diff --git a/.github/workflows/python.yml b/.github/workflows/python.yml
index d3b9360d..47342f87 100644
--- a/.github/workflows/python.yml
+++ b/.github/workflows/python.yml
@@ -21,7 +21,7 @@ jobs:
     - name: Install Poetry
       uses: snok/install-poetry@v1
       with:
-        virtualenv-create: true
+        virtualenvs-create: true
         virtualenvs-in-project: true
     - name: Poetry cache
       id: poetry_cache
diff --git a/facenet_pytorch/models/inception_resnet_v1.py b/facenet_pytorch/models/inception_resnet_v1.py
index 35c17232..db13c9c1 100644
--- a/facenet_pytorch/models/inception_resnet_v1.py
+++ b/facenet_pytorch/models/inception_resnet_v1.py
@@ -1,14 +1,23 @@
+from __future__ import annotations
+
 import os
+from pathlib import Path
+from typing import TYPE_CHECKING
 
 import torch
 from torch import nn
-from torch.nn import functional as F
+from torch.nn import functional as tv_functional
 
 from .utils.download import download_url_to_file
 
+if TYPE_CHECKING:
+    from torch.nn.common_types import _size_2_t
+
 
 class BasicConv2d(nn.Module):
-    def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
+    def __init__(  # noqa: PLR0913
+        self, in_planes: int, out_planes: int, kernel_size: _size_2_t, stride: _size_2_t, padding: _size_2_t = 0
+    ) -> None:
         super().__init__()
         self.conv = nn.Conv2d(
             in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, bias=False
@@ -21,15 +30,15 @@ def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
         )
         self.relu = nn.ReLU(inplace=False)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         x = self.conv(x)
         x = self.bn(x)
         x = self.relu(x)
-        return x
+        return x  # noqa: RET504
 
 
 class Block35(nn.Module):
-    def __init__(self, scale=1.0):
+    def __init__(self, scale: float = 1.0) -> None:
         super().__init__()
 
         self.scale = scale
@@ -49,19 +58,18 @@ def __init__(self, scale=1.0):
         self.conv2d = nn.Conv2d(96, 256, kernel_size=1, stride=1)
         self.relu = nn.ReLU(inplace=False)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         x0 = self.branch0(x)
         x1 = self.branch1(x)
         x2 = self.branch2(x)
         out = torch.cat((x0, x1, x2), 1)
         out = self.conv2d(out)
         out = out * self.scale + x
-        out = self.relu(out)
-        return out
+        return self.relu(out)
 
 
 class Block17(nn.Module):
-    def __init__(self, scale=1.0):
+    def __init__(self, scale: float = 1.0) -> None:
         super().__init__()
 
         self.scale = scale
@@ -77,18 +85,17 @@ def __init__(self, scale=1.0):
         self.conv2d = nn.Conv2d(256, 896, kernel_size=1, stride=1)
         self.relu = nn.ReLU(inplace=False)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         x0 = self.branch0(x)
         x1 = self.branch1(x)
         out = torch.cat((x0, x1), 1)
         out = self.conv2d(out)
         out = out * self.scale + x
-        out = self.relu(out)
-        return out
+        return self.relu(out)
 
 
 class Block8(nn.Module):
-    def __init__(self, scale=1.0, noReLU=False):
+    def __init__(self, scale: float = 1.0, *, noReLU: bool = False) -> None:  # noqa: N803
         super().__init__()
 
         self.scale = scale
@@ -106,7 +113,7 @@ def __init__(self, scale=1.0, noReLU=False):
         if not self.noReLU:
             self.relu = nn.ReLU(inplace=False)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         x0 = self.branch0(x)
         x1 = self.branch1(x)
         out = torch.cat((x0, x1), 1)
@@ -117,8 +124,8 @@ def forward(self, x):
         return out
 
 
-class Mixed_6a(nn.Module):
-    def __init__(self):
+class Mixed6a(nn.Module):
+    def __init__(self) -> None:
         super().__init__()
 
         self.branch0 = BasicConv2d(256, 384, kernel_size=3, stride=2)
@@ -131,16 +138,15 @@ def __init__(self):
 
         self.branch2 = nn.MaxPool2d(3, stride=2)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         x0 = self.branch0(x)
         x1 = self.branch1(x)
         x2 = self.branch2(x)
-        out = torch.cat((x0, x1, x2), 1)
-        return out
+        return torch.cat((x0, x1, x2), 1)
 
 
-class Mixed_7a(nn.Module):
-    def __init__(self):
+class Mixed7a(nn.Module):
+    def __init__(self) -> None:
         super().__init__()
 
         self.branch0 = nn.Sequential(
@@ -159,13 +165,12 @@ def __init__(self):
 
         self.branch3 = nn.MaxPool2d(3, stride=2)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         x0 = self.branch0(x)
         x1 = self.branch1(x)
         x2 = self.branch2(x)
         x3 = self.branch3(x)
-        out = torch.cat((x0, x1, x2, x3), 1)
-        return out
+        return torch.cat((x0, x1, x2, x3), 1)
 
 
 class InceptionResnetV1(nn.Module):
@@ -187,7 +192,15 @@ class InceptionResnetV1(nn.Module):
         dropout_prob {float} -- Dropout probability. (default: {0.6})
     """
 
-    def __init__(self, pretrained=None, classify=False, num_classes=None, dropout_prob=0.6, device=None):
+    def __init__(  # noqa: PLR0913
+        self,
+        pretrained: str | None = None,
+        *,
+        classify: bool = False,
+        num_classes: int | None = None,
+        dropout_prob: float = 0.6,
+        device: torch.device | str | None = None,
+    ) -> None:
         super().__init__()
 
         # Set simple attributes
@@ -200,7 +213,8 @@ def __init__(self, pretrained=None, classify=False, num_classes=None, dropout_pr
         elif pretrained == "casia-webface":
             tmp_classes = 10575
         elif pretrained is None and self.classify and self.num_classes is None:
-            raise Exception('If "pretrained" is not specified and "classify" is True, "num_classes" must be specified')
+            msg = "If 'pretrained' is not specified and 'classify' is True, 'num_classes' must be specified"
+            raise ValueError(msg)
 
         # Define layers
         self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2)
@@ -213,7 +227,7 @@ def __init__(self, pretrained=None, classify=False, num_classes=None, dropout_pr
         self.repeat_1 = nn.Sequential(
             Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17)
         )
-        self.mixed_6a = Mixed_6a()
+        self.mixed_6a = Mixed6a()
         self.repeat_2 = nn.Sequential(
             Block17(scale=0.10),
             Block17(scale=0.10),
@@ -226,7 +240,7 @@ def __init__(self, pretrained=None, classify=False, num_classes=None, dropout_pr
             Block17(scale=0.10),
             Block17(scale=0.10),
         )
-        self.mixed_7a = Mixed_7a()
+        self.mixed_7a = Mixed7a()
         self.repeat_3 = nn.Sequential(
             Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20)
         )
@@ -248,7 +262,7 @@ def __init__(self, pretrained=None, classify=False, num_classes=None, dropout_pr
             self.device = device
             self.to(device)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         """Calculate embeddings or logits given a batch of input image tensors.
 
         Arguments:
@@ -274,14 +288,11 @@ def forward(self, x):
         x = self.dropout(x)
         x = self.last_linear(x.view(x.shape[0], -1))
         x = self.last_bn(x)
-        if self.classify:
-            x = self.logits(x)
-        else:
-            x = F.normalize(x, p=2, dim=1)
+        x = self.logits(x) if self.classify else tv_functional.normalize(x, p=2, dim=1)
         return x
 
 
-def load_weights(mdl, name):
+def load_weights(mdl: torch.nn.Module, name: str) -> None:
     """Download pretrained state_dict and load into model.
 
     Arguments:
@@ -293,24 +304,25 @@ def load_weights(mdl, name):
     """
     if name == "vggface2":
         path = "https://github.com/timesler/facenet-pytorch/releases/download/v2.2.9/20180402-114759-vggface2.pt"
+        hash_prefix = "281cebca8662831adb987a874bdcb36e73f5b1c6dc5ee5878f305e985625d99b"
     elif name == "casia-webface":
         path = "https://github.com/timesler/facenet-pytorch/releases/download/v2.2.9/20180408-102900-casia-webface.pt"
+        hash_prefix = "7a67afdbbc995fce5e10128675e318799a70698c2f433ba75dd7eb9a2f096e7d"
     else:
-        raise ValueError('Pretrained models only exist for "vggface2" and "casia-webface"')
+        msg = "Pretrained models only exist for 'vggface2' and 'casia-webface'"
+        raise ValueError(msg)
 
-    model_dir = os.path.join(get_torch_home(), "checkpoints")
-    os.makedirs(model_dir, exist_ok=True)
+    model_dir = get_torch_home() / "checkpoints"
+    model_dir.mkdir(parents=True, exist_ok=True)
 
-    cached_file = os.path.join(model_dir, os.path.basename(path))
-    if not os.path.exists(cached_file):
-        download_url_to_file(path, cached_file)
+    cached_file = model_dir / Path(path).name
+    if not cached_file.exists():
+        download_url_to_file(path, cached_file, hash_prefix)
 
     state_dict = torch.load(cached_file)
     mdl.load_state_dict(state_dict)
 
 
-def get_torch_home():
-    torch_home = os.path.expanduser(
-        os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
-    )
-    return torch_home
+def get_torch_home() -> Path:
+    """Get the torch cache directory."""
+    return Path(os.getenv("TORCH_HOME", Path(os.getenv("XDG_CACHE_HOME", "~/.cache")) / "torch")).expanduser()
diff --git a/facenet_pytorch/models/mtcnn.py b/facenet_pytorch/models/mtcnn.py
index 1c9eae45..7598cb1d 100644
--- a/facenet_pytorch/models/mtcnn.py
+++ b/facenet_pytorch/models/mtcnn.py
@@ -2,15 +2,18 @@
 
 import importlib.resources
 import os
+from typing import TYPE_CHECKING
 
 import numpy as np
 import torch
-from PIL.Image import Image
 from torch import nn
 
 import facenet_pytorch.data
 from facenet_pytorch.models.utils.detect_face import detect_face, extract_face, get_size
 
+if TYPE_CHECKING:
+    from PIL.Image import Image
+
 
 class PNet(nn.Module):
     """MTCNN PNet.
diff --git a/facenet_pytorch/models/utils/download.py b/facenet_pytorch/models/utils/download.py
index 6f30331d..418ebba9 100644
--- a/facenet_pytorch/models/utils/download.py
+++ b/facenet_pytorch/models/utils/download.py
@@ -1,18 +1,22 @@
 from __future__ import annotations
 
 import hashlib
-import os
 import shutil
 import tempfile
+from pathlib import Path
+from typing import TYPE_CHECKING
 from urllib.request import Request, urlopen
 
+if TYPE_CHECKING:
+    import _hashlib
+
 try:
     from tqdm.auto import tqdm  # automatically select proper tqdm submodule if available
 except ImportError:
     from tqdm import tqdm
 
 
-def download_url_to_file(url: str, dst: PathLike | str, hash_prefix=None, progress: bool = True) -> None:
+def download_url_to_file(url: str, dst: Path | str, hash_prefix: str | None = None, *, progress: bool = True) -> None:
     """Download object at the given URL to a local path.
 
     Args:
@@ -20,51 +24,54 @@ def download_url_to_file(url: str, dst: PathLike | str, hash_prefix=None, progre
         dst (string): Full path where object will be saved, e.g. `/tmp/temporary_file`
         hash_prefix (string, optional): If not None, the SHA256 downloaded file should start with `hash_prefix`.
             Default: None
-        progress (bool, optional): whether or not to display a progress bar to stderr
+        progress (bool, optional): whether to display a progress bar to stderr
             Default: True
     Example:
-        >>> torch.hub.download_url_to_file('https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth', '/tmp/temporary_file')
+        >>> torch.hub.download_url_to_file("https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", "/tmp/temporary_file")
     """
     file_size = None
     # We use a different API for python2 since urllib(2) doesn't recognize the CA
     # certificates in older Python
-    req = Request(url, headers={"User-Agent": "torch.hub"})
-    u = urlopen(req)
-    meta = u.info()
-    if hasattr(meta, "getheaders"):
-        content_length = meta.getheaders("Content-Length")
-    else:
-        content_length = meta.get_all("Content-Length")
-    if content_length is not None and len(content_length) > 0:
-        file_size = int(content_length[0])
+    req = Request(url, headers={"User-Agent": "torch.hub"})  # noqa: S310
+    with urlopen(req) as response:  # noqa: S310
+        meta = response.info()
+        if hasattr(meta, "getheaders"):
+            content_length = meta.getheaders("Content-Length")
+        else:
+            content_length = meta.get_all("Content-Length")
+        if content_length is not None and len(content_length) > 0:
+            file_size = int(content_length[0])
 
-    # We deliberately save it in a temp file and move it after
-    # download is complete. This prevents a local working checkpoint
-    # being overridden by a broken download.
-    dst = os.path.expanduser(dst)
-    dst_dir = os.path.dirname(dst)
-    f = tempfile.NamedTemporaryFile(delete=False, dir=dst_dir)
+        # We deliberately save it in a temp file and move it after
+        # download is complete. This prevents a local working checkpoint
+        # being overridden by a broken download.
+        dst = dst.expanduser()
+        dst_dir = dst.parent
+        f = tempfile.NamedTemporaryFile(delete=False, dir=dst_dir)
 
-    try:
+        sha256: _hashlib.HASH | None = None
         if hash_prefix is not None:
             sha256 = hashlib.sha256()
-        with tqdm(total=file_size, disable=not progress, unit="B", unit_scale=True, unit_divisor=1024) as pbar:
-            while True:
-                buffer = u.read(8192)
-                if len(buffer) == 0:
-                    break
-                f.write(buffer)
-                if hash_prefix is not None:
-                    sha256.update(buffer)
-                pbar.update(len(buffer))
 
-        f.close()
-        if hash_prefix is not None:
-            digest = sha256.hexdigest()
-            if digest[: len(hash_prefix)] != hash_prefix:
-                raise RuntimeError(f'invalid hash value (expected "{hash_prefix}", got "{digest}")')
-        shutil.move(f.name, dst)
-    finally:
-        f.close()
-        if os.path.exists(f.name):
-            os.remove(f.name)
+        try:
+            with tqdm(total=file_size, disable=not progress, unit="B", unit_scale=True, unit_divisor=1024) as pbar:
+                while True:
+                    buffer = response.read(8192)
+                    if len(buffer) == 0:
+                        break
+                    f.write(buffer)
+                    if sha256 is not None:
+                        sha256.update(buffer)
+                    pbar.update(len(buffer))
+
+            f.close()
+            if sha256 is not None:
+                digest = sha256.hexdigest()
+                if digest[: len(hash_prefix)] != hash_prefix:
+                    msg = f"invalid hash value (expected '{hash_prefix}', got '{digest}')"
+                    raise RuntimeError(msg)
+            shutil.move(f.name, dst)
+        finally:
+            f.close()
+            if Path(f.name).exists():
+                Path(f.name).unlink()
diff --git a/facenet_pytorch/models/utils/tensorflow2pytorch.py b/facenet_pytorch/models/utils/tensorflow2pytorch.py
index 51aa4196..c8bf5eeb 100644
--- a/facenet_pytorch/models/utils/tensorflow2pytorch.py
+++ b/facenet_pytorch/models/utils/tensorflow2pytorch.py
@@ -31,8 +31,8 @@ def import_tf_params(tf_mdl_dir, sess):
     tf_layers = tf.trainable_variables()
     tf_params = sess.run(tf_layers)
 
-    tf_shapes = [p.shape for p in tf_params]
-    tf_layers = [l.name for l in tf_layers]
+    tf_shapes = [param.shape for param in tf_params]
+    tf_layers = [layer.name for layer in tf_layers]
 
     if not callable(tf_mdl_dir):
         path = os.path.join(tf_mdl_dir, "layer_description.json")
@@ -232,11 +232,11 @@ def compare_model_outputs(pt_mdl, sess, test_data):
 
 
 def compare_mtcnn(pt_mdl, tf_fun, sess, ind, test_data):
-    tf_mdls = tf_fun(sess)
-    tf_mdl = tf_mdls[ind]
+    tf_modules = tf_fun(sess)
+    tf_module = tf_modules[ind]
 
     print("\nPassing test data through TF model\n")
-    tf_output = tf_mdl(test_data.numpy())
+    tf_output = tf_module(test_data.numpy())
     tf_output = [torch.tensor(out) for out in tf_output]
     print("\n".join([str(o.view(-1)[:10]) for o in tf_output]))
 
@@ -368,7 +368,7 @@ def tensorflow2pytorch():
     }
 
     print("\nLoad PNet weights and save\n")
-    tf_mdl_dir = lambda sess: detect_face.create_mtcnn(sess, None)
+    tf_mdl_dir = lambda session: detect_face.create_mtcnn(session, None)
     mdl = PNet()
     data_name = "pnet"
     load_tf_model_weights(mdl, lookup_pnet, tf_mdl_dir, is_resnet=False, arg_num=0)
diff --git a/facenet_pytorch/models/utils/training.py b/facenet_pytorch/models/utils/training.py
index 90090c81..a0761f2f 100644
--- a/facenet_pytorch/models/utils/training.py
+++ b/facenet_pytorch/models/utils/training.py
@@ -1,7 +1,14 @@
+from __future__ import annotations
+
 import time
+from typing import TYPE_CHECKING, Callable
 
 import torch
 
+if TYPE_CHECKING:
+    import torch.utils.data
+    import torch.utils.tensorboard
+
 
 class Logger:
     def __init__(self, mode, length, calculate_mean=False):
@@ -11,7 +18,7 @@ def __init__(self, mode, length, calculate_mean=False):
         if self.calculate_mean:
             self.fn = lambda x, i: x / (i + 1)
         else:
-            self.fn = lambda x, i: x
+            self.fn = lambda x, _: x
 
     def __call__(self, loss, metrics, i):
         track_str = f"\r{self.mode} | {i + 1:5d}/{self.length:<5d}| "
@@ -24,6 +31,7 @@ def __call__(self, loss, metrics, i):
 
 class BatchTimer:
     """Batch timing class.
+
     Use this class for tracking training and testing time/rate per batch or per sample.
 
     Keyword Arguments:
@@ -33,13 +41,13 @@ class BatchTimer:
             (default: {True})
     """
 
-    def __init__(self, rate=True, per_sample=True):
+    def __init__(self, *, rate: bool = True, per_sample: bool = True):
         self.start = time.time()
         self.end = None
-        self.rate = rate
-        self.per_sample = per_sample
+        self.rate: bool = rate
+        self.per_sample: bool = per_sample
 
-    def __call__(self, y_pred, y):
+    def __call__(self, y_pred, y) -> torch.Tensor:
         self.end = time.time()
         elapsed = self.end - self.start
         self.start = self.end
@@ -59,38 +67,41 @@ def accuracy(logits, y):
 
 
 def pass_epoch(
-    model,
-    loss_fn,
-    loader,
-    optimizer=None,
-    scheduler=None,
-    batch_metrics={"time": BatchTimer()},
-    show_running=True,
-    device="cpu",
-    writer=None,
-):
+    model: torch.nn.Module,
+    loss_fn: Callable,
+    loader: torch.utils.data.DataLoader,
+    optimizer: torch.optim.Optimizer | None = None,
+    scheduler: torch.optim.lr_scheduler.LRScheduler = None,
+    batch_metrics: dict | None = None,
+    writer: torch.utils.tensorboard.SummaryWriter = None,
+    device: torch.device | str = "cpu",
+    *,
+    show_running: bool = True,
+) -> tuple[torch.Tensor, dict]:
     """Train or evaluate over a data epoch.
 
     Arguments:
         model {torch.nn.Module} -- Pytorch model.
         loss_fn {callable} -- A function to compute (scalar) loss.
         loader {torch.utils.data.DataLoader} -- A pytorch data loader.
-
-    Keyword Arguments:
         optimizer {torch.optim.Optimizer} -- A pytorch optimizer.
         scheduler {torch.optim.lr_scheduler._LRScheduler} -- LR scheduler (default: {None})
         batch_metrics {dict} -- Dictionary of metric functions to call on each batch. The default
             is a simple timer. A progressive average of these metrics, along with the average
             loss, is printed every batch. (default: {{'time': iter_timer()}})
+        writer {torch.utils.tensorboard.SummaryWriter} -- Tensorboard SummaryWriter. (default: {None})
+
+    Keyword Arguments:
+        device {str or torch.device} -- Device for pytorch to use. (default: {'cpu'})
         show_running {bool} -- Whether or not to print losses and metrics for the current batch
             or rolling averages. (default: {False})
-        device {str or torch.device} -- Device for pytorch to use. (default: {'cpu'})
-        writer {torch.utils.tensorboard.SummaryWriter} -- Tensorboard SummaryWriter. (default: {None})
 
     Returns:
         tuple(torch.Tensor, dict) -- A tuple of the average loss and a dictionary of average
             metric values across the epoch.
     """
+    if batch_metrics is None:
+        batch_metrics = {"time": BatchTimer()}
     mode = "Train" if model.training else "Valid"
     logger = Logger(mode, length=len(loader), calculate_mean=show_running)
     loss = 0
diff --git a/poetry.lock b/poetry.lock
index b72466a0..b287cdd8 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -574,63 +574,63 @@ test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
 
 [[package]]
 name = "coverage"
-version = "7.5.3"
+version = "7.5.4"
 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
@@ -759,18 +759,18 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 
 [[package]]
 name = "filelock"
-version = "3.15.1"
+version = "3.15.4"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.extras]
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+testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-asyncio (>=0.21)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)", "virtualenv (>=20.26.2)"]
 typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
@@ -851,13 +851,13 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2024.6.0"
+version = "2024.6.1"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -1070,22 +1070,22 @@ files = [
 
 [[package]]
 name = "importlib-metadata"
-version = "7.1.0"
+version = "8.0.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 zipp = ">=0.5"
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 perf = ["ipython"]
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+test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
 
 [[package]]
 name = "importlib-resources"
@@ -1525,13 +1525,13 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.2.2"
+version = "4.2.3"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2447,7 +2447,6 @@ optional = false
 python-versions = ">=3.6"
 files = [
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@@ -2460,10 +2459,10 @@ numpy = [
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 [[package]]
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 pytz = ">=2020.1"
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 description = "A Language Server Protocol implementation for Ruff."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "ruff_lsp-0.0.53-py3-none-any.whl", hash = "sha256:5ea39968510d046b3c62dc5a7e3b52e867c2de14af34a406883fe05d55bab2b0"},
-    {file = "ruff_lsp-0.0.53.tar.gz", hash = "sha256:de38eccd06020350630ac3518fe04a9640c8f66908758d8a623b5ea021bf84b0"},
+    {file = "ruff_lsp-0.0.54-py3-none-any.whl", hash = "sha256:1cc7d2f1cb69cbea1dfeba0f2d7dd5832bc68b0b052c7166530bcce63aa75f57"},
+    {file = "ruff_lsp-0.0.54.tar.gz", hash = "sha256:33e1d4dd20ca481fc6a811afcfdd451798c22fc39f2104df23c2855e322a0582"},
 ]
 
 [package.dependencies]
@@ -3549,7 +3549,7 @@ ruff = ">=0.0.274"
 typing-extensions = "*"
 
 [package.extras]
-dev = ["mypy (==1.4.1)", "pip-tools (>=6.13.0,<7.0.0)", "pytest (>=7.3.1,<8.0.0)", "pytest-asyncio (==0.21.1)", "python-lsp-jsonrpc (==1.0.0)"]
+dev = ["mypy (==1.4.1)", "pip-tools (>=6.13.0,<7.0.0)", "pytest (>=7.3.1,<8.0.0)", "pytest-asyncio (==0.21.2)", "python-lsp-jsonrpc (==1.0.0)"]
 
 [[package]]
 name = "scikit-learn"
@@ -3741,18 +3741,18 @@ win32 = ["pywin32"]
 
 [[package]]
 name = "setuptools"
-version = "70.0.0"
+version = "70.1.1"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "setuptools-70.0.0-py3-none-any.whl", hash = "sha256:54faa7f2e8d2d11bcd2c07bed282eef1046b5c080d1c32add737d7b5817b1ad4"},
-    {file = "setuptools-70.0.0.tar.gz", hash = "sha256:f211a66637b8fa059bb28183da127d4e86396c991a942b028c6650d4319c3fd0"},
+    {file = "setuptools-70.1.1-py3-none-any.whl", hash = "sha256:a58a8fde0541dab0419750bcc521fbdf8585f6e5cb41909df3a472ef7b81ca95"},
+    {file = "setuptools-70.1.1.tar.gz", hash = "sha256:937a48c7cdb7a21eb53cd7f9b59e525503aa8abaf3584c730dc5f7a5bec3a650"},
 ]
 
 [package.extras]
 docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
-testing = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+testing = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "mypy (==1.10.0)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.3.2)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
 
 [[package]]
 name = "six"
@@ -3822,15 +3822,15 @@ mpmath = ">=1.1.0,<1.4.0"
 
 [[package]]
 name = "tbb"
-version = "2021.12.0"
+version = "2021.13.0"
 description = "Intel® oneAPI Threading Building Blocks (oneTBB)"
 optional = false
 python-versions = "*"
 files = [
-    {file = "tbb-2021.12.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:f2cc9a7f8ababaa506cbff796ce97c3bf91062ba521e15054394f773375d81d8"},
-    {file = "tbb-2021.12.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:a925e9a7c77d3a46ae31c34b0bb7f801c4118e857d137b68f68a8e458fcf2bd7"},
-    {file = "tbb-2021.12.0-py3-none-win32.whl", hash = "sha256:b1725b30c174048edc8be70bd43bb95473f396ce895d91151a474d0fa9f450a8"},
-    {file = "tbb-2021.12.0-py3-none-win_amd64.whl", hash = "sha256:fc2772d850229f2f3df85f1109c4844c495a2db7433d38200959ee9265b34789"},
+    {file = "tbb-2021.13.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:a2567725329639519d46d92a2634cf61e76601dac2f777a05686fea546c4fe4f"},
+    {file = "tbb-2021.13.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:aaf667e92849adb012b8874d6393282afc318aca4407fc62f912ee30a22da46a"},
+    {file = "tbb-2021.13.0-py3-none-win32.whl", hash = "sha256:6669d26703e9943f6164c6407bd4a237a45007e79b8d3832fe6999576eaaa9ef"},
+    {file = "tbb-2021.13.0-py3-none-win_amd64.whl", hash = "sha256:3528a53e4bbe64b07a6112b4c5a00ff3c61924ee46c9c68e004a1ac7ad1f09c3"},
 ]
 
 [[package]]
@@ -4179,13 +4179,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "virtualenv"
-version = "20.26.2"
+version = "20.26.3"
 description = "Virtual Python Environment builder"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "virtualenv-20.26.2-py3-none-any.whl", hash = "sha256:a624db5e94f01ad993d476b9ee5346fdf7b9de43ccaee0e0197012dc838a0e9b"},
-    {file = "virtualenv-20.26.2.tar.gz", hash = "sha256:82bf0f4eebbb78d36ddaee0283d43fe5736b53880b8a8cdcd37390a07ac3741c"},
+    {file = "virtualenv-20.26.3-py3-none-any.whl", hash = "sha256:8cc4a31139e796e9a7de2cd5cf2489de1217193116a8fd42328f1bd65f434589"},
+    {file = "virtualenv-20.26.3.tar.gz", hash = "sha256:4c43a2a236279d9ea36a0d76f98d84bd6ca94ac4e0f4a3b9d46d05e10fea542a"},
 ]
 
 [package.dependencies]
diff --git a/pyproject.toml b/pyproject.toml
index bbacb4c4..432124a0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -66,6 +66,21 @@ coverage = "^7.5.3"
 requires = ["poetry-core>=1.9"]
 build-backend = "poetry.core.masonry.api"
 
+[tool.coverage.report]
+exclude_also = [
+    "def __repr__",
+    "if self.debug:",
+    "if settings.DEBUG",
+    "raise AssertionError",
+    "raise NotImplementedError",
+    "if 0:",
+    "if __name__ == .__main__.:",
+    "if TYPE_CHECKING:",
+    "class .*\\bProtocol\\):",
+    "@(abc\\.)?abstractmethod",
+    "except ImportError:",
+]
+
 [tool.ruff]
 force-exclude = true
 line-length = 120