`tf.linalg.matvec` convenience function.
    *   `tf.einsum()`raises `ValueError` for unsupported equations like
        `"ii->"`.
    *   Add DCT-I and IDCT-I in `tf.signal.dct` and `tf.signal.idct`.
    *   Add LU decomposition op.
    *   Add quantile loss to gradient boosted trees in estimator.
    *   Add `round_mode` to `QuantizeAndDequantizeV2` op to select rounding
        algorithm.

/// info

In Pydantic v1 the method was called `.dict()`, it was deprecated (but still supported) in Pydantic v2, and renamed to `.model_dump()`.

The examples here use `.dict()` for compatibility with Pydantic v1, but you should use `.model_dump()` instead if you can use Pydantic v2.

///

### About `**user_in.dict()` { #about-user-in-dict }

#### Pydantic's `.dict()` { #pydantics-dict }

`user_in` is a Pydantic model of class `UserIn`.

        // Test dictionary paths structure
        assertEquals("/admin/dict/admin_dict.jsp", FessHtmlPath.path_AdminDict_AdminDictJsp.getRoutingPath());
        assertEquals("/admin/dict/kuromoji/admin_dict_kuromoji.jsp",
                FessHtmlPath.path_AdminDictKuromoji_AdminDictKuromojiJsp.getRoutingPath());

The same way, this database wouldn't receive a Pydantic model (an object with attributes), only a `dict`.

You can use `jsonable_encoder` for that.

It receives an object, like a Pydantic model, and returns a JSON compatible version:

{* ../../docs_src/encoder/tutorial001_py310.py hl[4,21] *}

In this example, it would convert the Pydantic model to a `dict`, and the `datetime` to a `str`.

* The variable `items_s` is a `set`, and each of its items is of type `bytes`.

#### Dict { #dict }

To define a `dict`, you pass 2 type parameters, separated by commas.

The first type parameter is for the keys of the `dict`.

The second type parameter is for the values of the `dict`:

//// tab | Python 3.9+

```Python hl_lines="1"
{!> ../../docs_src/python_types/tutorial008_py39.py!}

* `Body()`
* `Form()`
* `File()`

The keys of the `dict` identify each example, and each value is another `dict`.

Each specific example `dict` in the `examples` can contain:

* `summary`: Short description for the example.
* `description`: A long description that can contain Markdown text.
* `value`: This is the actual example shown, e.g. a `dict`.

Before diving deeper into the **Dependency Injection** system, let's upgrade the previous example.

## A `dict` from the previous example { #a-dict-from-the-previous-example }

In the previous example, we were returning a `dict` from our dependency ("dependable"):

{* ../../docs_src/dependencies/tutorial001_an_py310.py hl[9] *}

But then we get a `dict` in the parameter `commons` of the *path operation function*.

 */
package org.codelibs.fess.dict.protwords;

import java.io.ByteArrayInputStream;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.InputStream;
import java.nio.charset.StandardCharsets;
import java.util.Date;

import org.codelibs.fess.dict.DictionaryException;
import org.codelibs.fess.dict.DictionaryFile.PagingList;

/// info

In Pydantic v1 the method was called `.dict()`, it was deprecated (but still supported) in Pydantic v2, and renamed to `.model_dump()`.

The examples here use `.dict()` for compatibility with Pydantic v1, but you should use `.model_dump()` instead if you can use Pydantic v2.

///

That would generate a `dict` with only the data that was set when creating the `item` model, excluding default values.

By default, **FastAPI** would automatically convert that return value to JSON using the `jsonable_encoder` explained in [JSON Compatible Encoder](../tutorial/encoder.md){.internal-link target=_blank}.