import com.google.common.annotations.GwtCompatible;
import com.google.common.base.Function;
import com.google.common.base.Functions;
import java.util.HashMap;
import java.util.Map;
import org.jspecify.annotations.NullMarked;

/**
 * Tests for {@link Maps#transformValues(Map, Function)}.
 *
 * @author Isaac Shum
 */
@GwtCompatible
@NullMarked

#include "absl/strings/match.h"
#include "tensorflow/c/c_api_internal.h"
#include "tensorflow/c/tf_buffer_internal.h"
#include "tensorflow/core/framework/attr_value_util.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/function.pb.h"
#include "tensorflow/core/framework/graph_to_functiondef.h"
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/framework/node_def_util.h"

```

The parameter values in your function will be:

* `skip=20`: because you set it in the URL
* `limit=10`: because that was the default value

## Optional parameters { #optional-parameters }

The same way, you can declare optional query parameters, by setting their default to `None`:

{* ../../docs_src/query_params/tutorial002_py310.py hl[7] *}

# Get Current User { #get-current-user }

In the previous chapter the security system (which is based on the dependency injection system) was giving the *path operation function* a `token` as a `str`:

{* ../../docs_src/security/tutorial001_an_py39.py hl[12] *}

But that is still not that useful.

Let's make it give us the current user.

## Create a user model { #create-a-user-model }

First, let's create a Pydantic user model.

In some cases you don't really need the return value of a dependency inside your *path operation function*.

Or the dependency doesn't return a value.

But you still need it to be executed/solved.

For those cases, instead of declaring a *path operation function* parameter with `Depends`, you can add a `list` of `dependencies` to the *path operation decorator*.

 *  @author Victor Jonsson, http://victorjonsson.se
 *  @license MIT
 */

 *  @author Victor Jonsson, http://victorjonsson.se
 *  @license MIT
 */

		[ $v1 == 0 -a $v2 == 0 ] && return 0
		sleep 10
	done
	return 1
}

function purge() {
	rm -rf "$1"
}

function fail() {
	for i in $(seq 1 3); do
		echo "server$i log:"
		cat "${WORK_DIR}/dist-minio-server$i.log"
	done
	pkill -9 minio
	echo "FAILED"
	purge "$WORK_DIR"
	exit 1
}

function __init__() {
	echo "Initializing environment"
	mkdir -p "$WORK_DIR"

participant code as Code
participant function as say_hi()
participant execute as Execute function

    rect rgba(0, 255, 0, .1)
        code ->> function: say_hi(name="Camila")
        function ->> execute: execute function code
        execute ->> code: return the result
    end

    rect rgba(0, 255, 255, .1)
        code ->> function: say_hi(name="Camila")
        function ->> code: return stored result
    end

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

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

So, if you have an object `something` (that might _not_ be a function) and you can "call" it (execute it) like:

```Python
something()
```

or

```Python
something(some_argument, some_keyword_argument="foo")
```

then it is a "callable".