But if we use the same model for another *path operation*, we could be sending our user's passwords to every client.

/// danger

Never store the plain password of a user or send it in a response like this, unless you know all the caveats and you know what you are doing.

///

## Add an output model { #add-an-output-model }

        the `run_eagerly` property, and will correctly run using `tf.function`
        by default. Note that `Model.fit_generator`, `Model.evaluate_generator`,
        and `Model.predict_generator` are deprecated endpoints. They are
        subsumed by `Model.fit`, `Model.evaluate`, and `Model.predict` which now
        support generators and Sequences.
*   `tf.lite`
    *   Legalization for `NMS` ops in TFLite.

series title: Building simple E-Commerce with NuxtJS and FastAPI - author: Rodrigo Arenas author_link: https://rodrigo-arenas.medium.com/ link: https://medium.com/analytics-vidhya/serve-a-machine-learning-model-using-sklearn-fastapi-and-docker-85aabf96729b title: "Serve a machine learning model using Sklearn, FastAPI and Docker" - author: Yashasvi Singh author_link: https://hashnode.com/@aUnicornDev link: https://aunicorndev.hashnode.dev/series/supafast-api title: "Building an API with FastAPI and...

package configurations

import common.Os
import common.buildScanTagParam
import common.getBuildScanCustomValueParam
import model.CIBuildModel
import model.Stage

class SanityCheck(
    model: CIBuildModel,
    stage: Stage,
) : OsAwareBaseGradleBuildType(os = Os.LINUX, stage = stage, init = {
        id(buildTypeId(model))
        name = "Sanity Check"
        description = "Static code analysis, checkstyle, release notes verification, etc."

### Model for Input { #model-for-input }

If you use this model as an input like here:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:15] hl[14] *}

...then the `description` field will **not be required**. Because it has a default value of `None`.

### Input Model in Docs { #input-model-in-docs }

package configurations

import common.Os.LINUX
import common.buildScanTagParam
import common.getBuildScanCustomValueParam
import model.CIBuildModel
import model.Stage

class BuildDistributions(
    model: CIBuildModel,
    stage: Stage,
) : OsAwareBaseGradleBuildType(os = LINUX, stage = stage, init = {
        id("${model.projectId}_BuildDistributions")
        name = "Build Distributions"

This would allow you to **re-use the model** in **multiple places** and also to declare validations and metadata for all the parameters at once. 😎

/// note

This is supported since FastAPI version `0.115.0`. 🤓

///

## Query Parameters with a Pydantic Model { #query-parameters-with-a-pydantic-model }

{* ../../docs_src/body/tutorial001_py310.py hl[2] *}

## Create your data model { #create-your-data-model }

Then you declare your data model as a class that inherits from `BaseModel`.

Use standard Python types for all the attributes:

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

# Extra Models { #extra-models }

Continuing with the previous example, it will be common to have more than one related model.

This is especially the case for user models, because:

* The **input model** needs to be able to have a password.
* The **output model** should not have a password.
* The **database model** would probably need to have a hashed password.

/// danger

     */
    @Nonnull
    Optional<Boolean> plugins();

    /**
     * Should apply all upgrade options (equivalent to --model-version 4.1.0 --infer --model --plugins).
     * This is a convenience option that combines model upgrade, inference, compatibility fixes, and plugin upgrades.
     *
     * @return an {@link Optional} containing the boolean value {@code true} if specified, or empty
     */