#
# The cascade of load() statements and tf_workspace?() calls works around the
# restriction that load() statements need to be at the top of .bzl files.
# E.g. we can not retrieve a new repository with http_archive and then load()
# a macro from that repository in the same file.
load("@//tensorflow:workspace3.bzl", "tf_workspace3")

tf_workspace3()

 */
private val FirFunctionCall.functionReferenceName: Name?
    get() {
        toResolvedCallableReference()?.let { return it.name }

        // unresolved reference has incorrect name, so we have to retrieve it by PSI
        val wholeCallExpression = realPsi as? KtExpression
        val callExpression = wholeCallExpression?.getPossiblyQualifiedCallExpression()

    {!> ../../../docs_src/body_updates/tutorial002.py!}
    ```

### Partial updates recap

In summary, to apply partial updates you would:

* (Optionally) use `PATCH` instead of `PUT`.
* Retrieve the stored data.
* Put that data in a Pydantic model.
* Generate a `dict` without default values from the input model (using `exclude_unset`).

  - LDAP/AD users and their respective service accounts
  - OpenID/OIDC service accounts

- On versioned buckets, FTP/SFTP only operates on latest objects, if you need to retrieve
  an older version you must use an `S3 API client` such as [`mc`](https://github.com/minio/mc).

- All features currently used by your buckets will work as is without any changes
  - SSE (Server Side Encryption)

  If <<<offline == true>>>:

  * If the wagon is not bound to "file://", then ignore the request and print
    a debug message.

  * If the wagon is bound to "file://" then:

    Retrieve the file or base-url file to be "downloaded".

    * If the file (or more usefully, the base-url file) exists, proceed.

    * If the file (or base-url file) doesn't exist, assume that this location

  // the server has more data available. The value is opaque and may be used to issue another request
  // to the endpoint that served this list to retrieve the next set of available objects. Continuing a
  // consistent list may not be possible if the server configuration has changed or more than a few

            // Given `fun f(...): R { blockExpression }`, `{ blockExpression }` is mapped to the enclosing anonymous function,
            // which may raise an exception if we attempt to retrieve, e.g., callable declaration from it.
            return null
        }
        if (function.typeReference == null) return null
        return getReturnTypeForKtDeclaration(function).nonErrorTypeOrNull()
    }

- Using the id_token the callback handler further talks to Google OAuth2 Token URL to obtain an JWT id_token.
- Once obtained the JWT id_token is further sent to STS endpoint i.e MinIO to retrieve temporary credentials.
- Temporary credentials are displayed on the browser upon successful retrieval.

## Using MinIO Console

  // along with values. Kubelet will expose these topology keys as labels
  // on its own node object.
  // When Kubernetes does topology aware provisioning, it can use this list to
  // determine which labels it should retrieve from the node object and pass
  // back to the driver.
  // It is possible for different nodes to use different topology keys.
  // This can be empty if driver does not support topology.
  // +optional

But recent versions of Pydantic allow providing a custom class that inherits from `pydantic.utils.GetterDict`, to provide the functionality used when using the `orm_mode = True` to retrieve the values for ORM model attributes.

We are going to create a custom `PeeweeGetterDict` class and use it in all the same Pydantic *models* / schemas that use `orm_mode`:

```Python hl_lines="3  8-13  31  49"