## List fields { #list-fields }

You can define an attribute to be a subtype. For example, a Python `list`:

{* ../../docs_src/body_nested_models/tutorial001_py310.py hl[12] *}

This will make `tags` be a list, although it doesn't declare the type of the elements of the list.

## List fields with type parameter { #list-fields-with-type-parameter }

But Python has a specific way to declare lists with internal types, or "type parameters":

import org.apache.maven.api.annotations.Provider;

/**
 * This interface supplies the API for providing feedback to the user from the {@code Mojo},
 * using standard Maven channels.
 * There should be no big surprises here, although you may notice that the methods accept
 * <code>java.lang.CharSequence</code> rather than <code>java.lang.String</code>. This is provided mainly as a

     * Return the <code>InputStream</code> used to read information
     * from this pipe instance. Presumably data would first be written
     * to the <code>OutputStream</code> associated with this Named
     * Pipe instance although this is not a requirement (e.g. a
     * read-only named pipe would write data to this stream on
     * connection). Reading from this stream may block. Therefore it

   * method may return before refresh completes.
   *
   * @since 11.0
   */
  @GwtIncompatible
  @J2ktIncompatible
  void refresh(K key);

  /**
   * {@inheritDoc}
   *
   * <p><b>Note that although the view <i>is</i> modifiable, no method on the returned map will ever
   * cause entries to be automatically loaded.</b>
   */
  @Override
  @J2ktIncompatible
  ConcurrentMap<K, V> asMap();

{* ../../docs_src/path_operation_advanced_configuration/tutorial007_py310.py hl[15:20, 22] *}

Nevertheless, although we are not using the default integrated functionality, we are still using a Pydantic model to manually generate the JSON Schema for the data that we want to receive in YAML.

We can do better...

## What makes a dependency { #what-makes-a-dependency }

Up to now you have seen dependencies declared as functions.

But that's not the only way to declare dependencies (although it would probably be the more common).

The key factor is that a dependency should be a "callable".

A "**callable**" in Python is anything that Python can "call" like a function.

 *
 * <p>While {@code ReadWriteLock} instances have different properties and can form cycles without
 * potential deadlock, this class treats {@code ReadWriteLock} instances as equivalent to
 * traditional exclusive locks. Although this increases the false positives that the locks detect
 * (i.e. cycles that will not actually result in deadlock), it simplifies the algorithm and

   * method may return before refresh completes.
   *
   * @since 11.0
   */
  @GwtIncompatible
  @J2ktIncompatible
  void refresh(K key);

  /**
   * {@inheritDoc}
   *
   * <p><b>Note that although the view <i>is</i> modifiable, no method on the returned map will ever
   * cause entries to be automatically loaded.</b>
   */
  @Override
  @J2ktIncompatible
  ConcurrentMap<K, V> asMap();

* `description`: A long description that can contain Markdown text.
* `value`: This is the actual example shown, e.g. a `dict`.
* `externalValue`: alternative to `value`, a URL pointing to the example. Although this might not be supported by as many tools as `value`.

You can use it like this:

{* ../../docs_src/schema_extra_example/tutorial005_an_py310.py hl[23:49] *}

Practically every Gradle feature is behind a flag, and additional sub-flags or options provide further control.

The number of existing Gradle properties is already in the double digits. More properties will be added in the future.
Although properties offer the benefit of precise user-side control and support gradual evolution practices, the high number of properties also presents challenges.

The main challenge on the user side is comprehensibility.