diff --git a/fortuna/calib_model/classification.py b/fortuna/calib_model/classification.py
index e8e4994e..4226e225 100644
--- a/fortuna/calib_model/classification.py
+++ b/fortuna/calib_model/classification.py
@@ -182,9 +182,11 @@ def calibrate(
             self._check_output_dim(val_data_loader)
         return self._calibrate(
             calib_data_loader=calib_data_loader,
-            uncertainty_fn=config.monitor.uncertainty_fn
-            if config.monitor.uncertainty_fn is not None
-            else self.prob_output_layer.mean,
+            uncertainty_fn=(
+                config.monitor.uncertainty_fn
+                if config.monitor.uncertainty_fn is not None
+                else self.prob_output_layer.mean
+            ),
             val_data_loader=val_data_loader,
             loss_fn=loss_fn,
             config=config,
diff --git a/fortuna/calib_model/loss.py b/fortuna/calib_model/loss.py
index 915ae781..f8505ce3 100644
--- a/fortuna/calib_model/loss.py
+++ b/fortuna/calib_model/loss.py
@@ -95,12 +95,16 @@ def __call__(
         aux = dict()
         if self.likelihood.output_calib_manager is not None:
             outs = self.likelihood.output_calib_manager.apply(
-                params=calib_params["output_calibrator"]
-                if calib_params is not None
-                else None,
-                mutable=calib_mutable["output_calibrator"]
-                if calib_mutable is not None
-                else None,
+                params=(
+                    calib_params["output_calibrator"]
+                    if calib_params is not None
+                    else None
+                ),
+                mutable=(
+                    calib_mutable["output_calibrator"]
+                    if calib_mutable is not None
+                    else None
+                ),
                 outputs=outputs,
                 calib="calib_mutable" in return_aux,
             )
diff --git a/fortuna/calib_model/predictive/regression.py b/fortuna/calib_model/predictive/regression.py
index 22263be1..580b99dd 100644
--- a/fortuna/calib_model/predictive/regression.py
+++ b/fortuna/calib_model/predictive/regression.py
@@ -146,9 +146,7 @@ def credible_interval(
         q = (
             jnp.array([0.5 * error, 1 - 0.5 * error])
             if interval_type == "two-tailed"
-            else error
-            if interval_type == "left-tailed"
-            else 1 - error
+            else error if interval_type == "left-tailed" else 1 - error
         )
         qq = self.quantile(
             q=q,
diff --git a/fortuna/calib_model/regression.py b/fortuna/calib_model/regression.py
index cffc3e4f..e1e5eec7 100644
--- a/fortuna/calib_model/regression.py
+++ b/fortuna/calib_model/regression.py
@@ -128,9 +128,11 @@ def calibrate(
             self._check_output_dim(val_data_loader)
         return self._calibrate(
             calib_data_loader=calib_data_loader,
-            uncertainty_fn=config.monitor.uncertainty_fn
-            if config.monitor.uncertainty_fn is not None
-            else self.prob_output_layer.variance,
+            uncertainty_fn=(
+                config.monitor.uncertainty_fn
+                if config.monitor.uncertainty_fn is not None
+                else self.prob_output_layer.variance
+            ),
             val_data_loader=val_data_loader,
             loss_fn=loss_fn,
             config=config,
diff --git a/fortuna/calib_model/state.py b/fortuna/calib_model/state.py
index 4a7be7b9..1a4e25ca 100644
--- a/fortuna/calib_model/state.py
+++ b/fortuna/calib_model/state.py
@@ -37,9 +37,11 @@ def init(
         return cls(
             apply_fn=None,
             params=params,
-            opt_state=kwargs["opt_state"]
-            if optimizer is None and "opt_state" in kwargs
-            else optimizer.init(params),
+            opt_state=(
+                kwargs["opt_state"]
+                if optimizer is None and "opt_state" in kwargs
+                else optimizer.init(params)
+            ),
             mutable=mutable,
             step=kwargs.get("step", 0),
             tx=optimizer,
@@ -78,11 +80,15 @@ def init_from_dict(
             FrozenDict(d["params"]),
             FrozenDict(d["mutable"]) if d["mutable"] is not None else None,
             optimizer,
-            FrozenDict(d.get("calib_params"))
-            if d["calib_params"] is not None
-            else None,
-            FrozenDict(d.get("calib_mutable"))
-            if d["calib_mutable"] is not None
-            else None,
+            (
+                FrozenDict(d.get("calib_params"))
+                if d["calib_params"] is not None
+                else None
+            ),
+            (
+                FrozenDict(d.get("calib_mutable"))
+                if d["calib_mutable"] is not None
+                else None
+            ),
             **kwargs,
         )
diff --git a/fortuna/likelihood/base.py b/fortuna/likelihood/base.py
index 118439f3..52b4b0ef 100644
--- a/fortuna/likelihood/base.py
+++ b/fortuna/likelihood/base.py
@@ -223,12 +223,16 @@ def _batched_log_joint_prob(
         aux = dict()
         if self.output_calib_manager is not None:
             outs = self.output_calib_manager.apply(
-                params=calib_params["output_calibrator"]
-                if calib_params is not None
-                else None,
-                mutable=calib_mutable["output_calibrator"]
-                if calib_mutable is not None
-                else None,
+                params=(
+                    calib_params["output_calibrator"]
+                    if calib_params is not None
+                    else None
+                ),
+                mutable=(
+                    calib_mutable["output_calibrator"]
+                    if calib_mutable is not None
+                    else None
+                ),
                 outputs=outputs,
                 calib="calib_mutable" in return_aux,
             )
@@ -407,12 +411,16 @@ def get_calibrated_outputs(
             and self.output_calib_manager.output_calibrator is not None
         ):
             outputs = self.output_calib_manager.apply(
-                params=calib_params["output_calibrator"]
-                if calib_params is not None
-                else None,
-                mutable=calib_mutable["output_calibrator"]
-                if calib_mutable is not None
-                else None,
+                params=(
+                    calib_params["output_calibrator"]
+                    if calib_params is not None
+                    else None
+                ),
+                mutable=(
+                    calib_mutable["output_calibrator"]
+                    if calib_mutable is not None
+                    else None
+                ),
                 outputs=outputs,
                 **kwargs,
             )
@@ -434,12 +442,16 @@ def _get_batched_calibrated_outputs(
             and self.output_calib_manager.output_calibrator is not None
         ):
             outputs = self.output_calib_manager.apply(
-                params=calib_params["output_calibrator"]
-                if calib_params is not None
-                else None,
-                mutable=calib_mutable["output_calibrator"]
-                if calib_mutable is not None
-                else None,
+                params=(
+                    calib_params["output_calibrator"]
+                    if calib_params is not None
+                    else None
+                ),
+                mutable=(
+                    calib_mutable["output_calibrator"]
+                    if calib_mutable is not None
+                    else None
+                ),
                 outputs=outputs,
                 **kwargs,
             )
diff --git a/fortuna/model/constant.py b/fortuna/model/constant.py
index 9eb2610e..6abbbf21 100644
--- a/fortuna/model/constant.py
+++ b/fortuna/model/constant.py
@@ -19,6 +19,7 @@ class ConstantModel(nn.Module):
         Function to initialize the model parameters.
         This must be one of the available options in :code:`flax.linen.initializers`.
     """
+
     output_dim: int
     initializer_fun: Optional[Initializer] = nn.initializers.zeros
 
diff --git a/fortuna/model/hyper.py b/fortuna/model/hyper.py
index 65d406a8..907262b2 100644
--- a/fortuna/model/hyper.py
+++ b/fortuna/model/hyper.py
@@ -13,6 +13,7 @@ class HyperparameterModel(nn.Module):
     value: Union[float, Array]
         Value of the hyperparameter.
     """
+
     value: Array
 
     def setup(self) -> None:
diff --git a/fortuna/model/model_manager/regression.py b/fortuna/model/model_manager/regression.py
index 2458c6d6..691aa738 100644
--- a/fortuna/model/model_manager/regression.py
+++ b/fortuna/model/model_manager/regression.py
@@ -120,9 +120,9 @@ def apply_fn(p, x, m_mutable, llv_mutable):
                     p,
                     x,
                     m_mutable=mutable["model"] if mutable is not None else False,
-                    llv_mutable=mutable["lik_log_var"]
-                    if mutable is not None
-                    else False,
+                    llv_mutable=(
+                        mutable["lik_log_var"] if mutable is not None else False
+                    ),
                 ),
                 model_params=params,
                 x=inputs,
diff --git a/fortuna/model/scalar_constant.py b/fortuna/model/scalar_constant.py
index df1a167c..9ad860a7 100644
--- a/fortuna/model/scalar_constant.py
+++ b/fortuna/model/scalar_constant.py
@@ -20,6 +20,7 @@ class ScalarConstantModel(nn.Module):
         Function to initialize the model parameters.
         This must be one of the available options in :code:`flax.linen.initializers`.
     """
+
     output_dim: int
     initializer_fun: Optional[Initializer] = nn.initializers.zeros
 
diff --git a/fortuna/model/scalar_hyper.py b/fortuna/model/scalar_hyper.py
index 6d3d9b1e..e11e3d94 100644
--- a/fortuna/model/scalar_hyper.py
+++ b/fortuna/model/scalar_hyper.py
@@ -16,6 +16,7 @@ class ScalarHyperparameterModel(nn.Module):
     value: float
         Scalar value of the hyperparameter.
     """
+
     output_dim: int
     value: float
 
diff --git a/fortuna/model/utils/spectral_norm.py b/fortuna/model/utils/spectral_norm.py
index 95d14d3c..7da95823 100644
--- a/fortuna/model/utils/spectral_norm.py
+++ b/fortuna/model/utils/spectral_norm.py
@@ -1,6 +1,7 @@
 """
 The code has been taken from https://github.com/google/edward2/blob/main/edward2/jax/nn/normalization.py
 """
+
 import dataclasses
 from typing import (
     Any,
diff --git a/fortuna/model/wideresnet.py b/fortuna/model/wideresnet.py
index 7ac25ff0..ee91cb16 100644
--- a/fortuna/model/wideresnet.py
+++ b/fortuna/model/wideresnet.py
@@ -2,6 +2,7 @@
 Wide ResNet model
 (adapted from https://github.com/google/flax/blob/v0.2/examples/cifar10/models/wideresnet.py)
 """
+
 from functools import partial
 from typing import (
     Any,
diff --git a/fortuna/ood_detection/ddu.py b/fortuna/ood_detection/ddu.py
index 555f556e..f8528daa 100644
--- a/fortuna/ood_detection/ddu.py
+++ b/fortuna/ood_detection/ddu.py
@@ -1,6 +1,7 @@
 """
 Adapted from https://github.com/omegafragger/DDU/blob/main/utils/gmm_utils.py
 """
+
 import logging
 from typing import (
     Callable,
diff --git a/fortuna/output_calib_model/classification.py b/fortuna/output_calib_model/classification.py
index 2d5d1d31..7c674f2b 100644
--- a/fortuna/output_calib_model/classification.py
+++ b/fortuna/output_calib_model/classification.py
@@ -102,9 +102,11 @@ def calibrate(
         if val_outputs is not None:
             self._check_output_dim(val_outputs, val_targets)
         return super()._calibrate(
-            uncertainty_fn=config.monitor.uncertainty_fn
-            if config.monitor.uncertainty_fn is not None
-            else self.prob_output_layer.mean,
+            uncertainty_fn=(
+                config.monitor.uncertainty_fn
+                if config.monitor.uncertainty_fn is not None
+                else self.prob_output_layer.mean
+            ),
             calib_outputs=calib_outputs,
             calib_targets=calib_targets,
             val_outputs=val_outputs,
diff --git a/fortuna/output_calib_model/regression.py b/fortuna/output_calib_model/regression.py
index cc20a58b..68c9c062 100644
--- a/fortuna/output_calib_model/regression.py
+++ b/fortuna/output_calib_model/regression.py
@@ -99,9 +99,11 @@ def calibrate(
         if val_outputs is not None:
             self._check_output_dim(val_outputs, val_targets)
         return super()._calibrate(
-            uncertainty_fn=config.monitor.uncertainty_fn
-            if config.monitor.uncertainty_fn is not None
-            else self.prob_output_layer.variance,
+            uncertainty_fn=(
+                config.monitor.uncertainty_fn
+                if config.monitor.uncertainty_fn is not None
+                else self.prob_output_layer.variance
+            ),
             calib_outputs=calib_outputs,
             calib_targets=calib_targets,
             val_outputs=val_outputs,
diff --git a/fortuna/output_calib_model/state.py b/fortuna/output_calib_model/state.py
index 11fa55d4..c69352d3 100644
--- a/fortuna/output_calib_model/state.py
+++ b/fortuna/output_calib_model/state.py
@@ -52,11 +52,11 @@ def init(
         return cls(
             apply_fn=None,
             params=params,
-            opt_state=kwargs["opt_state"]
-            if optimizer is None and "opt_state" in kwargs
-            else None
-            if optimizer is None
-            else optimizer.init(params),
+            opt_state=(
+                kwargs["opt_state"]
+                if optimizer is None and "opt_state" in kwargs
+                else None if optimizer is None else optimizer.init(params)
+            ),
             mutable=mutable,
             step=kwargs.get("step", 0),
             tx=optimizer,
diff --git a/fortuna/plot.py b/fortuna/plot.py
index 28b42a03..acdef3a9 100755
--- a/fortuna/plot.py
+++ b/fortuna/plot.py
@@ -167,9 +167,11 @@ def plot_2d_classification_predictions_and_uncertainty(
             inputs[:, 0],
             inputs[:, 1],
             s=marker_size,
-            c=[preds_color[0] if i == 1 else preds_color[1] for i in preds]
-            if preds is not None
-            else base_inputs_color,
+            c=(
+                [preds_color[0] if i == 1 else preds_color[1] for i in preds]
+                if preds is not None
+                else base_inputs_color
+            ),
         )
     if colorbar:
         plt.colorbar(im, ax=ax.ravel().tolist() if hasattr(ax, "ravel") else ax)
diff --git a/fortuna/prob_model/classification.py b/fortuna/prob_model/classification.py
index 1011a583..7ee36a46 100644
--- a/fortuna/prob_model/classification.py
+++ b/fortuna/prob_model/classification.py
@@ -287,9 +287,11 @@ def calibrate(
         if val_data_loader is not None:
             self._check_output_dim(val_data_loader)
         return super()._calibrate(
-            uncertainty_fn=calib_config.monitor.uncertainty_fn
-            if calib_config.monitor.uncertainty_fn is not None
-            else self.prob_output_layer.mean,
+            uncertainty_fn=(
+                calib_config.monitor.uncertainty_fn
+                if calib_config.monitor.uncertainty_fn is not None
+                else self.prob_output_layer.mean
+            ),
             calib_data_loader=calib_data_loader,
             val_data_loader=val_data_loader,
             calib_config=calib_config,
diff --git a/fortuna/prob_model/posterior/deep_ensemble/deep_ensemble_posterior.py b/fortuna/prob_model/posterior/deep_ensemble/deep_ensemble_posterior.py
index 6fe54e6f..557614f8 100755
--- a/fortuna/prob_model/posterior/deep_ensemble/deep_ensemble_posterior.py
+++ b/fortuna/prob_model/posterior/deep_ensemble/deep_ensemble_posterior.py
@@ -172,9 +172,11 @@ def _fit(i):
         else:
             self.state = PosteriorMultiStateRepository(
                 size=self.posterior_approximator.ensemble_size,
-                checkpoint_dir=fit_config.checkpointer.save_checkpoint_dir
-                if fit_config.checkpointer.dump_state is True
-                else None,
+                checkpoint_dir=(
+                    fit_config.checkpointer.save_checkpoint_dir
+                    if fit_config.checkpointer.dump_state is True
+                    else None
+                ),
             )
 
         status = []
diff --git a/fortuna/prob_model/posterior/laplace/laplace_posterior.py b/fortuna/prob_model/posterior/laplace/laplace_posterior.py
index ef852e6c..6f56116f 100755
--- a/fortuna/prob_model/posterior/laplace/laplace_posterior.py
+++ b/fortuna/prob_model/posterior/laplace/laplace_posterior.py
@@ -161,12 +161,16 @@ def apply_calib_model_manager(_params, _batch_inputs):
                 _params, _batch_inputs, mutable=mutable, train=False
             )
             outputs = self.joint.likelihood.output_calib_manager.apply(
-                params=calib_params["output_calibrator"]
-                if calib_params is not None
-                else None,
-                mutable=calib_mutable["output_calibrator"]
-                if calib_mutable is not None
-                else None,
+                params=(
+                    calib_params["output_calibrator"]
+                    if calib_params is not None
+                    else None
+                ),
+                mutable=(
+                    calib_mutable["output_calibrator"]
+                    if calib_mutable is not None
+                    else None
+                ),
                 outputs=outputs,
             )
             return outputs
diff --git a/fortuna/prob_model/posterior/map/map_posterior.py b/fortuna/prob_model/posterior/map/map_posterior.py
index c86be8c1..b36013cc 100755
--- a/fortuna/prob_model/posterior/map/map_posterior.py
+++ b/fortuna/prob_model/posterior/map/map_posterior.py
@@ -101,9 +101,9 @@ def fit(
             n_epochs=fit_config.optimizer.n_epochs,
             metrics=fit_config.monitor.metrics,
             validation_dataloader=val_data_loader,
-            validation_dataset_size=val_data_loader.size
-            if val_data_loader is not None
-            else None,
+            validation_dataset_size=(
+                val_data_loader.size if val_data_loader is not None else None
+            ),
             verbose=fit_config.monitor.verbose,
             callbacks=fit_config.callbacks,
             max_grad_norm=fit_config.hyperparameters.max_grad_norm,
diff --git a/fortuna/prob_model/posterior/normalizing_flow/advi/advi_posterior.py b/fortuna/prob_model/posterior/normalizing_flow/advi/advi_posterior.py
index 5cd83647..65ced66f 100755
--- a/fortuna/prob_model/posterior/normalizing_flow/advi/advi_posterior.py
+++ b/fortuna/prob_model/posterior/normalizing_flow/advi/advi_posterior.py
@@ -178,9 +178,9 @@ def fit(
             n_epochs=fit_config.optimizer.n_epochs,
             metrics=fit_config.monitor.metrics,
             validation_dataloader=val_data_loader,
-            validation_dataset_size=val_data_loader.size
-            if val_data_loader is not None
-            else None,
+            validation_dataset_size=(
+                val_data_loader.size if val_data_loader is not None else None
+            ),
             verbose=fit_config.monitor.verbose,
             unravel=self._unravel,
             n_samples=self.posterior_approximator.n_loss_samples,
diff --git a/fortuna/prob_model/posterior/posterior_multi_state_repository.py b/fortuna/prob_model/posterior/posterior_multi_state_repository.py
index 1cca1e86..734aacb4 100644
--- a/fortuna/prob_model/posterior/posterior_multi_state_repository.py
+++ b/fortuna/prob_model/posterior/posterior_multi_state_repository.py
@@ -21,9 +21,9 @@ def __init__(self, size: int, checkpoint_dir: Optional[Path] = None):
         self.size = size
         self.state = [
             PosteriorStateRepository(
-                checkpoint_dir=os.path.join(checkpoint_dir, str(i))
-                if checkpoint_dir
-                else None
+                checkpoint_dir=(
+                    os.path.join(checkpoint_dir, str(i)) if checkpoint_dir else None
+                )
             )
             for i in range(size)
         ]
diff --git a/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_posterior.py b/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_posterior.py
index d8f25150..780817c9 100644
--- a/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_posterior.py
+++ b/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_posterior.py
@@ -181,9 +181,9 @@ def fit(
             n_epochs=fit_config.optimizer.n_epochs,
             metrics=fit_config.monitor.metrics,
             validation_dataloader=val_data_loader,
-            validation_dataset_size=val_data_loader.size
-            if val_data_loader is not None
-            else None,
+            validation_dataset_size=(
+                val_data_loader.size if val_data_loader is not None else None
+            ),
             verbose=fit_config.monitor.verbose,
             callbacks=[cyclical_sampling_callback],
         )
diff --git a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_posterior.py b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_posterior.py
index 4641a71d..59ab9464 100644
--- a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_posterior.py
+++ b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_posterior.py
@@ -177,9 +177,9 @@ def fit(
             n_epochs=fit_config.optimizer.n_epochs,
             metrics=fit_config.monitor.metrics,
             validation_dataloader=val_data_loader,
-            validation_dataset_size=val_data_loader.size
-            if val_data_loader is not None
-            else None,
+            validation_dataset_size=(
+                val_data_loader.size if val_data_loader is not None else None
+            ),
             verbose=fit_config.monitor.verbose,
             callbacks=[sghmc_sampling_callback],
         )
diff --git a/fortuna/prob_model/posterior/state.py b/fortuna/prob_model/posterior/state.py
index 110c148a..de0b030d 100644
--- a/fortuna/prob_model/posterior/state.py
+++ b/fortuna/prob_model/posterior/state.py
@@ -74,9 +74,11 @@ def init(
         return cls(
             apply_fn=None,
             params=params,
-            opt_state=kwargs["opt_state"]
-            if optimizer is None and "opt_state" in kwargs
-            else optimizer.init(params),
+            opt_state=(
+                kwargs["opt_state"]
+                if optimizer is None and "opt_state" in kwargs
+                else optimizer.init(params)
+            ),
             mutable=mutable,
             step=kwargs.get("step", 0),
             tx=optimizer,
@@ -132,11 +134,15 @@ def init_from_dict(
             FrozenDict(d["params"]),
             FrozenDict(d["mutable"]) if d["mutable"] is not None else None,
             optimizer,
-            FrozenDict(d.get("calib_params"))
-            if d["calib_params"] is not None
-            else None,
-            FrozenDict(d.get("calib_mutable"))
-            if d["calib_mutable"] is not None
-            else None,
+            (
+                FrozenDict(d.get("calib_params"))
+                if d["calib_params"] is not None
+                else None
+            ),
+            (
+                FrozenDict(d.get("calib_mutable"))
+                if d["calib_mutable"] is not None
+                else None
+            ),
             **kwargs,
         )
diff --git a/fortuna/prob_model/posterior/swag/swag_posterior.py b/fortuna/prob_model/posterior/swag/swag_posterior.py
index 1cb4bb30..81dfec91 100755
--- a/fortuna/prob_model/posterior/swag/swag_posterior.py
+++ b/fortuna/prob_model/posterior/swag/swag_posterior.py
@@ -160,9 +160,9 @@ def fit(
             n_epochs=fit_config.optimizer.n_epochs,
             metrics=fit_config.monitor.metrics,
             validation_dataloader=val_data_loader,
-            validation_dataset_size=val_data_loader.size
-            if val_data_loader is not None
-            else None,
+            validation_dataset_size=(
+                val_data_loader.size if val_data_loader is not None else None
+            ),
             verbose=fit_config.monitor.verbose,
             callbacks=fit_config.callbacks,
             **kwargs,
diff --git a/fortuna/prob_model/posterior/swag/swag_trainer.py b/fortuna/prob_model/posterior/swag/swag_trainer.py
index 01351fac..2601d775 100644
--- a/fortuna/prob_model/posterior/swag/swag_trainer.py
+++ b/fortuna/prob_model/posterior/swag/swag_trainer.py
@@ -43,16 +43,22 @@ def _update_state_with_stats(self, state: SWAGState) -> SWAGState:
         var = jnp.maximum(var, 0.0)
         return state.update(
             dict(
-                mean=self._mean_rav_params
-                if not self.multi_device
-                else self._mean_rav_params[None],
+                mean=(
+                    self._mean_rav_params
+                    if not self.multi_device
+                    else self._mean_rav_params[None]
+                ),
                 std=jnp.sqrt(var) if not self.multi_device else jnp.sqrt(var)[None],
-                dev=self._deviation_rav_params
-                if not self.multi_device
-                else self._deviation_rav_params[None],
-                _encoded_which_params=self._encoded_which_params
-                if not self.multi_device
-                else tree_map(lambda v: v[None], self._encoded_which_params),
+                dev=(
+                    self._deviation_rav_params
+                    if not self.multi_device
+                    else self._deviation_rav_params[None]
+                ),
+                _encoded_which_params=(
+                    self._encoded_which_params
+                    if not self.multi_device
+                    else tree_map(lambda v: v[None], self._encoded_which_params)
+                ),
             )
         )
 
diff --git a/fortuna/prob_model/predictive/base.py b/fortuna/prob_model/predictive/base.py
index 4218a775..2aeedf64 100644
--- a/fortuna/prob_model/predictive/base.py
+++ b/fortuna/prob_model/predictive/base.py
@@ -239,12 +239,16 @@ def _lik_log_joint_prob(key):
                     n_data,
                     mutable=sample.mutable,
                     return_aux=return_aux,
-                    calib_params=calib_params
-                    if calib_params is not None
-                    else sample.calib_params,
-                    calib_mutable=calib_mutable
-                    if calib_mutable is not None
-                    else sample.calib_mutable,
+                    calib_params=(
+                        calib_params
+                        if calib_params is not None
+                        else sample.calib_params
+                    ),
+                    calib_mutable=(
+                        calib_mutable
+                        if calib_mutable is not None
+                        else sample.calib_mutable
+                    ),
                     **kwargs,
                 )
 
diff --git a/fortuna/prob_model/predictive/regression.py b/fortuna/prob_model/predictive/regression.py
index 7f9a67e4..c66d5380 100644
--- a/fortuna/prob_model/predictive/regression.py
+++ b/fortuna/prob_model/predictive/regression.py
@@ -309,9 +309,7 @@ def credible_interval(
         q = (
             jnp.array([0.5 * error, 1 - 0.5 * error])
             if interval_type == "two-tailed"
-            else error
-            if interval_type == "left-tailed"
-            else 1 - error
+            else error if interval_type == "left-tailed" else 1 - error
         )
         qq = self.quantile(
             q=q,
diff --git a/fortuna/prob_model/regression.py b/fortuna/prob_model/regression.py
index f39cdbc0..69146ac7 100755
--- a/fortuna/prob_model/regression.py
+++ b/fortuna/prob_model/regression.py
@@ -194,9 +194,11 @@ def calibrate(
         if val_data_loader is not None:
             self._check_output_dim(val_data_loader)
         return super()._calibrate(
-            uncertainty_fn=calib_config.monitor.uncertainty_fn
-            if calib_config.monitor.uncertainty_fn is not None
-            else self.prob_output_layer.variance,
+            uncertainty_fn=(
+                calib_config.monitor.uncertainty_fn
+                if calib_config.monitor.uncertainty_fn is not None
+                else self.prob_output_layer.variance
+            ),
             calib_data_loader=calib_data_loader,
             val_data_loader=val_data_loader,
             calib_config=calib_config,
diff --git a/fortuna/prob_output_layer/regression.py b/fortuna/prob_output_layer/regression.py
index 0fd99cd5..ff9a08d7 100644
--- a/fortuna/prob_output_layer/regression.py
+++ b/fortuna/prob_output_layer/regression.py
@@ -128,9 +128,7 @@ def credible_interval(
         q = (
             jnp.array([0.5 * error, 1 - 0.5 * error])
             if interval_type == "two-tailed"
-            else error
-            if interval_type == "left-tailed"
-            else 1 - error
+            else error if interval_type == "left-tailed" else 1 - error
         )
         qq = self.quantile(
             q=q, outputs=outputs, n_target_samples=n_target_samples, rng=rng
diff --git a/fortuna/training/trainer.py b/fortuna/training/trainer.py
index 5959bc2f..680415b6 100755
--- a/fortuna/training/trainer.py
+++ b/fortuna/training/trainer.py
@@ -706,9 +706,11 @@ def _get_all_params(
             return FrozenDict(
                 nested_update(
                     state.frozen_params.unfreeze(),
-                    trainable_params.unfreeze()
-                    if trainable_params is not None
-                    else state.params.unfreeze(),
+                    (
+                        trainable_params.unfreeze()
+                        if trainable_params is not None
+                        else state.params.unfreeze()
+                    ),
                 )
             )
         return trainable_params if trainable_params is not None else state.params
diff --git a/fortuna/utils/probit.py b/fortuna/utils/probit.py
index 4e6aaaad..ee31aa06 100644
--- a/fortuna/utils/probit.py
+++ b/fortuna/utils/probit.py
@@ -143,13 +143,13 @@ def _compute_cov(_x, idx):
             prev_idx = idx[prev_tau] if idx is not None else None
             size = n_outputs if idx is None else len(prev_idx)
 
-            new_fun = (
-                lambda p: _apply_fn(p, _x, new_tau)[new_idx]
+            new_fun = lambda p: (
+                _apply_fn(p, _x, new_tau)[new_idx]
                 if idx is not None
                 else _apply_fn(p, _x, new_tau)
             )
-            prev_fun = (
-                lambda p: _apply_fn(p, _x, prev_tau)[prev_idx]
+            prev_fun = lambda p: (
+                _apply_fn(p, _x, prev_tau)[prev_idx]
                 if idx is not None
                 else _apply_fn(p, _x, prev_tau)
             )
diff --git a/poetry.lock b/poetry.lock
index b548a425..b3b74abb 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -24,87 +24,87 @@ files = [
 
 [[package]]
 name = "aiohttp"
-version = "3.9.0"
+version = "3.9.3"
 description = "Async http client/server framework (asyncio)"
 optional = true
 python-versions = ">=3.8"
 files = [
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-    {file = "aiohttp-3.9.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1736d87dad8ef46a8ec9cddd349fa9f7bd3a064c47dd6469c0d6763d3d49a4fc"},
-    {file = "aiohttp-3.9.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8c9e5f4d7208cda1a2bb600e29069eecf857e6980d0ccc922ccf9d1372c16f4b"},
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 ]
 
 [package.dependencies]
@@ -890,47 +890,56 @@ toml = ["tomli"]
 
 [[package]]
 name = "cryptography"
-version = "41.0.6"
+version = "42.0.5"
 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 optional = false
 python-versions = ">=3.7"
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