# Header Parameter Models

If you have a group of related **header parameters**, you can create a **Pydantic model** to declare them.

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`. 🤓

///

## Header Parameters with a Pydantic Model

  public void testEquals_otherListContainingNull() {
    List<E> other = new ArrayList<>(getSampleElements());
    other.set(other.size() / 2, null);
    assertFalse(
        "Two Lists should not be equal if exactly one of them has null at a given index.",
        getList().equals(other));
  }

  @CollectionSize.Require(absent = CollectionSize.ZERO)
  @CollectionFeature.Require(ALLOWS_NULL_VALUES)
  public void testEquals_containingNull() {

    return ImmutableDoubleArray.copyOf(Arrays.asList(values));
  }

  // Test generators.  To let the GWT test suite generator access them, they need to be public named
  // classes with a public default constructor (not that we run these suites under GWT yet).

  @J2ktIncompatible
  @GwtIncompatible // used only from suite

from typing import Annotated

from fastapi import FastAPI, Header, HTTPException
from pydantic import BaseModel

fake_secret_token = "coneofsilence"

fake_db = {
    "foo": {"id": "foo", "title": "Foo", "description": "There goes my hero"},
    "bar": {"id": "bar", "title": "Bar", "description": "The bartenders"},
}

app = FastAPI()


class Item(BaseModel):
    id: str
    title: str
    description: str | None = None

        hmac.digest(response, 0, 16);
        System.arraycopy(clientChallenge, 0, response, 16, 8);
        return response;
    }


    /**
     * Generate the Unicode MD4 hash for the password associated with these credentials.
     * 
     * @param password
     * @param challenge
     * @return the calculated response
     * @throws GeneralSecurityException
     */

   *     sure you are passing the two parameters in the right order.
   * @param actual The type that the formal type variable(s) are mapped to. It can be or contain yet
   *     other type variables, in which case these type variables will be further resolved if
   *     corresponding mappings exist in the current {@code TypeResolver} instance.
   */
  public TypeResolver where(Type formal, Type actual) {

    throw IllegalStateException(
      """
      |Unreadable ResponseBody! These Response objects have bodies that are stripped:
      | * Response.cacheResponse
      | * Response.networkResponse
      | * Response.priorResponse
      | * EventSourceListener
      | * WebSocketListener
      |(It is safe to call contentType() and contentLength() on these response bodies.)
      """.trimMargin(),
    )
  }

		key   *float64
	}{
		{"debug", "debug log", nil},
		{"info", "info log", nil},
		{"warn", "warn log", nil},
		{"error", "error log", nil},
		{"info", "with labels", ptr.Of(float64(2))},
	}
	// For each level, there should be two lines, one from direct log,
	// the other one from UDS server
	gotLogs := strings.Split(
		strings.TrimSuffix(string(out), "\n"), "\n")
	if want, got := len(cases)*2, len(gotLogs); want != got {

)
async def read_item(item_id: str, img: Union[bool, None] = None):
    if img:
        return FileResponse("image.png", media_type="image/png")
    else:

  if (enable) {
    optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::ON_1);

    // These XLA flags are needed to trigger XLA properly from C (more generally
    // non-Python) clients. If this API is called again with `enable` set to
    // false, it is safe to keep these flag values as is.
    tensorflow::MarkForCompilationPassFlags* flags =
        tensorflow::GetMarkForCompilationPassFlags();