So, you would run **multiple containers** with different things, like a database, a Python application, a web server with a React frontend application, and connect them together via their internal network.

All the container management systems (like Docker or Kubernetes) have these networking features integrated into them.

## Containers and Processes { #containers-and-processes }

{* ../../docs_src/generate_clients/tutorial004.py *}

//// tab | Node.js

```Javascript
{!> ../../docs_src/generate_clients/tutorial004.js!}
```

////

With that, the operation IDs would be renamed from things like `items-get_items` to just `get_items`, that way the client generator can generate simpler method names.

{* ../../docs_src/body_nested_models/tutorial002.py hl[1] *}

### Declare a `list` with a type parameter { #declare-a-list-with-a-type-parameter }

To declare types that have type parameters (internal types), like `list`, `dict`, `tuple`:

* If you are in a Python version lower than 3.9, import their equivalent version from the `typing` module
* Pass the internal type(s) as "type parameters" using square brackets: `[` and `]`

# words checklist
segfault_memory:
   - segfault
   - memory leaks
# assignees
filesystem_security_assignee:
   - mihaimaruseac
   
# Cuda Comment
cuda_comment: >
   From the template it looks like you are installing **TensorFlow** (TF) prebuilt binaries:
      * For TF-GPU - See point 1
      * For TF-CPU - See point 2
   -----------------------------------------------------------------------------------------------
   

 * Futures#transform(ListenableFuture, com.google.common.base.Function, Executor) Futures.transform}
 * (or {@link FluentFuture#transform(com.google.common.base.Function, Executor)
 * FluentFuture.transform}), but you will often find it easier to use a framework. Frameworks
 * automate the process, often adding features like monitoring, debugging, and cancellation.

</ul>
<p>
Some memory operations are <i>read-like</i>, including read, atomic read, mutex lock, and channel receive.
Other memory operations are <i>write-like</i>, including write, atomic write, mutex unlock, channel send, and channel close.
Some, such as atomic compare-and-swap, are both read-like and write-like.
</p>

<p>

## Pydantic Models for Input and Output { #pydantic-models-for-input-and-output }

Let's say you have a Pydantic model with default values, like this one:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:7] hl[7] *}

### Model for Input { #model-for-input }

If you use this model as an input like here:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:15] hl[14] *}

# Help FastAPI - Get Help { #help-fastapi-get-help }

Do you like **FastAPI**?

Would you like to help FastAPI, other users, and the author?

Or would you like to get help with **FastAPI**?

There are very simple ways to help (several involve just one or two clicks).

And there are several ways to get help too.

## Subscribe to the newsletter { #subscribe-to-the-newsletter }

///

## 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`.

But if you don't remember what each number code is for, you can use the shortcut constants in `status`:

{* ../../docs_src/path_operation_configuration/tutorial001_py310.py hl[1,15] *}

```

that would mean that you would use the versions `0.112.0` or above, but less than `0.113.0`, for example, a version `0.112.2` would still be accepted.

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 }