{* ../../docs_src/security/tutorial005_an_py310.py hl[106,108:116] *}

## Verify the `username` and data shape

We verify that we get a `username`, and extract the scopes.

      gtl::ArraySlice<AbstractTensorHandle*> output_gradients,
      absl::Span<AbstractTensorHandle*> result) const override;

  // Builds a tensor filled with ones with the same shape and dtype as `t`.
  absl::Status BuildOnesLike(const TapeTensor& t,
                             AbstractTensorHandle** result) const override;

  // Looks up the ID of a Gradient.

     * 
     * @return local NETBIOS/short name to announce
     */
    String getNetbiosHostname ();


    /**
     * 
     * Property <tt>jcifs.smb.client.logonShare</tt>
     * 
     * @return share to connect to during authentication, if unset connect to IPC$
     */
    String getLogonShare ();


    /**
     * 
     * 
     * Property <tt>jcifs.smb.client.domain</tt>
     * 

## Pydantic models

<a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a> is a Python library to perform data validation.

You declare the "shape" of the data as classes with attributes.

And each attribute has a type.

For example, if you declare a parameter of type `Hero`, it will be read from the **JSON body**.

The same way, you can declare it as the function's **return type**, and then the shape of the data will show up in the automatic API docs UI.

{* ../../docs_src/sql_databases/tutorial001_an_py310.py ln[40:45] hl[40:45] *}

</details>

build:rbe_linux --linkopt=-lrt
build:rbe_linux --host_linkopt=-lrt
build:rbe_linux --linkopt=-lm
build:rbe_linux --host_linkopt=-lm

build:rbe_linux_cpu --config=rbe_linux
# Linux cpu and cuda builds share the same toolchain now.
build:rbe_linux_cpu --host_crosstool_top="@local_config_cuda//crosstool:toolchain"
build:rbe_linux_cpu --crosstool_top="@local_config_cuda//crosstool:toolchain"

And multiple processes normally **don't share any memory**. This means that each running process has its own things, variables, and memory. And if you are consuming a large amount of memory in your code, **each process** will consume an equivalent amount of memory.

## Create a Pull Request

You can [contribute](contributing.md){.internal-link target=_blank} to the source code with Pull Requests, for example:

* To fix a typo you found on the documentation.
* To share an article, video, or podcast you created or found about FastAPI by <a href="https://github.com/fastapi/fastapi/edit/master/docs/en/data/external_links.yml" class="external-link" target="_blank">editing this file</a>.

# Create targets for TARGET_OUT_LINUX/binary
# There are two use cases here:
# * Building all docker images (generally in CI). In this case we want to build everything at once, so they share work
# * Building a single docker image (generally during dev). In this case we just want to build the single binary alone
BUILD_ALL ?= true
define build-linux
.PHONY: $(TARGET_OUT_LINUX)/$(shell basename $(1))

 */
// TODO: Descriptive assertion messages, with hints as to probable fixes.
// TODO: Add another constructor parameter indicating whether the class under test is ordered, and
// check the order if so.
// TODO: Refactor to share code with SetTestBuilder etc.
@GwtCompatible
@ElementTypesAreNonnullByDefault
public abstract class MapInterfaceTest<K extends @Nullable Object, V extends @Nullable Object>
    extends TestCase {