Nils Lindemann <******@****.***> 1711821718 +0100

So, a Python class is also a **callable**.

Then, in **FastAPI**, you could use a Python class as a dependency.

What FastAPI actually checks is that it is a "callable" (function, class or anything else) and the parameters defined.

If you pass a "callable" as a dependency in **FastAPI**, it will analyze the parameters for that "callable", and process them in the same way as the parameters for a *path operation function*. Including sub-dependencies.

from .param_functions import File as File
from .param_functions import Form as Form
from .param_functions import Header as Header
from .param_functions import Path as Path
from .param_functions import Query as Query
from .param_functions import Security as Security
from .requests import Request as Request
from .responses import Response as Response
from .routing import APIRouter as APIRouter
from .websockets import WebSocket as WebSocket

            firDiagnostic as KtPsiDiagnostic,
            token,
        )
    }
    add(FirErrors.INTERFACE_AS_FUNCTION) { firDiagnostic ->
        InterfaceAsFunctionImpl(
            firSymbolBuilder.classifierBuilder.buildClassLikeSymbol(firDiagnostic.a),
            firDiagnostic as KtPsiDiagnostic,
            token,
        )
    }
    add(FirErrors.EXPECT_CLASS_AS_FUNCTION) { firDiagnostic ->

	}

	r = dryDB.Table("people as p").Table("user as u").Find(&User{}).Statement
	if !regexp.MustCompile("SELECT \\* FROM user as u WHERE .u.\\..deleted_at. IS NULL").MatchString(r.Statement.SQL.String()) {
		t.Errorf("Table with escape character, got %v", r.Statement.SQL.String())
	}

	r = dryDB.Table("people as p").Table("user").Find(&User{}).Statement

        // TODO Consider handling other non-ConeLookupTagBasedType types here (see KT-66418)
        val type = getDeclaredType()?.lowerBoundIfFlexible() as? ConeLookupTagBasedType

        return type?.toTargetSymbol(session, symbolBuilder) ?: run {
            val diagnostic = (this as? FirErrorTypeRef)?.diagnostic

  // resource metric across all relevant pods, represented as a percentage of
  // the requested value of the resource for the pods.
  // +optional
  optional int32 targetAverageUtilization = 2;

  // targetAverageValue is the target value of the average of the
  // resource metric across all relevant pods, as a raw value (instead of as
  // a percentage of the request), similar to the "pods" metric source type.

    from pydantic.fields import FieldInfo
    from pydantic.json_schema import GenerateJsonSchema as GenerateJsonSchema
    from pydantic.json_schema import JsonSchemaValue as JsonSchemaValue
    from pydantic_core import CoreSchema as CoreSchema
    from pydantic_core import PydanticUndefined, PydanticUndefinedType
    from pydantic_core import Url as Url

    try:

import distutils as _distutils
import importlib
import inspect as _inspect
import os as _os
import site as _site
import sys as _sys

# Do not remove this line; See https://github.com/tensorflow/tensorflow/issues/42596
from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow  # pylint: disable=unused-import
from tensorflow.python.tools import module_util as _module_util

    ```

The function parameters will be recognized as follows:

* If the parameter is also declared in the **path**, it will be used as a path parameter.
* If the parameter is of a **singular type** (like `int`, `float`, `str`, `bool`, etc) it will be interpreted as a **query** parameter.
* If the parameter is declared to be of the type of a **Pydantic model**, it will be interpreted as a request **body**.

!!! note