if not fields:
        return fields
    first_field = fields[0]
    if len(fields) == 1 and lenient_issubclass(first_field.type_, BaseModel):
        fields_to_extract = get_cached_model_fields(first_field.type_)
        return fields_to_extract
    return fields


def get_flat_params(dependant: Dependant) -> List[ModelField]:

 * governing permissions and limitations under the License.
 */
package org.codelibs.fess.crawler.entity;

/**
 * @author shinsuke
 *
 */
public class UrlQueueImpl<IDTYPE> implements UrlQueue<IDTYPE> {
    protected IDTYPE id;

    protected String sessionId;

    protected String method;

    protected String url;

    protected String metaData;

    protected String encoding;

 * governing permissions and limitations under the License.
 */
package org.codelibs.fess.crawler.entity;

/**
 * @author shinsuke
 *
 */
public interface UrlQueue<IDTYPE> {

    IDTYPE getId();

    void setId(IDTYPE id);

    String getSessionId();

    void setSessionId(String sessionId);

    String getMethod();

    void setMethod(String method);

    String getUrl();

These types that have internal types are called "**generic**" types. And it's possible to declare them, even with their internal types.

To declare those types and the internal types, you can use the standard Python module `typing`. It exists specifically to support these type hints.

#### Newer versions of Python

mdurl==0.1.2 \
    --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \
    --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba
    # via markdown-it-py
ml-dtypes==0.4.0 \
    --hash=sha256:03e7cda6ef164eed0abb31df69d2c00c3a5ab3e2610b6d4c42183a43329c72a5 \
    --hash=sha256:2bb83fd064db43e67e67d021e547698af4c8d5c6190f2e9b1c53c09f6ff5531d \

mdurl==0.1.2 \
    --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \
    --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba
    # via markdown-it-py
ml-dtypes==0.4.0 \
    --hash=sha256:03e7cda6ef164eed0abb31df69d2c00c3a5ab3e2610b6d4c42183a43329c72a5 \
    --hash=sha256:2bb83fd064db43e67e67d021e547698af4c8d5c6190f2e9b1c53c09f6ff5531d \

mdurl==0.1.2 \
    --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \
    --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba
    # via markdown-it-py
ml-dtypes==0.4.0 \
    --hash=sha256:03e7cda6ef164eed0abb31df69d2c00c3a5ab3e2610b6d4c42183a43329c72a5 \
    --hash=sha256:2bb83fd064db43e67e67d021e547698af4c8d5c6190f2e9b1c53c09f6ff5531d \

~~ under the License.

 ---
 Default Dependency Types
 ---
 Hervé Boutemy
 ---
 2024-04-02
 ---

Default Dependency Types Reference

  Defined in <<<DefaultTypeProvider>>>
  ({{{./apidocs/org/apache/maven/repository/internal/type/DefaultTypeProvider.html}javadoc}},
  {{{./xref/org/apache/maven/repository/internal/type/DefaultTypeProvider.html}source}}):

mdurl==0.1.2 \
    --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \
    --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba
    # via markdown-it-py
ml-dtypes==0.4.0 \
    --hash=sha256:03e7cda6ef164eed0abb31df69d2c00c3a5ab3e2610b6d4c42183a43329c72a5 \
    --hash=sha256:2bb83fd064db43e67e67d021e547698af4c8d5c6190f2e9b1c53c09f6ff5531d \

        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