# Move extra wheel files somewhere out of the way. This script
# expects just one wheel file to exist.
if [[ "$(ls *.whl | wc -l | tr -d ' ')" != "1" ]]; then
  echo "More than one wheel file is present: moving the oldest to"
  echo "$TFCI_OUTPUT_DIR/extra_wheels."
  # List all .whl files by their modification time (ls -t) and move anything
  # other than the most recently-modified one (the newest one).
  mkdir -p $TFCI_OUTPUT_DIR/extra_wheels

```

where `3.12` is the `Python` version you wish to update.

Note, since it is still `pip` and `pip-compile` tools used under the hood, so
most of the command line arguments and features supported by those tools will be
acknowledged by the Bazel requirements updater command as well. For example, if
you wish the updater to consider pre-release versions simply pass `--pre`

///

/// note | Technical Details

You could also use `from starlette.responses import JSONResponse`.

**FastAPI** provides the same `starlette.responses` as `fastapi.responses` just as a convenience for you, the developer. But most of the available responses come directly from Starlette. The same with `status`.

///

## OpenAPI and API docs { #openapi-and-api-docs }

///

/// note | Technical Details

You could also use `from starlette.templating import Jinja2Templates`.

**FastAPI** provides the same `starlette.templating` as `fastapi.templating` just as a convenience for you, the developer. But most of the available responses come directly from Starlette. The same with `Request` and `StaticFiles`.

///

## Writing templates { #writing-templates }

You could also use `from starlette.responses import Response` or `from starlette.responses import JSONResponse`.

**FastAPI** provides the same `starlette.responses` as `fastapi.responses` just as a convenience for you, the developer. But most of the available responses come directly from Starlette.

And as the `Response` can be used frequently to set headers and cookies, **FastAPI** also provides it at `fastapi.Response`.

///

## Running on Startup { #running-on-startup }

In most cases, when you create a web API, you want it to be **always running**, uninterrupted, so that your clients can always access it. This is of course, unless you have a specific reason why you want it to run only in certain situations, but most of the time you want it constantly running and **available**.

### In a Remote Server { #in-a-remote-server }

# limitations under the License.
# ==============================================================================


# This Workflow updates tensorflow/tools/toolchains/remote_config/configs.bzl
# to reference the most recent versions of the SIG Build Docker images.
name: Update RBE Configs
on:
  workflow_dispatch:

permissions:
  contents: read

jobs:
  rbe:
    name: Update RBE Configs
    runs-on: ubuntu-latest

        }
        /*
         * It would perhaps be better to do this more selectively instead of applying this transform so broadly and having
         * it just no-op in most cases.
         */
        registerTransform(Minify::class) {
            from.attribute(minified, false).attribute(artifactType, "jar")
            to.attribute(minified, true).attribute(artifactType, "jar")

{* ../../docs_src/body/tutorial001_py310.py hl[5:9] *}


The same as when declaring query parameters, when a model attribute has a default value, it is not required. Otherwise, it is required. Use `None` to make it just optional.

For example, this model above declares a JSON "`object`" (or Python `dict`) like:

```JSON
{
    "name": "Foo",
    "description": "An optional description",
    "price": 45.2,
    "tax": 3.5
}

//
// In most cases, new([Buffer]) (or just declaring a [Buffer] variable) is
// sufficient to initialize a [Buffer].
func NewBuffer(buf []byte) *Buffer { return &Buffer{buf: buf} }

// NewBufferString creates and initializes a new [Buffer] using string s as its
// initial contents. It is intended to prepare a buffer to read an existing
// string.
//