Then you can use this to generate a `dict` with only the data that was set (sent in the request), omitting default values:

{* ../../docs_src/body_updates/tutorial002_py310.py hl[32] *}

### Using Pydantic's `update` parameter { #using-pydantics-update-parameter }

The way HTML forms (`<form></form>`) sends the data to the server normally uses a "special" encoding for that data, it's different from JSON.

**FastAPI** will make sure to read that data from the right place instead of JSON.

/// note | Technical Details

Data from forms is normally encoded using the "media type" `application/x-www-form-urlencoded`.

// of the read content as ETag. Optionally, it also computes
// the SHA256 checksum of the content.
//
// If the reference values for the ETag and content SHA26
// are not empty then it will check whether the computed
// match the reference values.
type Reader struct {
	src         io.Reader
	bytesRead   int64
	expectedMin int64
	expectedMax int64

	size       int64
	actualSize int64

* `limit`: with a value of `10`

As they are part of the URL, they are "naturally" strings.

But when you declare them with Python types (in the example above, as `int`), they are converted to that type and validated against it.

All the same process that applied for path parameters also applies for query parameters:

* Editor support (obviously)

          <description>The version of the extension.</description>
          <version>1.0.0+</version>
          <required>true</required>
          <type>String</type>
        </field>
        <field>
          <name>classLoadingStrategy</name>

# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

 * the fact that the problem reporter has/should not have information about the calling context and hence cannot provide
 * an expressive source hint for the model problem. Instead, the source hint is configured by the model builder before
 * it delegates to other components that potentially encounter problems. Then, the problem reporter can focus on

Declare the **cookie** parameters that you need in a **Pydantic model**, and then declare the parameter as `Cookie`:

{* ../../docs_src/cookie_param_models/tutorial001_an_py310.py hl[9:12,16] *}

**FastAPI** will **extract** the data for **each field** from the **cookies** received in the request and give you the Pydantic model you defined.

## Check the Docs { #check-the-docs }

As indicated, each of those components require a Java runtime to run.

The recommended way of invoking Gradle is to use the Gradle Wrapper.
The Wrapper then downloads the Gradle distribution (if necessary), starts the Launcher and runs the build, spawning and connecting to other processes such as the Daemon as required.

# linking against the C and C++ APIs (such as libjpeg).  When we create
# the shared library, only export the core TF API functions to avoid
# causing library conflicts (e.g., those reported in github issue 1924).
# On Linux, tell the linker (-Wl,<option>) to use a version script that
# excludes all but a subset of function names.
# On MacOS, the linker does not support version_script, but has an