# o filterVariablesMap: (NotRequired - Default map:{})
    #  You can specify the filter variables for DDL.
    #
    #; filterVariablesMap = map:{abc=AAA}
    # - - - - - - - - - -/

    # /- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    # o additionalUserMap: (NotRequired - Default map:{})
    #  You can set additional users.
    #  Elements of this map are as below:

There are several parameters that you can pass to your *path operation decorator* to configure it.

/// warning

Notice that these parameters are passed directly to the *path operation decorator*, not to your *path operation function*.

///

## Response Status Code { #response-status-code }

You can define the (HTTP) `status_code` to be used in the response of your *path operation*.

You can pass directly the `int` code, like `404`.

///

This is of course not the frontend for the final users, but it's a great automatic tool to document interactively all your API.

It can be used by the frontend team (that can also be yourself).

It can be used by third party applications and systems.

And it can also be used by yourself, to debug, check and test the same application.

## The `password` flow { #the-password-flow }

You can probably skip this part.

///

There's an alternative way to define this logic to be executed during *startup* and during *shutdown*.

You can define event handlers (functions) that need to be executed before the application starts up, or when the application is shutting down.

These functions can be declared with `async def` or normal `def`.

# Path Parameters and Numeric Validations { #path-parameters-and-numeric-validations }

In the same way that you can declare more validations and metadata for query parameters with `Query`, you can declare the same type of validations and metadata for path parameters with `Path`.

## Import Path { #import-path }

First, import `Path` from `fastapi`, and import `Annotated`:

{* ../../docs_src/path_params_numeric_validations/tutorial001_an_py310.py hl[1,3] *}

As all these methods are `async` methods, you need to "await" them.

For example, inside of an `async` *path operation function* you can get the contents with:

```Python
contents = await myfile.read()
```

If you are inside of a normal `def` *path operation function*, you can access the `UploadFile.file` directly, for example:

```Python
contents = myfile.file.read()
```

/// note | `async` Technical Details

If you use any other tool to manage your installations, like `uv`, Poetry, Pipenv, or others, they all have a way that you can use to define specific versions for your packages.

## Available versions { #available-versions }

You can see the available versions (e.g. to check what is the current latest) in the [Release Notes](../release-notes.md){.internal-link target=_blank}.

$ python main.py

// Now it can read the environment variable

Hello Wade Wilson from Python
```

</div>

////

As environment variables can be set outside of the code, but can be read by the code, and don't have to be stored (committed to `git`) with the rest of the files, it's common to use them for configurations or **settings**.

functionality. Syntax is `mc support inspect ALIAS/path/to/files`. This can for example be used to collect `xl.meta` from objects that are misbehaving. To collect `xl.meta` from a specific object, for example placed at `ALIAS/bucket/path/to/file.txt` append `/xl.meta`, for instance `mc support inspect ALIAS/bucket/path/to/file.txt/xl.meta`. All files can be collected, so this can also be used to retrieve `part.*` files, etc.

Wildcards can be used, for example `mc support inspect ALIAS/bucket/path/**/xl.meta`...

Therefore, if you run a `tf.train.Server` in your network, anybody with access
to the network can execute arbitrary code with the privileges of the user
running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized