## Use Case

Let's start with an example **use case** and then see how to solve it with this.

Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖

The same models are shared among requests, so, it's not one model per request, or one per user or something similar.

        <artifactId>modello-maven-plugin</artifactId>
        <configuration>
          <version>1.2.0</version>
          <models>
            <model>src/main/mdo/core-extensions.mdo</model>
          </models>
          <templates>
            <template>model.vm</template>
          </templates>
          <params>
            <param>packageModelV4=org.apache.maven.api.cli.extensions</param>

    "functionalTests": true,
    "crossVersionTests": false
  },
  {
    "name": "model-core",
    "path": "platforms/core-configuration/model-core",
    "unitTests": true,
    "functionalTests": true,
    "crossVersionTests": false
  },
  {
    "name": "model-groovy",
    "path": "platforms/core-configuration/model-groovy",
    "unitTests": true,
    "functionalTests": true,
    "crossVersionTests": false

    page.get_by_role("button", name="Try it out").click()
    page.get_by_role("heading", name="Servers").click()
    # Manually add the screenshot
    page.screenshot(path="docs/en/docs/img/tutorial/query-param-models/image01.png")

    # ---------------------
    context.close()
    browser.close()


process = subprocess.Popen(
    ["fastapi", "run", "docs_src/query_param_models/tutorial001.py"]
)
try:

hould-try-7c0ac7eebb3e title: 5 Advanced Features of FastAPI You Should Try - author: Kaustubh Gupta author_link: https://medium.com/@kaustubhgupta1828/ link: https://www.analyticsvidhya.com/blog/2021/06/deploying-ml-models-as-api-using-fastapi-and-heroku/ title: Deploying ML Models as API Using FastAPI and Heroku - link: https://jarmos.netlify.app/posts/using-github-actions-to-deploy-a-fastapi-project-to-heroku/ title: Using GitHub Actions to Deploy a FastAPI Project to Heroku author_link: http...

              <goal>velocity</goal>
            </goals>
            <configuration>
              <version>4.0.0</version>
              <models>
                <model>target/mdo/maven.mdo</model>
              </models>
              <templates>
                <template>src/main/mdo/model-v3.vm</template>
                <template>src/main/mdo/merger.vm</template>
                <template>src/main/mdo/transformer.vm</template>

from enum import Enum

from fastapi import FastAPI


class ModelName(str, Enum):
    alexnet = "alexnet"
    resnet = "resnet"
    lenet = "lenet"


app = FastAPI()


@app.get("/models/{model_name}")
async def get_model(model_name: ModelName):
    if model_name is ModelName.alexnet:
        return {"model_name": model_name, "message": "Deep Learning FTW!"}

    if model_name.value == "lenet":

     * @return the transformed model, or the input model if no transformation is needed
     * @throws ModelTransformerException
     */
    @Nonnull
    default Model transformFileModel(@Nonnull Model model) throws ModelTransformerException {
        return model;
    }

    /**
     * Apply a transformation on the raw models.
     *
     * This method will be called on each raw model being loaded,

    disabled = user_dict["disabled"],
    hashed_password = user_dict["hashed_password"],
)
```

/// info

For a more complete explanation of `**user_dict` check back in [the documentation for **Extra Models**](../extra-models.md#about-user_indict){.internal-link target=_blank}.

///

## Return the token

The response of the `token` endpoint must be a JSON object.

package controllers;

import models.*;
import org.codelibs.fess.suggest.request.suggest.SuggestResponse;
import org.elasticsearch.common.xcontent.XContentBuilder;
import org.elasticsearch.common.xcontent.json.JsonXContent;
import play.Logger;
import play.mvc.Controller;
import play.mvc.Result;

import java.util.Map;

public class Suggest extends Controller {
    public static Result get() {