Ein Pydantic-Modell
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Das kann nun wie folgt verwendet werden:

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

second_user_data = {
    "id": 4,
    "name": "Mary",
    "joined": "2018-11-30",
}

* Be very, very careful.
* The cache has a builtin test, enabled with `UNSAFE_PILOT_ENABLE_RUNTIME_ASSERTIONS=true`, that runs in CI. This will panic if any key is written to with a different value.

#### Partial Computations

	// Object operations.

	// GetObjectNInfo returns a GetObjectReader that satisfies the
	// ReadCloser interface. The Close method runs any cleanup
	// functions, so it must always be called after reading till EOF
	//
	// IMPORTANTLY, when implementations return err != nil, this
	// function MUST NOT return a non-nil ReadCloser.

        };
    assertEquals(2, Iterables.size(iterable));
  }

  @SuppressWarnings("serial")
  public void testSize_collection_doesntIterate() {
    List<Integer> nums = asList(1, 2, 3, 4, 5);
    List<Integer> collection =
        new ArrayList<Integer>(nums) {
          @Override
          public Iterator<Integer> iterator() {
            throw new AssertionFailedError("Don't iterate me!");
          }
        };

   *
   * <p>Lookups by row key are often faster than lookups by column key, because the data is stored
   * in a {@code Map<R, Map<C, V>>}. A method call like {@code column(columnKey).get(rowKey)} still
   * runs quickly, since the row key is provided. However, {@code column(columnKey).size()} takes
   * longer, since an iteration across all row keys occurs.
   *

 * subtype of {@code Network} that provides methods for adding and removing nodes and edges. If you
 * do not need to mutate a network (e.g. if you write a method than runs a read-only algorithm on
 * the network), you should use the non-mutating {@link Network} interface, or an {@link
 * ImmutableNetwork}.
 *
 * <p>You can create an immutable copy of an existing {@code Network} using {@link

 * of {@code Graph} that provides methods for adding and removing nodes and edges. If you do not
 * need to mutate a graph (e.g. if you write a method than runs a read-only algorithm on the graph),
 * you should use the non-mutating {@link Graph} interface, or an {@link ImmutableGraph}.
 *
 * <p>You can create an immutable copy of an existing {@code Graph} using {@link

 * of {@code Graph} that provides methods for adding and removing nodes and edges. If you do not
 * need to mutate a graph (e.g. if you write a method than runs a read-only algorithm on the graph),
 * you should use the non-mutating {@link Graph} interface, or an {@link ImmutableGraph}.
 *
 * <p>You can create an immutable copy of an existing {@code Graph} using {@link