diff --git a/machine-learning/Dockerfile b/machine-learning/Dockerfile
index b32a564dfb..d0af8f95aa 100644
--- a/machine-learning/Dockerfile
+++ b/machine-learning/Dockerfile
@@ -1,4 +1,4 @@
-FROM python:3.11-bookworm@sha256:291405e32318285d8913b7b03293777c255fb1e89305c82aa495ac747b0049fe as builder
+FROM python:3.11-bookworm@sha256:497c00ec2cff14316a6859c4e30fc88e7ab1f11dd254fb43b8f4b201ca657596 as builder
 
 ENV PYTHONDONTWRITEBYTECODE=1 \
   PYTHONUNBUFFERED=1 \
@@ -27,7 +27,7 @@ RUN if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
   fi
 
 
-FROM python:3.11-slim-bookworm@sha256:8f64a67710f3d981cf3008d6f9f1dbe61accd7927f165f4e37ea3f8b883ccc3f
+FROM python:3.11-slim-bookworm@sha256:637774748f62b832dc11e7b286e48cd716727ed04b45a0322776c01bc526afc3
 ARG TARGETPLATFORM
 RUN apt-get update && apt-get install -y --no-install-recommends tini libmimalloc2.0 && rm -rf /var/lib/apt/lists/*
 
diff --git a/machine-learning/ann/ann.py b/machine-learning/ann/ann.py
index 8f8c5473c7..94f665bfc7 100644
--- a/machine-learning/ann/ann.py
+++ b/machine-learning/ann/ann.py
@@ -2,7 +2,7 @@ from __future__ import annotations
 
 from ctypes import CDLL, Array, c_bool, c_char_p, c_int, c_ulong, c_void_p
 from os.path import exists
-from typing import Any, Generic, Protocol, Type, TypeVar
+from typing import Any, Protocol, TypeVar
 
 import numpy as np
 from numpy.typing import NDArray
diff --git a/machine-learning/app/conftest.py b/machine-learning/app/conftest.py
index 977e110de2..da43732060 100644
--- a/machine-learning/app/conftest.py
+++ b/machine-learning/app/conftest.py
@@ -5,10 +5,10 @@ from unittest import mock
 import numpy as np
 import pytest
 from fastapi.testclient import TestClient
+from numpy.typing import NDArray
 from PIL import Image
 
 from .main import app
-from .schemas import ndarray_f32
 
 
 @pytest.fixture
@@ -17,7 +17,7 @@ def pil_image() -> Image.Image:
 
 
 @pytest.fixture
-def cv_image(pil_image: Image.Image) -> ndarray_f32:
+def cv_image(pil_image: Image.Image) -> NDArray[np.float32]:
     return np.asarray(pil_image)[:, :, ::-1]  # PIL uses RGB while cv2 uses BGR
 
 
diff --git a/machine-learning/app/main.py b/machine-learning/app/main.py
index 7631fe080b..eb3227acc0 100644
--- a/machine-learning/app/main.py
+++ b/machine-learning/app/main.py
@@ -2,11 +2,11 @@ import asyncio
 import gc
 import os
 import signal
-import sys
 import threading
 import time
 from concurrent.futures import ThreadPoolExecutor
-from typing import Any, Iterator
+from contextlib import asynccontextmanager
+from typing import Any, AsyncGenerator, Iterator
 from zipfile import BadZipFile
 
 import orjson
@@ -26,7 +26,6 @@ from .schemas import (
 )
 
 MultiPartParser.max_file_size = 2**26  # spools to disk if payload is 64 MiB or larger
-app = FastAPI()
 
 model_cache = ModelCache(ttl=settings.model_ttl, revalidate=settings.model_ttl > 0)
 thread_pool: ThreadPoolExecutor | None = None
@@ -35,8 +34,8 @@ active_requests = 0
 last_called: float | None = None
 
 
-@app.on_event("startup")
-def startup() -> None:
+@asynccontextmanager
+async def lifespan(_: FastAPI) -> AsyncGenerator[None, None]:
     global thread_pool
     log.info(
         (
@@ -44,21 +43,22 @@ def startup() -> None:
             f"{f'after {settings.model_ttl}s of inactivity' if settings.model_ttl > 0 else 'disabled'}."
         )
     )
-    # asyncio is a huge bottleneck for performance, so we use a thread pool to run blocking code
-    thread_pool = ThreadPoolExecutor(settings.request_threads) if settings.request_threads > 0 else None
-    if settings.model_ttl > 0 and settings.model_ttl_poll_s > 0:
-        asyncio.ensure_future(idle_shutdown_task())
-    log.info(f"Initialized request thread pool with {settings.request_threads} threads.")
-
-
-@app.on_event("shutdown")
-def shutdown() -> None:
-    log.handlers.clear()
-    for model in model_cache.cache._cache.values():
-        del model
-    if thread_pool is not None:
-        thread_pool.shutdown()
-    gc.collect()
+    
+    try:
+        if settings.request_threads > 0:
+            # asyncio is a huge bottleneck for performance, so we use a thread pool to run blocking code
+            thread_pool = ThreadPoolExecutor(settings.request_threads) if settings.request_threads > 0 else None
+            log.info(f"Initialized request thread pool with {settings.request_threads} threads.")
+        if settings.model_ttl > 0 and settings.model_ttl_poll_s > 0:
+            asyncio.ensure_future(idle_shutdown_task())
+        yield
+    finally:
+        log.handlers.clear()
+        for model in model_cache.cache._cache.values():
+            del model
+        if thread_pool is not None:
+            thread_pool.shutdown()
+        gc.collect()
 
 
 def update_state() -> Iterator[None]:
@@ -71,6 +71,9 @@ def update_state() -> Iterator[None]:
         active_requests -= 1
 
 
+app = FastAPI(lifespan=lifespan)
+
+
 @app.get("/", response_model=MessageResponse)
 async def root() -> dict[str, str]:
     return {"message": "Immich ML"}
diff --git a/machine-learning/app/models/ann.py b/machine-learning/app/models/ann.py
index 55781729fa..6bd3065ab9 100644
--- a/machine-learning/app/models/ann.py
+++ b/machine-learning/app/models/ann.py
@@ -3,10 +3,10 @@ from __future__ import annotations
 from pathlib import Path
 from typing import Any, NamedTuple
 
-from numpy import ascontiguousarray
+import numpy as np
+from numpy.typing import NDArray
 
 from ann.ann import Ann
-from app.schemas import ndarray_f32, ndarray_i32
 
 from ..config import log, settings
 
@@ -56,10 +56,10 @@ class AnnSession:
     def run(
         self,
         output_names: list[str] | None,
-        input_feed: dict[str, ndarray_f32] | dict[str, ndarray_i32],
+        input_feed: dict[str, NDArray[np.float32]] | dict[str, NDArray[np.int32]],
         run_options: Any = None,
-    ) -> list[ndarray_f32]:
-        inputs: list[ndarray_f32] = [ascontiguousarray(v) for v in input_feed.values()]
+    ) -> list[NDArray[np.float32]]:
+        inputs: list[NDArray[np.float32]] = [np.ascontiguousarray(v) for v in input_feed.values()]
         return self.ann.execute(self.model, inputs)
 
 
diff --git a/machine-learning/app/models/clip.py b/machine-learning/app/models/clip.py
index 61ddf9040a..f70d27f9f2 100644
--- a/machine-learning/app/models/clip.py
+++ b/machine-learning/app/models/clip.py
@@ -6,12 +6,13 @@ from pathlib import Path
 from typing import Any, Literal
 
 import numpy as np
+from numpy.typing import NDArray
 from PIL import Image
 from tokenizers import Encoding, Tokenizer
 
 from app.config import clean_name, log
 from app.models.transforms import crop, get_pil_resampling, normalize, resize, to_numpy
-from app.schemas import ModelType, ndarray_f32, ndarray_i32
+from app.schemas import ModelType
 
 from .base import InferenceModel
 
@@ -40,7 +41,7 @@ class BaseCLIPEncoder(InferenceModel):
             self.vision_model = self._make_session(self.visual_path)
             log.debug(f"Loaded clip vision model '{self.model_name}'")
 
-    def _predict(self, image_or_text: Image.Image | str) -> ndarray_f32:
+    def _predict(self, image_or_text: Image.Image | str) -> NDArray[np.float32]:
         if isinstance(image_or_text, bytes):
             image_or_text = Image.open(BytesIO(image_or_text))
 
@@ -48,7 +49,7 @@ class BaseCLIPEncoder(InferenceModel):
             case Image.Image():
                 if self.mode == "text":
                     raise TypeError("Cannot encode image as text-only model")
-                outputs: ndarray_f32 = self.vision_model.run(None, self.transform(image_or_text))[0][0]
+                outputs: NDArray[np.float32] = self.vision_model.run(None, self.transform(image_or_text))[0][0]
             case str():
                 if self.mode == "vision":
                     raise TypeError("Cannot encode text as vision-only model")
@@ -59,11 +60,11 @@ class BaseCLIPEncoder(InferenceModel):
         return outputs
 
     @abstractmethod
-    def tokenize(self, text: str) -> dict[str, ndarray_i32]:
+    def tokenize(self, text: str) -> dict[str, NDArray[np.int32]]:
         pass
 
     @abstractmethod
-    def transform(self, image: Image.Image) -> dict[str, ndarray_f32]:
+    def transform(self, image: Image.Image) -> dict[str, NDArray[np.float32]]:
         pass
 
     @property
@@ -161,11 +162,11 @@ class OpenCLIPEncoder(BaseCLIPEncoder):
         self.tokenizer.enable_truncation(max_length=context_length)
         log.debug(f"Loaded tokenizer for CLIP model '{self.model_name}'")
 
-    def tokenize(self, text: str) -> dict[str, ndarray_i32]:
+    def tokenize(self, text: str) -> dict[str, NDArray[np.int32]]:
         tokens: Encoding = self.tokenizer.encode(text)
         return {"text": np.array([tokens.ids], dtype=np.int32)}
 
-    def transform(self, image: Image.Image) -> dict[str, ndarray_f32]:
+    def transform(self, image: Image.Image) -> dict[str, NDArray[np.float32]]:
         image = resize(image, self.size)
         image = crop(image, self.size)
         image_np = to_numpy(image)
@@ -174,7 +175,7 @@ class OpenCLIPEncoder(BaseCLIPEncoder):
 
 
 class MCLIPEncoder(OpenCLIPEncoder):
-    def tokenize(self, text: str) -> dict[str, ndarray_i32]:
+    def tokenize(self, text: str) -> dict[str, NDArray[np.int32]]:
         tokens: Encoding = self.tokenizer.encode(text)
         return {
             "input_ids": np.array([tokens.ids], dtype=np.int32),
diff --git a/machine-learning/app/models/facial_recognition.py b/machine-learning/app/models/facial_recognition.py
index e64de9f65c..6b66e57c47 100644
--- a/machine-learning/app/models/facial_recognition.py
+++ b/machine-learning/app/models/facial_recognition.py
@@ -5,9 +5,10 @@ import cv2
 import numpy as np
 from insightface.model_zoo import ArcFaceONNX, RetinaFace
 from insightface.utils.face_align import norm_crop
+from numpy.typing import NDArray
 
 from app.config import clean_name
-from app.schemas import BoundingBox, Face, ModelType, ndarray_f32
+from app.schemas import Face, ModelType, is_ndarray
 
 from .base import InferenceModel
 
@@ -36,22 +37,25 @@ class FaceRecognizer(InferenceModel):
         )
         self.rec_model.prepare(ctx_id=0)
 
-    def _predict(self, image: ndarray_f32 | bytes) -> list[Face]:
+    def _predict(self, image: NDArray[np.uint8] | bytes) -> list[Face]:
         if isinstance(image, bytes):
-            image = cv2.imdecode(np.frombuffer(image, np.uint8), cv2.IMREAD_COLOR)
-        bboxes, kpss = self.det_model.detect(image)
+            decoded_image = cv2.imdecode(np.frombuffer(image, np.uint8), cv2.IMREAD_COLOR)
+        else:
+            decoded_image = image
+        assert is_ndarray(decoded_image, np.uint8)
+        bboxes, kpss = self.det_model.detect(decoded_image)
         if bboxes.size == 0:
             return []
-        assert isinstance(image, np.ndarray) and isinstance(kpss, np.ndarray)
+        assert is_ndarray(kpss, np.float32)
 
         scores = bboxes[:, 4].tolist()
         bboxes = bboxes[:, :4].round().tolist()
 
         results = []
-        height, width, _ = image.shape
+        height, width, _ = decoded_image.shape
         for (x1, y1, x2, y2), score, kps in zip(bboxes, scores, kpss):
-            cropped_img = norm_crop(image, kps)
-            embedding: ndarray_f32 = self.rec_model.get_feat(cropped_img)[0]
+            cropped_img = norm_crop(decoded_image, kps)
+            embedding: NDArray[np.float32] = self.rec_model.get_feat(cropped_img)[0]
             face: Face = {
                 "imageWidth": width,
                 "imageHeight": height,
diff --git a/machine-learning/app/models/transforms.py b/machine-learning/app/models/transforms.py
index 93b98d0eff..a41b7cd3d8 100644
--- a/machine-learning/app/models/transforms.py
+++ b/machine-learning/app/models/transforms.py
@@ -1,8 +1,7 @@
 import numpy as np
+from numpy.typing import NDArray
 from PIL import Image
 
-from app.schemas import ndarray_f32
-
 _PIL_RESAMPLING_METHODS = {resampling.name.lower(): resampling for resampling in Image.Resampling}
 
 
@@ -23,11 +22,13 @@ def crop(img: Image.Image, size: int) -> Image.Image:
     return img.crop((left, upper, right, lower))
 
 
-def to_numpy(img: Image.Image) -> ndarray_f32:
+def to_numpy(img: Image.Image) -> NDArray[np.float32]:
     return np.asarray(img.convert("RGB")).astype(np.float32) / 255.0
 
 
-def normalize(img: ndarray_f32, mean: float | ndarray_f32, std: float | ndarray_f32) -> ndarray_f32:
+def normalize(
+    img: NDArray[np.float32], mean: float | NDArray[np.float32], std: float | NDArray[np.float32]
+) -> NDArray[np.float32]:
     return (img - mean) / std
 
 
diff --git a/machine-learning/app/schemas.py b/machine-learning/app/schemas.py
index 839287924b..53f66446b8 100644
--- a/machine-learning/app/schemas.py
+++ b/machine-learning/app/schemas.py
@@ -1,13 +1,10 @@
 from enum import StrEnum
-from typing import Any, Protocol, TypeAlias, TypedDict, TypeGuard
+from typing import Any, Protocol, TypedDict, TypeGuard
 
 import numpy as np
+import numpy.typing as npt
 from pydantic import BaseModel
 
-ndarray_f32: TypeAlias = np.ndarray[int, np.dtype[np.float32]]
-ndarray_i64: TypeAlias = np.ndarray[int, np.dtype[np.int64]]
-ndarray_i32: TypeAlias = np.ndarray[int, np.dtype[np.int32]]
-
 
 class TextResponse(BaseModel):
     __root__: str
@@ -35,7 +32,7 @@ class HasProfiling(Protocol):
 
 class Face(TypedDict):
     boundingBox: BoundingBox
-    embedding: ndarray_f32
+    embedding: npt.NDArray[np.float32]
     imageWidth: int
     imageHeight: int
     score: float
@@ -43,3 +40,7 @@ class Face(TypedDict):
 
 def has_profiling(obj: Any) -> TypeGuard[HasProfiling]:
     return hasattr(obj, "profiling") and isinstance(obj.profiling, dict)
+
+
+def is_ndarray(obj: Any, dtype: "type[np._DTypeScalar_co]") -> "TypeGuard[npt.NDArray[np._DTypeScalar_co]]":
+    return isinstance(obj, np.ndarray) and obj.dtype == dtype
diff --git a/machine-learning/export/Dockerfile b/machine-learning/export/Dockerfile
index 09d639ada1..2acb73d324 100644
--- a/machine-learning/export/Dockerfile
+++ b/machine-learning/export/Dockerfile
@@ -1,4 +1,4 @@
-FROM mambaorg/micromamba:bookworm-slim@sha256:a7033851ca71454ed8cfbc958fdd62a72e6fa90f71ccb3aa9b15e67a98f66a1e as builder
+FROM mambaorg/micromamba:bookworm-slim@sha256:e2586bd4097636856cbf725304ce240c968bea4123bece888d9e02ca2572ea2d as builder
 
 ENV NODE_ENV=production \
   TRANSFORMERS_CACHE=/cache \
diff --git a/machine-learning/poetry.lock b/machine-learning/poetry.lock
index 53fa1b52fd..8eb5fb67a6 100644
--- a/machine-learning/poetry.lock
+++ b/machine-learning/poetry.lock
@@ -63,33 +63,33 @@ trio = ["trio (<0.22)"]
 
 [[package]]
 name = "black"
-version = "23.12.0"
+version = "23.12.1"
 description = "The uncompromising code formatter."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -672,19 +672,18 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "fastapi"
-version = "0.106.0"
+version = "0.109.0"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 optional = false
 python-versions = ">=3.8"
 files = [
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-starlette = ">=0.27.0,<0.28.0"
+starlette = ">=0.35.0,<0.36.0"
 typing-extensions = ">=4.8.0"
 
 [package.extras]
@@ -1265,13 +1264,13 @@ socks = ["socksio (==1.*)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.20.1"
+version = "0.20.2"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1557,13 +1556,13 @@ test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"]
 
 [[package]]
 name = "locust"
-version = "2.20.0"
+version = "2.20.1"
 description = "Developer friendly load testing framework"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1577,7 +1576,7 @@ msgpack = ">=1.0.0"
 psutil = ">=5.9.1"
 pywin32 = {version = "*", markers = "platform_system == \"Windows\""}
 pyzmq = ">=25.0.0"
-requests = ">=2.23.0"
+requests = ">=2.26.0"
 roundrobin = ">=0.0.2"
 Werkzeug = ">=2.0.0"
 
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 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -2020,18 +2019,18 @@ sympy = "*"
 
 [[package]]
 name = "opencv-python-headless"
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+version = "4.9.0.80"
 description = "Wrapper package for OpenCV python bindings."
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
 [package.extras]
 docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
+fpx = ["olefile"]
+mic = ["olefile"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
+typing = ["typing-extensions"]
+xmp = ["defusedxml"]
 
 [[package]]
 name = "platformdirs"
@@ -2389,13 +2406,13 @@ files = [
 
 [[package]]
 name = "pytest"
-version = "7.4.3"
+version = "7.4.4"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "pytest-7.4.3-py3-none-any.whl", hash = "sha256:0d009c083ea859a71b76adf7c1d502e4bc170b80a8ef002da5806527b9591fac"},
-    {file = "pytest-7.4.3.tar.gz", hash = "sha256:d989d136982de4e3b29dabcc838ad581c64e8ed52c11fbe86ddebd9da0818cd5"},
+    {file = "pytest-7.4.4-py3-none-any.whl", hash = "sha256:b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8"},
+    {file = "pytest-7.4.4.tar.gz", hash = "sha256:2cf0005922c6ace4a3e2ec8b4080eb0d9753fdc93107415332f50ce9e7994280"},
 ]
 
 [package.dependencies]
@@ -2411,13 +2428,13 @@ testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "no
 
 [[package]]
 name = "pytest-asyncio"
-version = "0.23.2"
+version = "0.23.3"
 description = "Pytest support for asyncio"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pytest-asyncio-0.23.2.tar.gz", hash = "sha256:c16052382554c7b22d48782ab3438d5b10f8cf7a4bdcae7f0f67f097d95beecc"},
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+    {file = "pytest-asyncio-0.23.3.tar.gz", hash = "sha256:af313ce900a62fbe2b1aed18e37ad757f1ef9940c6b6a88e2954de38d6b1fb9f"},
+    {file = "pytest_asyncio-0.23.3-py3-none-any.whl", hash = "sha256:37a9d912e8338ee7b4a3e917381d1c95bfc8682048cb0fbc35baba316ec1faba"},
 ]
 
 [package.dependencies]
@@ -2759,28 +2776,28 @@ files = [
 
 [[package]]
 name = "ruff"
-version = "0.1.9"
+version = "0.1.11"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "ruff-0.1.11-py3-none-win_amd64.whl", hash = "sha256:eb85ee287b11f901037a6683b2374bb0ec82928c5cbc984f575d0437979c521a"},
+    {file = "ruff-0.1.11-py3-none-win_arm64.whl", hash = "sha256:97ce4d752f964ba559c7023a86e5f8e97f026d511e48013987623915431c7ea9"},
+    {file = "ruff-0.1.11.tar.gz", hash = "sha256:f9d4d88cb6eeb4dfe20f9f0519bd2eaba8119bde87c3d5065c541dbae2b5a2cb"},
 ]
 
 [[package]]
@@ -2960,13 +2977,13 @@ files = [
 
 [[package]]
 name = "starlette"
-version = "0.27.0"
+version = "0.35.1"
 description = "The little ASGI library that shines."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "starlette-0.27.0-py3-none-any.whl", hash = "sha256:918416370e846586541235ccd38a474c08b80443ed31c578a418e2209b3eef91"},
-    {file = "starlette-0.27.0.tar.gz", hash = "sha256:6a6b0d042acb8d469a01eba54e9cda6cbd24ac602c4cd016723117d6a7e73b75"},
+    {file = "starlette-0.35.1-py3-none-any.whl", hash = "sha256:50bbbda9baa098e361f398fda0928062abbaf1f54f4fadcbe17c092a01eb9a25"},
+    {file = "starlette-0.35.1.tar.gz", hash = "sha256:3e2639dac3520e4f58734ed22553f950d3f3cb1001cd2eaac4d57e8cdc5f66bc"},
 ]
 
 [package.dependencies]
diff --git a/machine-learning/pyproject.toml b/machine-learning/pyproject.toml
index 9143794cd9..c7dd3f6c7b 100644
--- a/machine-learning/pyproject.toml
+++ b/machine-learning/pyproject.toml
@@ -60,7 +60,6 @@ warn_untyped_fields = true
 line-length = 120
 target-version = "py311"
 select = ["E", "F", "I"]
-ignore = ["F401"]
 
 [tool.ruff.per-file-ignores]
 "test_main.py" = ["F403"]