SmbTreeHandleImpl t2 = h.getTree();

        assertSame(tree, t1, "Expected same mocked tree");
        assertSame(tree, t2, "Expected same mocked tree");

        // acquire is called once in ctor + once per getTree() call
        verify(tree, times(3)).acquire();
    }

    @Test
    @DisplayName("isValid() true when open, same tree id, connected")
    void isValid_true_whenOpenSameTreeConnected() {

And of course, it supports the same:

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

/// info

Keep in mind that dataclasses can't do everything Pydantic models can do.

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.

    #  You can drop additional other schemas.
    #  Elements of this map are as below:
    #   o url: (NotRequired - Default same as main schema)
    #   o schema: (Required: if empty schema means valid schema, not required)
    #   o user: (NotRequired - Default same as main schema)
    #   o password: (NotRequired - Default same as main schema)
    #   o propertiesMap: (NotRequired - Default map:{})

         * processes one artifact at a time and hence cannot associate the artifacts from the same project to use the
         * same version index. Allowing the caller to pass in metadata from a previous deployment allows to re-establish
         * the association between the artifacts of the same project.
         */
        for (Iterator<? extends Metadata> it = metadatas.iterator(); it.hasNext(); ) {

{* ../../docs_src/request_files/tutorial001_03_an_py39.py hl[9,15] *}

## Multiple File Uploads { #multiple-file-uploads }

It's possible to upload several files at the same time.

They would be associated to the same "form field" sent using "form data".

To use that, declare a list of `bytes` or `UploadFile`:

{* ../../docs_src/request_files/tutorial002_an_py39.py hl[10,15] *}

If you pass a "callable" as a dependency in **FastAPI**, it will analyze the parameters for that "callable", and process them in the same way as the parameters for a *path operation function*. Including sub-dependencies.

That also applies to callables with no parameters at all. The same as it would be for *path operation functions* with no parameters.

SS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),l||i(a,d),a}var k,l,m="3.7.3",n=a.html5||{},o=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,p=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,q="_html5shiv",r=0,s={};!function(){try{var a=b.createElement("a");a.innerHTML="<xyz></xyz>",k="hidden"in a,l=1==a....

You can use the same dependency *functions* you use normally.

### Dependency requirements { #dependency-requirements }

They can declare request requirements (like headers) or other sub-dependencies:

{* ../../docs_src/dependencies/tutorial006_an_py39.py hl[8,13] *}

### Raise exceptions { #raise-exceptions }

These dependencies can `raise` exceptions, the same as normal dependencies:

You can easily use the same Pydantic settings to configure your generated OpenAPI and the docs UIs.

For example:

{* ../../docs_src/conditional_openapi/tutorial001_py39.py hl[6,11] *}

Here we declare the setting `openapi_url` with the same default of `"/openapi.json"`.

And then we use it when creating the `FastAPI` app.