// Add boundary values manually to avoid over/under flow (this covers 2^N for 0 and 31).
    intValues.add(Integer.MAX_VALUE - 1, Integer.MAX_VALUE);
    // Add values up to 40. This covers cases like "square of a prime" and such.
    for (int i = 1; i <= 40; i++) {
      intValues.add(i);
    }
    // Now add values near 2^N for lots of values of N.
    for (int exponent : asList(2, 3, 4, 9, 15, 16, 17, 24, 25, 30)) {

          | (1 << 29));

  /**
   * Returns {@code true} if {@code n} is a <a
   * href="http://mathworld.wolfram.com/PrimeNumber.html">prime number</a>: an integer <i>greater
   * than one</i> that cannot be factored into a product of <i>smaller</i> positive integers.
   * Returns {@code false} if {@code n} is zero, one, or a composite number (one which <i>can</i> be

    //     ends up at a[d], which in turn ends up at a[2d], and so on until we get back to a[0].
    //     (All indices taken mod n.) If d and n are mutually prime, all elements will have been
    //     moved at that point. Otherwise, we can rotate the cycle a[1], a[1 + d], a[1 + 2d], etc,
    //     then a[2] etc, and so on until we have rotated all elements. There are gcd(d, n) cycles

    hashFunctions[0] = Murmur3_128HashFunction.GOOD_FAST_HASH_128;
    int seed = GOOD_FAST_HASH_SEED;
    for (int i = 1; i < hashFunctionsNeeded; i++) {
      seed += 1500450271; // a prime; shouldn't matter
      hashFunctions[i] = murmur3_128(seed);
    }
    return new ConcatenatedHashFunction(hashFunctions);
  }

  /**

```Python hl_lines="4  9-12  25-27"
from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: Union[bool, None] = None


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):

```JSON hl_lines="2"
{
    "item": {
        "name": "Foo",
        "description": "The pretender",
        "price": 42.0,
        "tax": 3.2
    }
}
```

instead of:

```JSON
{
    "name": "Foo",
    "description": "The pretender",
    "price": 42.0,
    "tax": 3.2
}
```

## Recap { #recap }

    public void test_evaluate_withSpecialCharacters() {
        String template = "Price: $${price} (with ${discount}% off)";
        Map<String, Object> paramMap = new HashMap<>();
        paramMap.put("price", "100");
        paramMap.put("discount", "20");

        Object result = scriptEngine.evaluate(template, paramMap);
        assertEquals("Price: $100 (with 20% off)", result);
    }

So, if you send a request to that *path operation* for the item with ID `foo`, the response (not including default values) will be:

```JSON
{
    "name": "Foo",
    "price": 50.2
}
```

/// info

In Pydantic v1 the method was called `.dict()`, it was deprecated (but still supported) in Pydantic v2, and renamed to `.model_dump()`.

```Python hl_lines="4  9-12  25-27"
from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: Union[bool, None] = None


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):

You will also get autocompletion for the payload to send:

<img src="/img/tutorial/generate-clients/image03.png">

/// tip

Notice the autocompletion for `name` and `price`, that was defined in the FastAPI application, in the `Item` model.

///

You will have inline errors for the data that you send:

<img src="/img/tutorial/generate-clients/image04.png">