ARG BASE_DISTRIBUTION=debug

# Version is the base image version from the TLD Makefile
ARG BASE_VERSION=latest
ARG ISTIO_BASE_REGISTRY=gcr.io/istio-release

# The following section is used as base image if BASE_DISTRIBUTION=debug
FROM ${ISTIO_BASE_REGISTRY}/base:${BASE_VERSION} as debug

# The following section is used as base image if BASE_DISTRIBUTION=distroless

!!! warning
    There's a high chance that you **don't** need this base image or any other similar one, and would be better off by building the image from scratch as [described above in: Build a Docker Image for FastAPI](#build-a-docker-image-for-fastapi).

This image has an **auto-tuning** mechanism included to set the **number of worker processes** based on the CPU cores available.

mkdocs-material==9.5.18
mdx-include >=1.4.1,<2.0.0
mkdocs-redirects>=1.2.1,<1.3.0
typer >=0.12.0
pyyaml >=5.3.1,<7.0.0
# For Material for MkDocs, Chinese search
jieba==0.42.1
# For image processing by Material for MkDocs
pillow==10.3.0
# For image processing by Material for MkDocs
cairosvg==2.7.0
mkdocstrings[python]==0.24.3
griffe-typingdoc==0.2.2
# For griffe, it formats with black
black==24.3.0

    Note como `Offer` tem uma lista de `Item`s, que por sua vez possui opcionalmente uma lista `Image`s

## Corpos de listas puras

Se o valor de primeiro nível do corpo JSON que você espera for um `array` do JSON (uma` lista` do Python), você pode declarar o tipo no parâmetro da função, da mesma forma que nos modelos do Pydantic:


```Python
images: List[Image]
```

como em:

```Python hl_lines="15"

Par exemple, vous pouvez ajouter un type de média supplémentaire `image/png`, en déclarant que votre *opération de chemin* peut renvoyer un objet JSON (avec le type de média `application/json`) ou une image PNG :

```Python hl_lines="19-24 28"
{!../../../docs_src/additional_responses/tutorial002.py!}
```

!!! note "Remarque"
    Notez que vous devez retourner l'image en utilisant directement un `FileResponse`.

!!! info

I'll also show you the **official Docker image** that includes **Gunicorn with Uvicorn workers** and some default configurations that can be useful for simple cases.

        ops.
    *   Add `tf.image.psnr`, `tf.image.ssim`, `tf.image.ssim_multiscale`,
        `tf.image.image_gradients`, `tf.image.sobel_edges`.
    *   Add links to https://js.tensorflow.org.
    *   Fix non-uniformity of orthogonal matrices.
    *   Fix bug where multi-image Estimator eval summaries were not displayed
        correctly.

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