diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml
index ff2f7dd6..413e93d2 100644
--- a/.github/workflows/documentation.yml
+++ b/.github/workflows/documentation.yml
@@ -32,7 +32,7 @@ jobs:
       - name: Install and configure Poetry
         uses: snok/install-poetry@v1
         with:
-          version: 1.2.2
+          version: 1.8.3
           virtualenvs-create: true
           virtualenvs-in-project: false
           installer-parallel: true
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 78a19f7c..60356575 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -13,7 +13,7 @@ jobs:
     strategy:
       matrix:
         # Select the Python versions to test against
-        python-version: ['3.8', '3.9', '3.10', '3.11']
+        python-version: ["3.9", "3.10", "3.11"]
     steps:
       - name: Check out the code
         uses: actions/checkout@v3
@@ -28,7 +28,7 @@ jobs:
       - name: Install Poetry
         uses: snok/install-poetry@v1.3.3
         with:
-          version: 1.4.0
+          version: 1.8.3
 
       # Configure Poetry to use the virtual environment in the project
       - name: Setup Poetry
@@ -38,7 +38,7 @@ jobs:
       # Install the dependencies
       - name: Install Package
         run: |
-          poetry install --with tests
+          poetry install --with test
 
       # Run the unit tests and build the coverage report
       - name: Run Tests
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index 2a848f34..47cc6754 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -4,7 +4,7 @@ version: 2
 build:
   os: ubuntu-20.04
   tools:
-    python: '3.10'
+    python: '3.11'
   jobs:
     post_create_environment:
       # Install poetry
diff --git a/examples/bring_in_your_own.pct.py b/examples/bring_in_your_own.pct.py
index 8235069d..08196cf0 100644
--- a/examples/bring_in_your_own.pct.py
+++ b/examples/bring_in_your_own.pct.py
@@ -77,7 +77,7 @@ def __call__(self, x):
 from fortuna.utils.random import generate_rng_like_tree
 from jax.flatten_util import ravel_pytree
 from jax.tree_util import tree_map
-from jax._src.prng import PRNGKeyArray
+import jax
 import jax.numpy as jnp
 
 
@@ -86,7 +86,7 @@ def log_joint_prob(self, params: Params) -> float:
         v = jnp.mean((ravel_pytree(params)[0] <= 1) & (ravel_pytree(params)[0] >= 0))
         return jnp.where(v == 1.0, jnp.array(0), -jnp.inf)
 
-    def sample(self, params_like: Params, rng: Optional[PRNGKeyArray] = None) -> Params:
+    def sample(self, params_like: Params, rng: Optional[jax.Array] = None) -> Params:
         if rng is None:
             rng = self.rng.get()
         keys = generate_rng_like_tree(rng, params_like)
diff --git a/fortuna/calib_model/calib_model_calibrator.py b/fortuna/calib_model/calib_model_calibrator.py
index 81c58aa3..d22bdda3 100644
--- a/fortuna/calib_model/calib_model_calibrator.py
+++ b/fortuna/calib_model/calib_model_calibrator.py
@@ -8,7 +8,7 @@
 )
 
 from flax.core import FrozenDict
-from jax._src.prng import PRNGKeyArray
+import jax
 import jax.numpy as jnp
 from optax._src.base import PyTree
 
@@ -35,7 +35,7 @@ def training_loss_step(
         params: Params,
         batch: Batch,
         mutable: Mutable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
         unravel: Optional[Callable[[any], PyTree]] = None,
         calib_params: Optional[CalibParams] = None,
@@ -71,7 +71,7 @@ def validation_step(
         state: CalibState,
         batch: Batch,
         loss_fun: Callable[[Any], Union[float, Tuple[float, dict]]],
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
         metrics: Optional[Tuple[Callable[[jnp.ndarray, Array], float], ...]] = None,
         unravel: Optional[Callable[[any], PyTree]] = None,
diff --git a/fortuna/calib_model/classification.py b/fortuna/calib_model/classification.py
index 4226e225..7e4b3f5d 100644
--- a/fortuna/calib_model/classification.py
+++ b/fortuna/calib_model/classification.py
@@ -124,7 +124,7 @@ def _get_model_manager(
                 )
             else:
                 model_manager = model_manager_cls(model, model_editor)
-        except ModuleNotFoundError as e:
+        except ModuleNotFoundError:
             logging.warning(
                 "No module named 'transformer' is installed. "
                 "If you are not working with models from the `transformers` library ignore this warning, otherwise "
diff --git a/fortuna/calib_model/config/monitor.py b/fortuna/calib_model/config/monitor.py
index ae9b0550..369a2845 100644
--- a/fortuna/calib_model/config/monitor.py
+++ b/fortuna/calib_model/config/monitor.py
@@ -66,7 +66,7 @@ def __init__(
                         f"All metrics in `metrics` must be callable objects, but {metric} is not."
                     )
         if uncertainty_fn is not None and not callable(uncertainty_fn):
-            raise ValueError(f"`uncertainty_fn` must be a a callable function.")
+            raise ValueError("`uncertainty_fn` must be a a callable function.")
 
         self.metrics = metrics
         self.uncertainty_fn = uncertainty_fn
diff --git a/fortuna/calib_model/loss.py b/fortuna/calib_model/loss.py
index f8505ce3..8e675a1e 100644
--- a/fortuna/calib_model/loss.py
+++ b/fortuna/calib_model/loss.py
@@ -6,7 +6,7 @@
     Tuple,
 )
 
-from jax._src.prng import PRNGKeyArray
+import jax
 import jax.numpy as jnp
 
 from fortuna.likelihood.base import Likelihood
@@ -40,7 +40,7 @@ def __call__(
         return_aux: Optional[List[str]] = None,
         train: bool = False,
         outputs: Optional[jnp.ndarray] = None,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         **kwargs,
     ) -> Tuple[jnp.ndarray, Any]:
         if return_aux is None:
diff --git a/fortuna/calib_model/predictive/base.py b/fortuna/calib_model/predictive/base.py
index e17baee5..6431921f 100644
--- a/fortuna/calib_model/predictive/base.py
+++ b/fortuna/calib_model/predictive/base.py
@@ -1,6 +1,6 @@
 from typing import Optional
 
-from jax._src.prng import PRNGKeyArray
+import jax
 import jax.numpy as jnp
 
 from fortuna.data.loader import (
@@ -60,7 +60,7 @@ def sample(
         self,
         inputs_loader: InputsLoader,
         n_samples: int = 1,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -80,7 +80,7 @@ def sample(
             A loader of input data points.
         n_samples : int
             Number of target samples to sample for each input data point.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
diff --git a/fortuna/calib_model/predictive/regression.py b/fortuna/calib_model/predictive/regression.py
index 580b99dd..42f0ccfb 100644
--- a/fortuna/calib_model/predictive/regression.py
+++ b/fortuna/calib_model/predictive/regression.py
@@ -4,7 +4,7 @@
     Union,
 )
 
-from jax._src.prng import PRNGKeyArray
+import jax
 import jax.numpy as jnp
 
 from fortuna.calib_model.predictive.base import Predictive
@@ -21,7 +21,7 @@ def entropy(
         self,
         inputs_loader: InputsLoader,
         n_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -42,7 +42,7 @@ def entropy(
             A loader of input data points.
         n_samples : int
             Number of samples to draw for each input.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -67,7 +67,7 @@ def quantile(
         q: Union[float, Array, List],
         inputs_loader: InputsLoader,
         n_samples: Optional[int] = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> Union[float, jnp.ndarray]:
         r"""
@@ -81,7 +81,7 @@ def quantile(
             A loader of input data points.
         n_samples : int
             Number of target samples to sample for each input data point.
-        rng: Optional[PRNGKeyArray]
+        rng: Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -109,7 +109,7 @@ def credible_interval(
         n_samples: int = 30,
         error: float = 0.05,
         interval_type: str = "two-tailed",
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -126,7 +126,7 @@ def credible_interval(
             `error=0.05` corresponds to a 95% level of credibility.
         interval_type: str
             The interval type. We support "two-tailed" (default), "right-tailed" and "left-tailed".
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
diff --git a/fortuna/conformal/multivalid/mixins/classification/top_label_multicalibrator.py b/fortuna/conformal/multivalid/mixins/classification/top_label_multicalibrator.py
index b992e503..b241e8a8 100644
--- a/fortuna/conformal/multivalid/mixins/classification/top_label_multicalibrator.py
+++ b/fortuna/conformal/multivalid/mixins/classification/top_label_multicalibrator.py
@@ -1,6 +1,5 @@
 from typing import Optional
 
-from jax import vmap
 import jax.numpy as jnp
 
 from fortuna.conformal.multivalid.mixins.multicalibrator import MulticalibratorMixin
diff --git a/fortuna/conformal/multivalid/one_shot/base.py b/fortuna/conformal/multivalid/one_shot/base.py
index 4d8f022f..1a73f40f 100644
--- a/fortuna/conformal/multivalid/one_shot/base.py
+++ b/fortuna/conformal/multivalid/one_shot/base.py
@@ -1,9 +1,7 @@
 import abc
 import logging
 from typing import (
-    Dict,
     Optional,
-    Tuple,
     Union,
 )
 
diff --git a/fortuna/data/dataset/huggingface_datasets.py b/fortuna/data/dataset/huggingface_datasets.py
index 7d556459..ec536e44 100644
--- a/fortuna/data/dataset/huggingface_datasets.py
+++ b/fortuna/data/dataset/huggingface_datasets.py
@@ -15,9 +15,9 @@
     Dataset,
     DatasetDict,
 )
+import jax
 from jax import numpy as jnp
 import jax.random
-from jax.random import PRNGKeyArray
 from tqdm import tqdm
 from transformers import (
     BatchEncoding,
@@ -90,7 +90,7 @@ def get_data_loader(
         self,
         dataset: Dataset,
         per_device_batch_size: int,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         shuffle: bool = False,
         drop_last: bool = False,
         verbose: bool = False,
@@ -105,7 +105,7 @@ def get_data_loader(
             A tokenizeed dataset (see :meth:`.HuggingFaceClassificationDatasetABC.get_tokenized_datasets`).
         per_device_batch_size: bool
             Batch size for each device.
-        rng: PRNGKeyArray
+        rng: jax.Array
             Random number generator.
         shuffle: bool
             if True, shuffle the data so that each batch is a ranom sample from the dataset.
@@ -141,7 +141,7 @@ def _collate(self, batch: Dict[str, Array], batch_size: int) -> Dict[str, Array]
 
     @staticmethod
     def _get_batches_idxs(
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         dataset_size: int,
         batch_size: int,
         shuffle: bool = False,
@@ -167,7 +167,7 @@ def _get_data_loader(
         batch_size: int,
         shuffle: bool = False,
         drop_last: bool = False,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         verbose: bool = False,
     ) -> Union[Iterable[Dict[str, Array]], Iterable[Tuple[Dict[str, Array], Array]]]:
         batch_idxs_gen = self._get_batches_idxs(
@@ -375,7 +375,7 @@ def __init__(
         super(HuggingFaceMaskedLMDataset, self).__init__(*args, **kwargs)
         if not self.tokenizer.is_fast:
             logger.warning(
-                f"You are not using a Fast Tokenizer, so whole words cannot be masked, only tokens."
+                "You are not using a Fast Tokenizer, so whole words cannot be masked, only tokens."
             )
         self.mlm = mlm
         self.mlm_probability = mlm_probability
@@ -407,7 +407,7 @@ def get_tokenized_datasets(
         ), "Only one text column should be passed when the task is MaskedLM."
 
         def _tokenize_fn(
-            batch: Dict[str, List[Union[str, int]]]
+            batch: Dict[str, List[Union[str, int]]],
         ) -> Dict[str, List[int]]:
             tokenized_inputs = self.tokenizer(
                 *[batch[col] for col in text_columns],
diff --git a/fortuna/distribution/gaussian.py b/fortuna/distribution/gaussian.py
index 959deadc..8c441b7d 100755
--- a/fortuna/distribution/gaussian.py
+++ b/fortuna/distribution/gaussian.py
@@ -1,10 +1,10 @@
 from typing import Union
 
+import jax
 from jax import (
     random,
     vmap,
 )
-from jax._src.prng import PRNGKeyArray
 import jax.numpy as jnp
 from jax.scipy.stats import (
     multivariate_normal,
@@ -30,11 +30,11 @@ def __init__(self, mean: Union[float, Array], std: Union[float, Array]):
         self.std = std
         self.dim = 1 if type(mean) in [int, float] else len(mean)
 
-    def sample(self, rng: PRNGKeyArray, n_samples: int = 1) -> jnp.ndarray:
+    def sample(self, rng: jax.Array, n_samples: int = 1) -> jnp.ndarray:
         """
         Sample from the diagonal Gaussian.
 
-        :param rng: PRNGKeyArray
+        :param rng: jax.Array
             Random number generator.
         :param n_samples: int
             Number of samples.
@@ -72,11 +72,11 @@ def __init__(self, mean: Array, cov: Array):
         self.cov = cov
         self.dim = len(mean)
 
-    def sample(self, rng: PRNGKeyArray, n_samples: int = 1) -> jnp.ndarray:
+    def sample(self, rng: jax.Array, n_samples: int = 1) -> jnp.ndarray:
         """
         Sample from the multivariate Gaussian.
 
-        :param rng: PRNGKeyArray
+        :param rng: jax.Array
             Random number generator.
         :param n_samples: int
             Number of samples.
diff --git a/fortuna/hallucination/base.py b/fortuna/hallucination/base.py
index 8b2fee51..193628f6 100644
--- a/fortuna/hallucination/base.py
+++ b/fortuna/hallucination/base.py
@@ -6,7 +6,6 @@
     List,
     Optional,
     Tuple,
-    Union,
 )
 
 import numpy as np
diff --git a/fortuna/likelihood/base.py b/fortuna/likelihood/base.py
index 52b4b0ef..37b392c2 100644
--- a/fortuna/likelihood/base.py
+++ b/fortuna/likelihood/base.py
@@ -8,11 +8,11 @@
     Union,
 )
 
+import jax
 from jax import (
     jit,
     pmap,
 )
-from jax._src.prng import PRNGKeyArray
 import jax.numpy as jnp
 
 from fortuna.data.loader import (
@@ -133,7 +133,7 @@ def _batched_log_joint_prob(
         return_aux: Optional[List[str]] = None,
         train: bool = False,
         outputs: Optional[jnp.ndarray] = None,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         **kwargs,
     ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, Any]]:
         """
@@ -161,7 +161,7 @@ def _batched_log_joint_prob(
             Whether the method is called during training.
         outputs : Optional[jnp.ndarray]
             Pre-computed batch of outputs.
-        rng: Optional[PRNGKeyArray]
+        rng: Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
 
         Returns
@@ -272,7 +272,7 @@ def sample(
         calib_params: Optional[CalibParams] = None,
         calib_mutable: Optional[CalibMutable] = None,
         return_aux: Optional[List[str]] = None,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
         **kwargs,
     ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, dict]]:
@@ -296,7 +296,7 @@ def sample(
         return_aux : Optional[List[str]]
             The auxiliary objects to return. We support 'outputs'. If this argument is not given, no auxiliary object
             is returned.
-        rng: Optional[PRNGKeyArray]
+        rng: Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -345,7 +345,7 @@ def _batched_sample(
         calib_params: Optional[CalibParams] = None,
         calib_mutable: Optional[CalibMutable] = None,
         return_aux: Optional[List[str]] = None,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         **kwargs,
     ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, dict]]:
         if return_aux is None:
diff --git a/fortuna/likelihood/regression.py b/fortuna/likelihood/regression.py
index c57b0d11..32ba58f1 100644
--- a/fortuna/likelihood/regression.py
+++ b/fortuna/likelihood/regression.py
@@ -3,8 +3,8 @@
     Union,
 )
 
+import jax
 from jax import vmap
-from jax._src.prng import PRNGKeyArray
 import jax.numpy as jnp
 import numpy as np
 
@@ -106,7 +106,7 @@ def entropy(
         calib_params: Optional[CalibParams] = None,
         calib_mutable: Optional[CalibMutable] = None,
         n_target_samples: Optional[int] = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
         **kwargs,
     ) -> jnp.ndarray:
@@ -139,7 +139,7 @@ def quantile(
         calib_mutable: Optional[CalibMutable] = None,
         n_target_samples: Optional[int] = 30,
         target_samples: Optional[jnp.ndarray] = None,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
         **kwargs,
     ) -> Union[float, jnp.ndarray]:
@@ -164,7 +164,7 @@ def quantile(
             Number of target samples to sample for each input data point.
         target_samples: Optional[jnp.ndarray] = None
             Samples of the target variable for each input, used to estimate the quantiles.
-        rng: Optional[PRNGKeyArray]
+        rng: Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
diff --git a/fortuna/loss/classification/cross_entropy.py b/fortuna/loss/classification/cross_entropy.py
index ef9b5f1a..2a72ddb6 100644
--- a/fortuna/loss/classification/cross_entropy.py
+++ b/fortuna/loss/classification/cross_entropy.py
@@ -1,9 +1,3 @@
-from typing import (
-    Any,
-    Callable,
-    Tuple,
-)
-
 import jax
 import jax.numpy as jnp
 import jax.scipy as jsp
diff --git a/fortuna/loss/regression/scaled_mse.py b/fortuna/loss/regression/scaled_mse.py
index 9f40d542..e0653a64 100644
--- a/fortuna/loss/regression/scaled_mse.py
+++ b/fortuna/loss/regression/scaled_mse.py
@@ -1,5 +1,3 @@
-from typing import Tuple
-
 import jax.numpy as jnp
 
 from fortuna.typing import Array
diff --git a/fortuna/model/model_manager/base.py b/fortuna/model/model_manager/base.py
index 77f90773..62b8ce34 100755
--- a/fortuna/model/model_manager/base.py
+++ b/fortuna/model/model_manager/base.py
@@ -8,9 +8,8 @@
 )
 
 from flax import linen as nn
-from flax.core import FrozenDict
 from flax.training.checkpoints import PyTree
-from jax._src.prng import PRNGKeyArray
+import jax
 import jax.numpy as jnp
 
 from fortuna.typing import (
@@ -38,7 +37,7 @@ def apply(
         inputs: InputData,
         mutable: Optional[Mutable] = None,
         train: bool = False,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
     ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, PyTree]]:
         """
         Apply the models' forward pass.
@@ -53,7 +52,7 @@ def apply(
             The mutable objects used to evaluate the models.
         train : bool
             Whether the method is called during training.
-        rng: Optional[PRNGKeyArray]
+        rng: Optional[jax.Array]
             A random number generator.
             If not passed,
             this will be taken from the attributes of this class.
@@ -67,7 +66,7 @@ def apply(
 
     @abc.abstractmethod
     def init(
-        self, input_shape: Tuple[int, ...], rng: Optional[PRNGKeyArray] = None, **kwargs
+        self, input_shape: Tuple[int, ...], rng: Optional[jax.Array] = None, **kwargs
     ) -> Dict[str, Mapping]:
         """
         Initialize random parameters and mutable objects.
@@ -76,7 +75,7 @@ def init(
         ----------
         input_shape : Tuple
             The shape of the input variable.
-        rng: Optional[PRNGKeyArray]
+        rng: Optional[jax.Array]
             A random number generator.
             If not passed,
             this will be taken from the attributes of this class.
diff --git a/fortuna/model/model_manager/classification.py b/fortuna/model/model_manager/classification.py
index df4c85fe..ba92d3a0 100644
--- a/fortuna/model/model_manager/classification.py
+++ b/fortuna/model/model_manager/classification.py
@@ -8,12 +8,14 @@
     Union,
 )
 
-from flax.core import FrozenDict
+from flax.core import (
+    FrozenDict,
+    unfreeze,
+)
 import flax.linen as nn
 from flax.training.checkpoints import PyTree
 import jax
 from jax import random
-from jax._src.prng import PRNGKeyArray
 import jax.numpy as jnp
 
 from fortuna.model.model_manager.base import ModelManager
@@ -50,7 +52,7 @@ def apply(
         inputs: Array,
         mutable: Optional[Mutable] = None,
         train: bool = False,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
     ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, PyTree]]:
         variables = params["model"].unfreeze()
 
@@ -97,7 +99,7 @@ def apply_fn(v, x):
         return outputs
 
     def init(
-        self, input_shape: Tuple[int, ...], rng: Optional[PRNGKeyArray] = None, **kwargs
+        self, input_shape: Tuple[int, ...], rng: Optional[jax.Array] = None, **kwargs
     ) -> Dict[str, Mapping]:
         if rng is None:
             rng = self.rng.get()
@@ -215,7 +217,7 @@ def _mean_field_logits(
         """
         # Implementation adapted from https://github.com/google/edward2/blob/520e28285e905e0021e49b52b982ee5ea170071c/edward2/tensorflow/layers/utils.py#L379
         if self.mean_field_factor < 0:
-            raise ValueError(f"mean_field_factor cannot be < 0.")
+            raise ValueError("mean_field_factor cannot be < 0.")
 
         # Compute standard deviation.
         if covariance is None:
@@ -238,7 +240,7 @@ def apply(
         inputs: Array,
         mutable: Optional[Mutable] = None,
         train: bool = False,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
     ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, PyTree]]:
         if mutable and mutable is not None:
             deep_feature_extractor_mutable = {
@@ -294,7 +296,7 @@ def apply_fn(p, x):
             outputs = outputs[0]
             if gp_mutable is not None:
                 mutable = deep_feature_extractor_mutable["mutable"]["model"].unfreeze()
-                mutable.update(gp_mutable.unfreeze())
+                mutable.update(unfreeze(gp_mutable))
                 mutable = {"mutable": FrozenDict({"model": mutable})}
             return outputs, mutable
         else:
@@ -325,7 +327,7 @@ def __init__(self, model: nn.Module, *args, **kwargs):
         super(SNGPClassificationModelManager, self).__init__(model, *args, **kwargs)
 
     def init(
-        self, input_shape: Tuple[int, ...], rng: Optional[PRNGKeyArray] = None, **kwargs
+        self, input_shape: Tuple[int, ...], rng: Optional[jax.Array] = None, **kwargs
     ) -> Dict[str, FrozenDict]:
         if rng is None:
             rng = self.rng.get()
@@ -345,9 +347,7 @@ def init(
             )
         gp_params = self._gp_output_model.init(rngs, jnp.zeros(output_shape), **kwargs)
         params = dict(
-            model=FrozenDict(
-                nested_update(model_params.unfreeze(), gp_params.unfreeze())
-            )
+            model=FrozenDict(nested_update(unfreeze(model_params), unfreeze(gp_params)))
         )
 
         if self.model_editor is not None:
diff --git a/fortuna/model/model_manager/regression.py b/fortuna/model/model_manager/regression.py
index 691aa738..d915c4b1 100644
--- a/fortuna/model/model_manager/regression.py
+++ b/fortuna/model/model_manager/regression.py
@@ -10,7 +10,6 @@
 from flax.training.checkpoints import PyTree
 import jax
 from jax import random
-from jax._src.prng import PRNGKeyArray
 import jax.numpy as jnp
 
 from fortuna.model.model_manager.base import ModelManager
@@ -53,7 +52,7 @@ def apply(
         inputs: Array,
         mutable: Optional[Mutable] = None,
         train: bool = False,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
     ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, PyTree]]:
         # setup dropout key
         if rng is not None:
@@ -141,7 +140,7 @@ def apply_fn(p, x, m_mutable, llv_mutable):
         return outputs
 
     def init(
-        self, input_shape: Tuple, rng: Optional[PRNGKeyArray] = None, **kwargs
+        self, input_shape: Tuple, rng: Optional[jax.Array] = None, **kwargs
     ) -> Dict[str, FrozenDict]:
         if rng is None:
             rng = self.rng.get()
diff --git a/fortuna/model/model_manager/transformers/classification.py b/fortuna/model/model_manager/transformers/classification.py
index 6b793433..7af0118e 100644
--- a/fortuna/model/model_manager/transformers/classification.py
+++ b/fortuna/model/model_manager/transformers/classification.py
@@ -14,7 +14,6 @@
     numpy as jnp,
     random,
 )
-from jax._src.prng import PRNGKeyArray
 
 from fortuna.model.model_manager.classification import (
     ClassificationModelManager,
@@ -37,7 +36,7 @@ def apply(
         inputs: Dict[str, Array],
         mutable: Optional[Mutable] = None,
         train: bool = False,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         **kwargs,
     ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, PyTree]]:
         # setup dropout key
@@ -87,7 +86,7 @@ def apply_fn(p, x):
         return outputs
 
     def init(
-        self, input_shape: Tuple[int, ...], rng: Optional[PRNGKeyArray] = None, **kwargs
+        self, input_shape: Tuple[int, ...], rng: Optional[jax.Array] = None, **kwargs
     ) -> Dict[str, Mapping]:
         assert self.model._is_initialized, (
             "At the moment Fortuna supports models from Hugging Face that are loaded via "
@@ -135,7 +134,7 @@ def __init__(
         )
 
     def init(
-        self, input_shape: Tuple[int, ...], rng: Optional[PRNGKeyArray] = None, **kwargs
+        self, input_shape: Tuple[int, ...], rng: Optional[jax.Array] = None, **kwargs
     ) -> Dict[str, FrozenDict]:
         if rng is None:
             rng = self.rng.get()
diff --git a/fortuna/model/utils/random_features.py b/fortuna/model/utils/random_features.py
index be02e4e7..adb79f9c 100644
--- a/fortuna/model/utils/random_features.py
+++ b/fortuna/model/utils/random_features.py
@@ -12,12 +12,12 @@
 )
 
 import flax.linen as nn
+import jax
 from jax import (
     lax,
     random,
 )
 import jax.numpy as jnp
-from jax.random import PRNGKeyArray
 
 from fortuna.typing import (
     Array,
@@ -29,7 +29,7 @@
 # Default config for random features.
 default_rbf_bias_init = nn.initializers.uniform(scale=2.0 * jnp.pi)
 # Using "he_normal" style random feature distribution (see https://arxiv.org/abs/1502.01852).
-# Effectively, this is equivalent to approximating a RBF kernel but with the input standardized by
+# Effectively, this is equivalent to approximating an RBF kernel but with the input standardized by
 # its dimensionality (i.e., input_scaled = input * sqrt(2. / dim_input)) and
 # empirically leads to better performance for neural network inputs.
 # default_rbf_kernel_init = nn.initializers.variance_scaling(
@@ -154,10 +154,10 @@ class RandomFourierFeatures(nn.Module):
         Scale to apply to the output.
         When using GP layer as the output layer of a nerual network, it is recommended to set this to 1.
         to prevent it from changing the learning rate to the hidden layers.
-    kernel_init:  Callable[[PRNGKeyArray, Shape, Type], Array]
-         Callable[[PRNGKeyArray, Shape, Type], Array] function for the weight matrix.
-    bias_init:  Callable[[PRNGKeyArray, Shape, Type], Array]
-         Callable[[PRNGKeyArray, Shape, Type], Array] function for the bias.
+    kernel_init:  Callable[[jax.Array, Shape, Type], Array]
+         Callable[[jax.Array, Shape, Type], Array] function for the weight matrix.
+    bias_init:  Callable[[jax.Array, Shape, Type], Array]
+         Callable[[jax.Array, Shape, Type], Array] function for the bias.
     seed: int
         Random seed for generating random features. This will override the external RNGs.
     dtype: Type
@@ -167,8 +167,8 @@ class RandomFourierFeatures(nn.Module):
     features: int
     kernel_scale: Optional[float] = 1.0
     feature_scale: Optional[float] = 1.0
-    kernel_init: Callable[[PRNGKeyArray, Shape, Type], Array] = default_rbf_kernel_init
-    bias_init: Callable[[PRNGKeyArray, Shape, Type], Array] = default_rbf_bias_init
+    kernel_init: Callable[[jax.Array, Shape, Type], Array] = default_rbf_kernel_init
+    bias_init: Callable[[jax.Array, Shape, Type], Array] = default_rbf_bias_init
     seed: int = 0
     dtype: Type = jnp.float32
     collection_name: str = "random_features"
diff --git a/fortuna/model/utils/spectral_norm.py b/fortuna/model/utils/spectral_norm.py
index 7da95823..6c8da7e6 100644
--- a/fortuna/model/utils/spectral_norm.py
+++ b/fortuna/model/utils/spectral_norm.py
@@ -16,7 +16,6 @@
 import flax.linen as nn
 import jax
 import jax.numpy as jnp
-from jax.random import PRNGKeyArray
 import numpy as np
 
 from fortuna.typing import (
@@ -44,9 +43,9 @@ class SpectralNormalization(nn.Module):
     norm_multiplier: float
         Multiplicative constant to threshold the normalization.
         Usually under normalization, the singular value will converge to this value.
-    u_init: Callable[[PRNGKeyArray, Shape, Type], Array]
+    u_init: Callable[[jax.Array, Shape, Type], Array]
         Initializer function for the first left singular vectors of the kernel.
-    v_init: Callable[[PRNGKeyArray, Shape, Type], Array]
+    v_init: Callable[[jax.Array, Shape, Type], Array]
         Initializer function for the first right singular vectors of the kernel.
     kernel_apply_kwargs: Optional[Mapping[str, Any]]
         Updated keyword arguments to clone the input layer.
@@ -70,10 +69,10 @@ class SpectralNormalization(nn.Module):
     layer: nn.Module
     iteration: int = 1
     norm_multiplier: float = 0.95
-    u_init: Callable[[PRNGKeyArray, Shape, Type], Array] = nn.initializers.normal(
+    u_init: Callable[[jax.Array, Shape, Type], Array] = nn.initializers.normal(
         stddev=0.05
     )
-    v_init: Callable[[PRNGKeyArray, Shape, Type], Array] = nn.initializers.normal(
+    v_init: Callable[[jax.Array, Shape, Type], Array] = nn.initializers.normal(
         stddev=0.05
     )
     kernel_apply_kwargs: Optional[Mapping[str, Any]] = None
diff --git a/fortuna/model_editor/base.py b/fortuna/model_editor/base.py
index c24b8c65..d4e58cb3 100644
--- a/fortuna/model_editor/base.py
+++ b/fortuna/model_editor/base.py
@@ -2,7 +2,6 @@
     Any,
     Callable,
     Dict,
-    Optional,
     Tuple,
     Union,
 )
@@ -10,10 +9,7 @@
 import flax.linen as nn
 import jax.numpy as jnp
 
-from fortuna.typing import (
-    InputData,
-    Mutable,
-)
+from fortuna.typing import InputData
 
 
 class ModelEditor(nn.Module):
diff --git a/fortuna/output_calib_model/config/monitor.py b/fortuna/output_calib_model/config/monitor.py
index ae9b0550..369a2845 100644
--- a/fortuna/output_calib_model/config/monitor.py
+++ b/fortuna/output_calib_model/config/monitor.py
@@ -66,7 +66,7 @@ def __init__(
                         f"All metrics in `metrics` must be callable objects, but {metric} is not."
                     )
         if uncertainty_fn is not None and not callable(uncertainty_fn):
-            raise ValueError(f"`uncertainty_fn` must be a a callable function.")
+            raise ValueError("`uncertainty_fn` must be a a callable function.")
 
         self.metrics = metrics
         self.uncertainty_fn = uncertainty_fn
diff --git a/fortuna/output_calib_model/loss.py b/fortuna/output_calib_model/loss.py
index 529dc8cd..cac3eb05 100644
--- a/fortuna/output_calib_model/loss.py
+++ b/fortuna/output_calib_model/loss.py
@@ -7,7 +7,7 @@
     Union,
 )
 
-from jax._src.prng import PRNGKeyArray
+import jax
 import jax.numpy as jnp
 
 from fortuna.output_calib_model.predictive.base import Predictive
@@ -34,7 +34,7 @@ def __call__(
         targets: Array,
         outputs: Array,
         mutable: Optional[CalibMutable] = None,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         return_aux: Optional[List[str]] = None,
     ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, Any]]:
         if return_aux is None:
diff --git a/fortuna/output_calib_model/output_calib_model_calibrator.py b/fortuna/output_calib_model/output_calib_model_calibrator.py
index d18ea327..2dfb5a93 100644
--- a/fortuna/output_calib_model/output_calib_model_calibrator.py
+++ b/fortuna/output_calib_model/output_calib_model_calibrator.py
@@ -20,7 +20,6 @@
     random,
     value_and_grad,
 )
-from jax._src.prng import PRNGKeyArray
 import jax.numpy as jnp
 from jax.tree_util import tree_map
 from tqdm import trange
@@ -85,7 +84,7 @@ def __init__(
 
     def train(
         self,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         state: OutputCalibState,
         loss_fun: Callable,
         n_epochs: int = 1,
@@ -175,7 +174,7 @@ def _training_loop(
         metrics: Optional[
             Tuple[Callable[[jnp.ndarray, jnp.ndarray, Array], Array], ...]
         ],
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         state: OutputCalibState,
         targets: Array,
         outputs: Array,
@@ -228,7 +227,7 @@ def training_step(
         targets: Array,
         outputs: Array,
         loss_fun: Callable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
     ) -> Tuple[OutputCalibState, Dict[str, Any]]:
         # ensure to use a different key at each step
         model_key = random.fold_in(rng, state.step)
@@ -264,7 +263,7 @@ def training_loss_step(
         targets: Array,
         outputs: Array,
         mutable: CalibMutable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
     ) -> Tuple[jnp.ndarray, Dict[str, Any]]:
         return_aux = ["outputs"]
         if mutable is not None:
@@ -340,7 +339,7 @@ def _val_loop(
         metrics: Optional[
             Tuple[Callable[[jnp.ndarray, jnp.ndarray, Array], Array], ...]
         ],
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         state: OutputCalibState,
         targets: Array,
         outputs: Array,
@@ -379,7 +378,7 @@ def val_step(
         targets: Array,
         outputs: Array,
         loss_fun: Callable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         metrics: Optional[
             Tuple[Callable[[jnp.ndarray, jnp.ndarray, Array], Array], ...]
         ] = None,
@@ -394,7 +393,7 @@ def val_loss_step(
         targets: Array,
         outputs: Array,
         loss_fun: Callable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
     ) -> Tuple[jnp.ndarray, Dict[str, jnp.ndarray]]:
         loss, aux = loss_fun(
             params=state.params,
@@ -473,8 +472,8 @@ def on_train_start(
         state: OutputCalibState,
         targets: List[Array],
         outputs: List[Array],
-        rng: PRNGKeyArray,
-    ) -> Tuple[OutputCalibState, List[Array], List[Array], PRNGKeyArray]:
+        rng: jax.Array,
+    ) -> Tuple[OutputCalibState, List[Array], List[Array], jax.Array]:
         return state, targets, outputs, rng
 
     def on_train_end(self, state: OutputCalibState) -> OutputCalibState:
@@ -512,7 +511,7 @@ def training_step(
         targets: Array,
         outputs: Array,
         loss_fun: Callable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
     ) -> Tuple[OutputCalibState, Dict[str, Any]]:
         return super().training_step(state, targets, outputs, loss_fun, rng)
 
@@ -523,7 +522,7 @@ def val_loss_step(
         targets: Array,
         outputs: Array,
         loss_fun: Callable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
     ) -> Dict[str, jnp.ndarray]:
         return super().val_loss_step(state, targets, outputs, loss_fun, rng)
 
@@ -580,8 +579,8 @@ def on_train_start(
         state: OutputCalibState,
         targets: List[Array],
         outputs: List[Array],
-        rng: PRNGKeyArray,
-    ) -> Tuple[OutputCalibState, List[DataLoader], List[TargetsLoader], PRNGKeyArray]:
+        rng: jax.Array,
+    ) -> Tuple[OutputCalibState, List[DataLoader], List[TargetsLoader], jax.Array]:
         state, targets, outputs, rng = super(MultiDeviceMixin, self).on_train_start(
             state, targets, outputs, rng
         )
@@ -608,7 +607,7 @@ def training_step(
         targets: Array,
         outputs: Array,
         loss_fun: Callable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
     ) -> Tuple[OutputCalibState, Dict[str, Any]]:
         return super().training_step(state, targets, outputs, loss_fun, rng)
 
@@ -638,7 +637,7 @@ def val_loss_step(
         targets: Array,
         outputs: Array,
         loss_fun: Callable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
     ) -> Dict[str, jnp.ndarray]:
         val_losses = super().val_loss_step(state, targets, outputs, loss_fun, rng)
         return lax.pmean(val_losses, axis_name="batch")
diff --git a/fortuna/output_calib_model/predictive/base.py b/fortuna/output_calib_model/predictive/base.py
index 0d1b20ef..a0707fbb 100644
--- a/fortuna/output_calib_model/predictive/base.py
+++ b/fortuna/output_calib_model/predictive/base.py
@@ -7,7 +7,7 @@
     Union,
 )
 
-from jax._src.prng import PRNGKeyArray
+import jax
 import jax.numpy as jnp
 
 from fortuna.output_calibrator.output_calib_manager.base import OutputCalibManager
@@ -70,7 +70,7 @@ def sample(
         self,
         n_samples: int,
         outputs: Array,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         calibrated: bool = True,
         **kwargs,
     ) -> jnp.ndarray:
@@ -83,7 +83,7 @@ def sample(
             The number of target samples to draw for each of the outputs.
         outputs : Array
             Calibrated outputs.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         calibrated : bool
             Whether the outputs should be calibrated when computing this method. If `calibrated` is set to True, the
@@ -266,7 +266,7 @@ def _log_joint_prob(
         targets: Array,
         outputs: Array,
         mutable: Optional[CalibMutable] = None,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         return_aux: Optional[List[str]] = None,
     ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, Any]]:
         if return_aux is None:
diff --git a/fortuna/output_calib_model/predictive/regression.py b/fortuna/output_calib_model/predictive/regression.py
index fdef4a55..a4f7ff13 100644
--- a/fortuna/output_calib_model/predictive/regression.py
+++ b/fortuna/output_calib_model/predictive/regression.py
@@ -4,7 +4,7 @@
     Union,
 )
 
-from jax._src.prng import PRNGKeyArray
+import jax
 import jax.numpy as jnp
 
 from fortuna.output_calib_model.predictive.base import Predictive
@@ -29,7 +29,7 @@ def quantile(
         q: Union[float, Array, List],
         outputs: Array,
         n_samples: Optional[int] = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         calibrated: bool = True,
     ) -> jnp.ndarray:
         """
@@ -43,7 +43,7 @@ def quantile(
             Model outputs.
         n_samples: Optional[int]
             Number of target samples to draw when computing quantiles.
-        rng: Optional[PRNGKeyArray]
+        rng: Optional[jax.Array]
             A random number generator.
         calibrated : bool
             Whether the outputs should be calibrated when computing this method. If `calibrated` is set to True, the
@@ -70,7 +70,7 @@ def credible_interval(
         n_samples: int = 30,
         error: float = 0.05,
         interval_type: str = "two-tailed",
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         calibrated: bool = True,
     ) -> jnp.ndarray:
         """
@@ -88,7 +88,7 @@ def credible_interval(
             `error=0.05` corresponds to a 95% level of credibility.
         interval_type: str
             The interval type. We support "two-tailed" (default), "right-tailed" and "left-tailed".
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         calibrated : bool
             Whether the outputs should be calibrated when computing this method. If `calibrated` is set to True, the
diff --git a/fortuna/output_calib_model/state.py b/fortuna/output_calib_model/state.py
index c69352d3..9d10be68 100644
--- a/fortuna/output_calib_model/state.py
+++ b/fortuna/output_calib_model/state.py
@@ -8,7 +8,6 @@
 )
 
 from flax.core import FrozenDict
-import jax.numpy as jnp
 
 from fortuna.training.train_state import TrainState
 from fortuna.typing import (
diff --git a/fortuna/output_calibrator/output_calib_manager/base.py b/fortuna/output_calibrator/output_calib_manager/base.py
index fcc4b50d..069e9ecf 100644
--- a/fortuna/output_calibrator/output_calib_manager/base.py
+++ b/fortuna/output_calibrator/output_calib_manager/base.py
@@ -7,8 +7,8 @@
 from flax.core import FrozenDict
 import flax.linen as nn
 from flax.training.checkpoints import PyTree
+import jax
 from jax import random
-from jax._src.prng import PRNGKeyArray
 import jax.numpy as jnp
 
 from fortuna.typing import (
@@ -29,7 +29,7 @@ def apply(
         outputs: Array,
         mutable: Optional[CalibMutable] = None,
         calib: bool = False,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
     ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, PyTree]]:
         """
         Apply the output calibrator forward pass.
@@ -44,7 +44,7 @@ def apply(
             The mutable objects used to evaluate the models.
         calib : bool
             Whether the method is called during calibration.
-        rng: Optional[PRNGKeyArray]
+        rng: Optional[jax.Array]
             A random number generator.
             If not passed,
             this will be taken from the attributes of this class.
@@ -82,7 +82,7 @@ def apply(
             )
 
     def init(
-        self, output_dim: int, rng: Optional[PRNGKeyArray] = None, **kwargs
+        self, output_dim: int, rng: Optional[jax.Array] = None, **kwargs
     ) -> Optional[FrozenDict]:
         """
         Initialize random parameters and mutable objects.
@@ -91,7 +91,7 @@ def init(
         ----------
         output_dim: int
             The output dimension.
-        rng: Optional[PRNGKeyArray]
+        rng: Optional[jax.Array]
             A random number generator.
             If not passed,
             this will be taken from the attributes of this class.
diff --git a/fortuna/prob_model/calib_config/monitor.py b/fortuna/prob_model/calib_config/monitor.py
index f3a6e4a8..edf67099 100644
--- a/fortuna/prob_model/calib_config/monitor.py
+++ b/fortuna/prob_model/calib_config/monitor.py
@@ -68,7 +68,7 @@ def __init__(
                         f"All metrics in `metrics` must be callable objects, but {metric} is not."
                     )
         if uncertainty_fn is not None and not callable(uncertainty_fn):
-            raise ValueError(f"`uncertainty_fn` must be a a callable function.")
+            raise ValueError("`uncertainty_fn` must be a a callable function.")
 
         self.metrics = metrics
         self.uncertainty_fn = uncertainty_fn
diff --git a/fortuna/prob_model/classification.py b/fortuna/prob_model/classification.py
index 7ee36a46..66bc57ad 100644
--- a/fortuna/prob_model/classification.py
+++ b/fortuna/prob_model/classification.py
@@ -201,7 +201,7 @@ def _get_model_manager(
                     model_editor=model_editor,
                     **posterior_approximator.posterior_method_kwargs,
                 )
-        except ModuleNotFoundError as e:
+        except ModuleNotFoundError:
             logging.warning(
                 "No module named 'transformer' is installed. "
                 "If you are not working with models from the `transformers` library ignore this warning, otherwise "
diff --git a/fortuna/prob_model/joint/base.py b/fortuna/prob_model/joint/base.py
index b113c548..7af4d41a 100755
--- a/fortuna/prob_model/joint/base.py
+++ b/fortuna/prob_model/joint/base.py
@@ -6,7 +6,7 @@
 )
 
 from flax.core import FrozenDict
-from jax._src.prng import PRNGKeyArray
+import jax
 import jax.numpy as jnp
 
 from fortuna.likelihood.base import Likelihood
@@ -62,7 +62,7 @@ def _batched_log_joint_prob(
         return_aux: Optional[List[str]] = None,
         train: Optional[bool] = False,
         outputs: Optional[jnp.ndarray] = None,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         **kwargs,
     ) -> Union[float, Tuple[float, dict]]:
         """
@@ -90,7 +90,7 @@ def _batched_log_joint_prob(
             Whether the method is called during training.
         outputs : Optional[jnp.ndarray]
             Pre-computed batch of outputs.
-        rng: Optional[PRNGKeyArray]
+        rng: Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
 
         Returns
@@ -132,7 +132,7 @@ def _batched_negative_log_joint_prob(
         return_aux: Optional[List[str]] = None,
         train: Optional[bool] = False,
         outputs: Optional[jnp.ndarray] = None,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         **kwargs,
     ) -> Union[float, Tuple[float, dict]]:
         outs = self._batched_log_joint_prob(
diff --git a/fortuna/prob_model/posterior/base.py b/fortuna/prob_model/posterior/base.py
index 0676bb9f..98237d64 100755
--- a/fortuna/prob_model/posterior/base.py
+++ b/fortuna/prob_model/posterior/base.py
@@ -9,7 +9,7 @@
 )
 
 from flax.core import FrozenDict
-from jax._src.prng import PRNGKeyArray
+import jax
 
 from fortuna.data.loader import DataLoader
 from fortuna.prob_model.fit_config.base import FitConfig
@@ -149,13 +149,13 @@ def fit(
         pass
 
     @abc.abstractmethod
-    def sample(self, rng: Optional[PRNGKeyArray] = None, *args, **kwargs) -> JointState:
+    def sample(self, rng: Optional[jax.Array] = None, *args, **kwargs) -> JointState:
         """
         Sample from the posterior distribution.
 
         Parameters
         ----------
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
 
         Returns
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 557614f8..bf72cdb0 100755
--- a/fortuna/prob_model/posterior/deep_ensemble/deep_ensemble_posterior.py
+++ b/fortuna/prob_model/posterior/deep_ensemble/deep_ensemble_posterior.py
@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 import logging
-import os
 import pathlib
 from typing import (
     List,
@@ -11,11 +10,11 @@
 )
 
 from flax.core import FrozenDict
+import jax
 from jax import (
     pure_callback,
     random,
 )
-from jax._src.prng import PRNGKeyArray
 
 from fortuna.data.loader import DataLoader
 from fortuna.prob_model.fit_config.base import FitConfig
@@ -192,7 +191,7 @@ def _fit(i):
         logging.info("Fit completed.")
         return status
 
-    def sample(self, rng: Optional[PRNGKeyArray] = None, **kwargs) -> JointState:
+    def sample(self, rng: Optional[jax.Array] = None, **kwargs) -> JointState:
         if rng is None:
             rng = self.rng.get()
         state = pure_callback(
diff --git a/fortuna/prob_model/posterior/laplace/laplace_posterior.py b/fortuna/prob_model/posterior/laplace/laplace_posterior.py
index 6f56116f..5e6376f9 100755
--- a/fortuna/prob_model/posterior/laplace/laplace_posterior.py
+++ b/fortuna/prob_model/posterior/laplace/laplace_posterior.py
@@ -23,7 +23,6 @@
     pmap,
     vjp,
 )
-from jax._src.prng import PRNGKeyArray
 from jax.flatten_util import ravel_pytree
 import jax.numpy as jnp
 from jax.tree_util import tree_map
@@ -316,7 +315,7 @@ def fit(
 
     def sample(
         self,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         **kwargs,
     ) -> JointState:
         if rng is None:
@@ -426,7 +425,7 @@ def _batched_log_prob(
         batch,
         prior_log_var: float,
         n_posterior_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         **kwargs,
     ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, dict]]:
         import jax.random as random
@@ -472,7 +471,7 @@ def prior_log_variance_tuning(
             )
         elif mode == "marginal_lik":
             raise NotImplementedError(
-                f"Optimizing the prior log variance via marginal likelihood maximization is not yet available."
+                "Optimizing the prior log variance via marginal likelihood maximization is not yet available."
             )
         else:
             raise ValueError(f"Unrecognized mode={mode} for prior log variance tuning.")
diff --git a/fortuna/prob_model/posterior/laplace/laplace_state.py b/fortuna/prob_model/posterior/laplace/laplace_state.py
index 2970811b..97354082 100644
--- a/fortuna/prob_model/posterior/laplace/laplace_state.py
+++ b/fortuna/prob_model/posterior/laplace/laplace_state.py
@@ -9,7 +9,6 @@
 )
 
 from flax.core import FrozenDict
-import jax.numpy as jnp
 
 from fortuna.prob_model.posterior.map.map_state import MAPState
 from fortuna.prob_model.posterior.state import PosteriorState
diff --git a/fortuna/prob_model/posterior/map/map_posterior.py b/fortuna/prob_model/posterior/map/map_posterior.py
index b36013cc..331f0129 100755
--- a/fortuna/prob_model/posterior/map/map_posterior.py
+++ b/fortuna/prob_model/posterior/map/map_posterior.py
@@ -1,7 +1,7 @@
 import logging
 from typing import Optional
 
-from jax._src.prng import PRNGKeyArray
+import jax
 
 from fortuna.data.loader import DataLoader
 from fortuna.prob_model.fit_config.base import FitConfig
@@ -118,7 +118,7 @@ def fit(
         logging.info("Fit completed.")
         return status
 
-    def sample(self, rng: Optional[PRNGKeyArray] = None, **kwargs) -> JointState:
+    def sample(self, rng: Optional[jax.Array] = None, **kwargs) -> JointState:
         state = self.state.get()
         return JointState(
             params=state.params,
diff --git a/fortuna/prob_model/posterior/map/map_state.py b/fortuna/prob_model/posterior/map/map_state.py
index 159233da..9c361ed6 100644
--- a/fortuna/prob_model/posterior/map/map_state.py
+++ b/fortuna/prob_model/posterior/map/map_state.py
@@ -1,7 +1,5 @@
 from __future__ import annotations
 
-import jax.numpy as jnp
-
 from fortuna.prob_model.posterior.state import PosteriorState
 from fortuna.utils.strings import convert_string_to_tuple
 
diff --git a/fortuna/prob_model/posterior/map/map_trainer.py b/fortuna/prob_model/posterior/map/map_trainer.py
index d1d7d955..a99487a1 100644
--- a/fortuna/prob_model/posterior/map/map_trainer.py
+++ b/fortuna/prob_model/posterior/map/map_trainer.py
@@ -10,7 +10,7 @@
 )
 
 from flax.core import FrozenDict
-from jax._src.prng import PRNGKeyArray
+import jax
 import jax.numpy as jnp
 from optax._src.base import PyTree
 
@@ -38,7 +38,7 @@ def training_loss_step(
         params: Params,
         batch: Batch,
         mutable: Mutable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
         unravel: Optional[Callable[[any], PyTree]] = None,
         calib_params: Optional[CalibParams] = None,
@@ -74,7 +74,7 @@ def validation_step(
         state: MAPState,
         batch: Batch,
         loss_fun: Callable[[Any], Union[float, Tuple[float, dict]]],
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
         metrics: Optional[Tuple[Callable[[jnp.ndarray, Array], float], ...]] = None,
         unravel: Optional[Callable[[any], PyTree]] = None,
diff --git a/fortuna/prob_model/posterior/normalizing_flow/advi/advi_architecture.py b/fortuna/prob_model/posterior/normalizing_flow/advi/advi_architecture.py
index 932043d4..a737315a 100644
--- a/fortuna/prob_model/posterior/normalizing_flow/advi/advi_architecture.py
+++ b/fortuna/prob_model/posterior/normalizing_flow/advi/advi_architecture.py
@@ -6,8 +6,8 @@
     Tuple,
 )
 
+import jax
 from jax import random
-from jax._src.prng import PRNGKeyArray
 import jax.numpy as jnp
 
 from fortuna.utils.builtins import HashableMixin
@@ -85,7 +85,7 @@ def backward(
 
     def init_params(
         self,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         mean: Optional[jnp.ndarray] = None,
         log_std: Optional[jnp.ndarray] = None,
     ) -> Dict[str, jnp.ndarray]:
@@ -94,7 +94,7 @@ def init_params(
 
         Parameters
         ----------
-        rng: PRNGKeyArray
+        rng: jax.Array
             Random number generator.
         mean: jnp.ndarray
             If the main model has already been initialized calling `model.init`, the already
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 65ced66f..cde86b88 100755
--- a/fortuna/prob_model/posterior/normalizing_flow/advi/advi_posterior.py
+++ b/fortuna/prob_model/posterior/normalizing_flow/advi/advi_posterior.py
@@ -11,10 +11,9 @@
 )
 
 from flax.core import FrozenDict
-from jax._src.prng import PRNGKeyArray
+import jax
 from jax.flatten_util import ravel_pytree
 import jax.numpy as jnp
-import numpy as np
 
 from fortuna.data.loader import (
     DataLoader,
@@ -267,7 +266,7 @@ def load_state(self, checkpoint_path: Path) -> None:
 
     def sample(
         self,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         input_shape: Optional[Tuple[int, ...]] = None,
         inputs_loader: Optional[InputsLoader] = None,
         inputs: Optional[Array] = None,
@@ -277,7 +276,7 @@ def sample(
         Sample from the posterior distribution. Either `input_shape` or `_inputs_loader` must be passed.
         Parameters
         ----------
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         input_shape: Optional[Tuple[int, ...]]
             Shape of a single input.
diff --git a/fortuna/prob_model/posterior/normalizing_flow/advi/advi_state.py b/fortuna/prob_model/posterior/normalizing_flow/advi/advi_state.py
index 2db8bb78..4efe3d4d 100644
--- a/fortuna/prob_model/posterior/normalizing_flow/advi/advi_state.py
+++ b/fortuna/prob_model/posterior/normalizing_flow/advi/advi_state.py
@@ -6,8 +6,6 @@
     Optional,
 )
 
-import jax.numpy as jnp
-
 from fortuna.prob_model.posterior.normalizing_flow.normalizing_flow_state import (
     NormalizingFlowState,
 )
diff --git a/fortuna/prob_model/posterior/normalizing_flow/advi/advi_trainer.py b/fortuna/prob_model/posterior/normalizing_flow/advi/advi_trainer.py
index 995276dd..a2fa1612 100644
--- a/fortuna/prob_model/posterior/normalizing_flow/advi/advi_trainer.py
+++ b/fortuna/prob_model/posterior/normalizing_flow/advi/advi_trainer.py
@@ -5,7 +5,6 @@
 
 from flax.core import FrozenDict
 import jax
-from jax._src.prng import PRNGKeyArray
 from jax.flatten_util import ravel_pytree
 from jax.tree_util import tree_map
 
@@ -97,8 +96,8 @@ def on_train_start(
         self,
         state: NormalizingFlowState,
         dataloaders: List[DataLoader],
-        rng: PRNGKeyArray,
-    ) -> Tuple[NormalizingFlowState, List[DataLoader], PRNGKeyArray]:
+        rng: jax.Array,
+    ) -> Tuple[NormalizingFlowState, List[DataLoader], jax.Array]:
         if self.freeze_fun is not None:
             frozen_paths = get_frozen_paths(
                 self._unravel(state.params["mean"]), self.freeze_fun
diff --git a/fortuna/prob_model/posterior/normalizing_flow/normalizing_flow_trainer.py b/fortuna/prob_model/posterior/normalizing_flow/normalizing_flow_trainer.py
index be6a237e..eeb76fcf 100644
--- a/fortuna/prob_model/posterior/normalizing_flow/normalizing_flow_trainer.py
+++ b/fortuna/prob_model/posterior/normalizing_flow/normalizing_flow_trainer.py
@@ -12,11 +12,11 @@
 )
 
 from flax.core import FrozenDict
+import jax
 from jax import (
     random,
     vmap,
 )
-from jax._src.prng import PRNGKeyArray
 import jax.numpy as jnp
 from jax.tree_util import tree_map
 from optax._src.base import PyTree
@@ -32,7 +32,6 @@
     CalibParams,
     Mutable,
     Params,
-    Path,
 )
 from fortuna.utils.strings import encode_tuple_of_lists_of_strings_to_numpy
 
@@ -48,7 +47,7 @@ def sample_forward(self, rng: jnp.array, params: any, n_samples: int) -> tuple:
 
         Parameters
         ----------
-        rng: PRNGKeyArray
+        rng: jax.Array
             Random number generator.
         params: Params
             Transformation parameters.
@@ -99,7 +98,7 @@ def training_loss_step(
         params: Params,
         batch: Batch,
         mutable: Mutable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
         unravel: Optional[Callable[[any], PyTree]] = None,
         calib_params: Optional[CalibParams] = None,
@@ -197,7 +196,7 @@ def validation_step(
         state: PosteriorState,
         batch: Batch,
         loss_fun: Callable[[Any], Union[float, Tuple[float, dict]]],
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
         metrics: Optional[Tuple[Callable[[jnp.ndarray, Array], float]], ...] = None,
         unravel: Optional[Callable[[any], PyTree]] = None,
diff --git a/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_callback.py b/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_callback.py
index fed81699..16aebc57 100644
--- a/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_callback.py
+++ b/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_callback.py
@@ -1,8 +1,6 @@
 from fortuna.prob_model.posterior.sgmcmc.sgmcmc_sampling_callback import (
     SGMCMCSamplingCallback,
 )
-from fortuna.training.callback import Callback
-from fortuna.training.train_state import TrainState
 from fortuna.training.train_state_repository import TrainStateRepository
 from fortuna.training.trainer import TrainerABC
 
diff --git a/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_integrator.py b/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_integrator.py
index a6e525c6..2843f8f0 100644
--- a/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_integrator.py
+++ b/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_integrator.py
@@ -1,7 +1,6 @@
 from typing import NamedTuple
 
 import jax
-from jax._src.prng import PRNGKeyArray
 import optax
 from optax import GradientTransformation
 
@@ -20,7 +19,7 @@ class OptaxCyclicalSGLDState(NamedTuple):
 
 
 def cyclical_sgld_integrator(
-    rng_key: PRNGKeyArray,
+    rng_key: jax.Array,
     init_step_size: float,
     cycle_length: int,
     exploration_ratio: float,
@@ -30,7 +29,7 @@ def cyclical_sgld_integrator(
 
     Parameters
     ----------
-        rng_key: PRNGKeyArray
+        rng_key: jax.Array
             An initial random number generator.
         init_step_size: float
             The initial step 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 780817c9..59c945d4 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
@@ -96,7 +96,7 @@ def fit(
             )
 
         if fit_config.optimizer.method is not None:
-            logging.info(f"`FitOptimizer` method in CyclicalSGLD is ignored.")
+            logging.info("`FitOptimizer` method in CyclicalSGLD is ignored.")
 
         fit_config.optimizer.method = cyclical_sgld_integrator(
             rng_key=self.rng.get(),
@@ -171,7 +171,7 @@ def fit(
             keep_top_n_checkpoints=fit_config.checkpointer.keep_top_n_checkpoints,
         )
 
-        logging.info(f"Run CyclicalSGLD.")
+        logging.info("Run CyclicalSGLD.")
         state, status = trainer.train(
             rng=self.rng.get(),
             state=state,
diff --git a/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_state.py b/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_state.py
index 5aec1731..de078074 100644
--- a/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_state.py
+++ b/fortuna/prob_model/posterior/sgmcmc/cyclical_sgld/cyclical_sgld_state.py
@@ -7,8 +7,6 @@
     Tuple,
 )
 
-import jax.numpy as jnp
-
 from fortuna.prob_model.posterior.map.map_state import MAPState
 from fortuna.prob_model.posterior.state import PosteriorState
 from fortuna.typing import (
diff --git a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_approximator.py b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_approximator.py
index 02083a06..b28a8abf 100644
--- a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_approximator.py
+++ b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_approximator.py
@@ -57,7 +57,7 @@ def __init__(
         if isinstance(step_schedule, float):
             step_schedule = constant_schedule(step_schedule)
         elif not callable(step_schedule):
-            raise ValueError(f"`step_schedule` must be a a callable function.")
+            raise ValueError("`step_schedule` must be a a callable function.")
         self.burnin_length = burnin_length
         self.momentum_decay = momentum_decay
         self.step_schedule = step_schedule
diff --git a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_callback.py b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_callback.py
index c9dfa12e..30ad00e2 100644
--- a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_callback.py
+++ b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_callback.py
@@ -1,8 +1,6 @@
 from fortuna.prob_model.posterior.sgmcmc.sgmcmc_sampling_callback import (
     SGMCMCSamplingCallback,
 )
-from fortuna.training.callback import Callback
-from fortuna.training.train_state import TrainState
 from fortuna.training.train_state_repository import TrainStateRepository
 from fortuna.training.trainer import TrainerABC
 
diff --git a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_integrator.py b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_integrator.py
index 769ad617..5e3ca9ed 100644
--- a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_integrator.py
+++ b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_integrator.py
@@ -4,7 +4,6 @@
 )
 
 import jax
-from jax._src.prng import PRNGKeyArray
 import jax.numpy as jnp
 from optax import GradientTransformation
 from optax._src.base import PyTree
@@ -22,7 +21,7 @@ class OptaxSGHMCState(NamedTuple):
     """Optax state for the SGHMC integrator."""
 
     count: Array
-    rng_key: PRNGKeyArray
+    rng_key: jax.Array
     momentum: PyTree
     preconditioner_state: PreconditionerState
 
@@ -30,7 +29,7 @@ class OptaxSGHMCState(NamedTuple):
 def sghmc_integrator(
     momentum_decay: float,
     momentum_resample_steps: Optional[int],
-    rng_key: PRNGKeyArray,
+    rng_key: jax.Array,
     step_schedule: StepSchedule,
     preconditioner: Preconditioner,
 ) -> GradientTransformation:
@@ -40,7 +39,7 @@ def sghmc_integrator(
     ----------
         momentum_decay: float
             The momentum decay parameter.
-        rng_key: PRNGKeyArray
+        rng_key: jax.Array
             An initial random number generator.
         step_schedule: StepSchedule
             A function that takes training step as input and returns the step size.
diff --git a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_posterior.py b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_posterior.py
index 59ab9464..cb84d464 100644
--- a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_posterior.py
+++ b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_posterior.py
@@ -7,7 +7,6 @@
 from fortuna.data.loader import DataLoader
 from fortuna.prob_model.fit_config.base import FitConfig
 from fortuna.prob_model.joint.base import Joint
-from fortuna.prob_model.posterior.map.map_posterior import MAPPosterior
 from fortuna.prob_model.posterior.map.map_state import MAPState
 from fortuna.prob_model.posterior.map.map_trainer import (
     JittedMAPTrainer,
@@ -93,7 +92,7 @@ def fit(
             )
 
         if fit_config.optimizer.method is not None:
-            logging.info(f"`FitOptimizer` method in SGHMC is ignored.")
+            logging.info("`FitOptimizer` method in SGHMC is ignored.")
 
         fit_config.optimizer.method = sghmc_integrator(
             momentum_decay=self.posterior_approximator.momentum_decay,
@@ -167,7 +166,7 @@ def fit(
             keep_top_n_checkpoints=fit_config.checkpointer.keep_top_n_checkpoints,
         )
 
-        logging.info(f"Run SGHMC.")
+        logging.info("Run SGHMC.")
         state, status["sghmc"] = trainer.train(
             rng=self.rng.get(),
             state=state,
diff --git a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_state.py b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_state.py
index 28605cb4..836e7bd4 100644
--- a/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_state.py
+++ b/fortuna/prob_model/posterior/sgmcmc/sghmc/sghmc_state.py
@@ -7,8 +7,6 @@
     Tuple,
 )
 
-import jax.numpy as jnp
-
 from fortuna.prob_model.posterior.map.map_state import MAPState
 from fortuna.prob_model.posterior.state import PosteriorState
 from fortuna.typing import (
diff --git a/fortuna/prob_model/posterior/sgmcmc/sgmcmc_posterior.py b/fortuna/prob_model/posterior/sgmcmc/sgmcmc_posterior.py
index ac48e64c..b6de41e2 100644
--- a/fortuna/prob_model/posterior/sgmcmc/sgmcmc_posterior.py
+++ b/fortuna/prob_model/posterior/sgmcmc/sgmcmc_posterior.py
@@ -5,11 +5,11 @@
     Type,
 )
 
+import jax
 from jax import (
     pure_callback,
     random,
 )
-from jax._src.prng import PRNGKeyArray
 
 from fortuna.prob_model.fit_config.base import FitConfig
 from fortuna.prob_model.joint.state import JointState
@@ -27,7 +27,7 @@ class SGMCMCPosterior(Posterior):
 
     def sample(
         self,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         **kwargs,
     ) -> JointState:
         """
@@ -35,7 +35,7 @@ def sample(
 
         Parameters
         ----------
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
 
         Returns
diff --git a/fortuna/prob_model/posterior/sgmcmc/sgmcmc_sampling_callback.py b/fortuna/prob_model/posterior/sgmcmc/sgmcmc_sampling_callback.py
index 7641ba84..77a40d1f 100644
--- a/fortuna/prob_model/posterior/sgmcmc/sgmcmc_sampling_callback.py
+++ b/fortuna/prob_model/posterior/sgmcmc/sgmcmc_sampling_callback.py
@@ -1,5 +1,3 @@
-from typing import Optional
-
 from fortuna.training.callback import Callback
 from fortuna.training.train_state import TrainState
 from fortuna.training.train_state_repository import TrainStateRepository
diff --git a/fortuna/prob_model/posterior/sngp/sngp_posterior.py b/fortuna/prob_model/posterior/sngp/sngp_posterior.py
index 2a40efb1..fc067141 100755
--- a/fortuna/prob_model/posterior/sngp/sngp_posterior.py
+++ b/fortuna/prob_model/posterior/sngp/sngp_posterior.py
@@ -65,7 +65,7 @@ def _check_state(state: PosteriorState) -> None:
         path = find_one_path_to_key(state.mutable, "spectral_stats")
         if len(path) == 0:
             raise ValueError(
-                f"It looks like your deep feature extractor does not have Spectral Normalization, "
-                f"which is required by SNGP. Please include spectral normalization in your model."
-                f"Check out `fortuna.model.utils.spectral_norm.WithSpectralNorm` for more details."
+                "It looks like your deep feature extractor does not have Spectral Normalization, "
+                "which is required by SNGP. Please include spectral normalization in your model."
+                "Check out `fortuna.model.utils.spectral_norm.WithSpectralNorm` for more details."
             )
diff --git a/fortuna/prob_model/posterior/swag/swag_posterior.py b/fortuna/prob_model/posterior/swag/swag_posterior.py
index 81dfec91..d04e58f4 100755
--- a/fortuna/prob_model/posterior/swag/swag_posterior.py
+++ b/fortuna/prob_model/posterior/swag/swag_posterior.py
@@ -4,8 +4,8 @@
 from typing import Optional
 
 from flax.core import FrozenDict
+import jax
 from jax import random
-from jax._src.prng import PRNGKeyArray
 from jax.flatten_util import ravel_pytree
 import jax.numpy as jnp
 
@@ -179,7 +179,7 @@ def fit(
 
     def sample(
         self,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         inputs_loader: Optional[InputsLoader] = None,
         inputs: Optional[Array] = None,
         **kwargs,
@@ -189,7 +189,7 @@ def sample(
 
         Parameters
         ----------
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         inputs_loader: Optional[InputsLoader]
             Input data loader. This or `inputs` is required if the posterior state includes mutable objects.
diff --git a/fortuna/prob_model/predictive/base.py b/fortuna/prob_model/predictive/base.py
index 2aeedf64..e6983d1e 100644
--- a/fortuna/prob_model/predictive/base.py
+++ b/fortuna/prob_model/predictive/base.py
@@ -9,13 +9,13 @@
 )
 
 from flax.training.common_utils import shard_prng_key
+import jax
 from jax import (
     jit,
     lax,
     pmap,
     random,
 )
-from jax._src.prng import PRNGKeyArray
 import jax.numpy as jnp
 import jax.scipy as jsp
 from jax.tree_util import tree_map
@@ -54,7 +54,7 @@ def ensemble_log_prob(
         self,
         data_loader: DataLoader,
         n_posterior_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
         **kwargs,
     ) -> jnp.ndarray:
@@ -76,7 +76,7 @@ def ensemble_log_prob(
         n_posterior_samples : int
             Number of posterior samples to draw in order to compute the log -ikelihood.
             that would be produced using the posterior distribution state.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -105,7 +105,7 @@ def _batched_ensemble_log_prob(
         self,
         batch: Batch,
         n_posterior_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         **kwargs,
     ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, dict]]:
         if rng is None:
@@ -132,7 +132,7 @@ def log_prob(
         self,
         data_loader: DataLoader,
         n_posterior_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
         **kwargs,
     ) -> jnp.ndarray:
@@ -154,7 +154,7 @@ def log_prob(
         n_posterior_samples : int
             Number of posterior samples to draw in order to approximate the predictive log-pdf.
             that would be produced using the posterior distribution state.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -180,7 +180,7 @@ def _batched_log_prob(
         self,
         batch: Batch,
         n_posterior_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         **kwargs,
     ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, dict]]:
         if rng is None:
@@ -211,7 +211,7 @@ def _batched_log_joint_prob(
         ensemble_outputs: Optional[jnp.ndarray] = None,
         calib_params: Optional[CalibParams] = None,
         calib_mutable: Optional[CalibMutable] = None,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         **kwargs,
     ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, Dict]]:
         if return_aux is None:
@@ -295,7 +295,7 @@ def _batched_negative_log_joint_prob(
         ensemble_outputs: Optional[jnp.ndarray] = None,
         calib_params: Optional[CalibParams] = None,
         calib_mutable: Optional[CalibMutable] = None,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         **kwargs,
     ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, Dict]]:
         outs = self._batched_log_joint_prob(
@@ -320,7 +320,7 @@ def sample(
         inputs_loader: InputsLoader,
         n_target_samples: int = 1,
         return_aux: Optional[List[str]] = None,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
         **kwargs,
     ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, Dict[str, jnp.ndarray]]]:
@@ -343,7 +343,7 @@ def sample(
             Number of target samples to sample for each input data point.
         return_aux : Optional[List[str]]
             Return auxiliary objects. We currently support 'outputs'.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -408,7 +408,7 @@ def _batched_sample(
         inputs: Array,
         n_target_samples: int = 1,
         return_aux: Optional[List[str]] = None,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         **kwargs,
     ) -> jnp.ndarray:
         if return_aux is None:
@@ -443,7 +443,7 @@ def sample_calibrated_outputs(
         self,
         inputs_loader: InputsLoader,
         n_output_samples: int = 1,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -455,7 +455,7 @@ def sample_calibrated_outputs(
             A loader of input data points.
         n_output_samples : int
             Number of output samples to draw for each input.
-        rng: Optional[PRNGKeyArray]
+        rng: Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -480,7 +480,7 @@ def _sample_batched_calibrated_outputs(
         self,
         inputs: Array,
         n_output_samples: int = 1,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
     ) -> jnp.ndarray:
         if rng is None:
             rng = self.rng.get()
@@ -502,7 +502,7 @@ def _sample_outputs(
         self,
         inputs_loader: InputsLoader,
         n_output_samples: int = 1,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         if rng is None:
@@ -520,7 +520,7 @@ def _sample_batched_outputs(
         self,
         inputs: Array,
         n_output_samples: int = 1,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
     ) -> jnp.ndarray:
         if rng is None:
             rng = self.rng.get()
@@ -538,7 +538,7 @@ def _sample_outputs_loader(
         self,
         inputs_loader: InputsLoader,
         n_output_samples: int = 1,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         return_size: bool = False,
         distribute: bool = True,
     ) -> Union[TargetsLoader, Tuple[TargetsLoader, int]]:
@@ -583,7 +583,7 @@ def mean(
         self,
         inputs_loader: InputsLoader,
         n_posterior_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -604,7 +604,7 @@ def mean(
             A loader of input data points.
         n_posterior_samples : int
             Number of samples to draw from the posterior distribution for each input.
-        rng: Optional[PRNGKeyArray]
+        rng: Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -625,7 +625,7 @@ def _batched_mean(
         self,
         inputs: Array,
         n_posterior_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
     ) -> jnp.ndarray:
         if rng is None:
             rng = self.rng.get()
@@ -652,7 +652,7 @@ def mode(
         inputs_loader: InputsLoader,
         n_posterior_samples: int = 30,
         means: Optional[jnp.ndarray] = None,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -674,7 +674,7 @@ def mode(
             Number of samples to draw from the posterior distribution for each input.
         means : Optional[jnp.ndarray] = None
             An estimate of the predictive mean.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -690,7 +690,7 @@ def aleatoric_variance(
         self,
         inputs_loader: InputsLoader,
         n_posterior_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -711,7 +711,7 @@ def aleatoric_variance(
             A loader of input data points.
         n_posterior_samples : int
             Number of samples to draw from the posterior distribution for each input.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -736,7 +736,7 @@ def _batched_aleatoric_variance(
         self,
         inputs: Array,
         n_posterior_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
     ) -> jnp.ndarray:
         if rng is None:
             rng = self.rng.get()
@@ -761,7 +761,7 @@ def epistemic_variance(
         self,
         inputs_loader: InputsLoader,
         n_posterior_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -782,7 +782,7 @@ def epistemic_variance(
             A loader of input data points.
         n_posterior_samples : int
             Number of samples to draw from the posterior distribution for each input.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -807,7 +807,7 @@ def _batched_epistemic_variance(
         self,
         inputs: Array,
         n_posterior_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
     ) -> jnp.ndarray:
         if rng is None:
             rng = self.rng.get()
@@ -841,7 +841,7 @@ def variance(
         n_posterior_samples: int = 30,
         aleatoric_variances: Optional[jnp.ndarray] = None,
         epistemic_variances: Optional[jnp.ndarray] = None,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -867,7 +867,7 @@ def variance(
             An estimate of the aleatoric predictive variance for each input.
         epistemic_variances: Optional[jnp.ndarray]
             An estimate of the epistemic predictive variance for each input.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -902,7 +902,7 @@ def std(
         inputs_loader: InputsLoader,
         n_posterior_samples: int = 30,
         variances: Optional[jnp.ndarray] = None,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -924,7 +924,7 @@ def std(
             Number of samples to draw from the posterior distribution for each input.
         variances: Optional[jnp.ndarray]
             An estimate of the predictive variance.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -954,7 +954,7 @@ def _loop_fun_through_inputs_loader(
         fun: Callable,
         inputs_loader: InputsLoader,
         n_posterior_samples: int,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         distribute: bool = True,
         **kwargs,
     ) -> Array:
@@ -979,7 +979,7 @@ def _loop_fun_through_data_loader(
         fun: Callable,
         data_loader: DataLoader,
         n_posterior_samples: int,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         distribute: bool = True,
         **kwargs,
     ) -> Array:
@@ -1001,7 +1001,7 @@ def _loop_ensemble_fun_through_inputs_loader(
         fun: Callable,
         inputs_loader: InputsLoader,
         n_posterior_samples: int,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         distribute: bool = True,
         **kwargs,
     ) -> Array:
diff --git a/fortuna/prob_model/predictive/classification.py b/fortuna/prob_model/predictive/classification.py
index 5d4c0297..8119cd55 100644
--- a/fortuna/prob_model/predictive/classification.py
+++ b/fortuna/prob_model/predictive/classification.py
@@ -1,11 +1,11 @@
 from typing import Optional
 
+import jax
 from jax import (
     jit,
     lax,
     vmap,
 )
-from jax._src.prng import PRNGKeyArray
 import jax.numpy as jnp
 import jax.scipy as jsp
 import numpy as np
@@ -35,7 +35,7 @@ def mean(
         self,
         inputs_loader: InputsLoader,
         n_posterior_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -56,7 +56,7 @@ def mean(
             A loader of input data points.
         n_posterior_samples : int
             Number of samples to draw from the posterior distribution for each input.
-        rng: Optional[PRNGKeyArray]
+        rng: Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -73,7 +73,7 @@ def mode(
         inputs_loader: InputsLoader,
         n_posterior_samples: int = 30,
         means: Optional[jnp.ndarray] = None,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         if means is None:
@@ -89,7 +89,7 @@ def aleatoric_variance(
         self,
         inputs_loader: InputsLoader,
         n_posterior_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -110,7 +110,7 @@ def aleatoric_variance(
             A loader of input data points.
         n_posterior_samples : int
             Number of samples to draw from the posterior distribution for each input.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -128,7 +128,7 @@ def epistemic_variance(
         self,
         inputs_loader: InputsLoader,
         n_posterior_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
         **kwargs,
     ) -> jnp.ndarray:
@@ -150,7 +150,7 @@ def epistemic_variance(
             A loader of input data points.
         n_posterior_samples : int
             Number of samples to draw from the posterior distribution for each input.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -170,7 +170,7 @@ def variance(
         n_posterior_samples: int = 30,
         aleatoric_variances: Optional[jnp.ndarray] = None,
         epistemic_variances: Optional[jnp.ndarray] = None,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -194,7 +194,7 @@ def variance(
             An estimate of the aleatoric predictive variance.
         epistemic_variances: Optional[jnp.ndarray]
             An estimate of the epistemic predictive variance.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -218,7 +218,7 @@ def std(
         inputs_loader: InputsLoader,
         n_posterior_samples: int = 30,
         variances: Optional[jnp.ndarray] = None,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -240,7 +240,7 @@ def std(
             Number of samples to draw from the posterior distribution for each input.
         variances: Optional[jnp.ndarray]
             An estimate of the predictive variance.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -258,7 +258,7 @@ def aleatoric_entropy(
         self,
         inputs_loader: InputsLoader,
         n_posterior_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -279,7 +279,7 @@ def aleatoric_entropy(
             A loader of input data points.
         n_posterior_samples : int
             Number of samples to draw from the posterior distribution for each input.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -313,7 +313,7 @@ def epistemic_entropy(
         self,
         inputs_loader: InputsLoader,
         n_posterior_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -338,7 +338,7 @@ def epistemic_entropy(
             A loader of input data points.
         n_posterior_samples : int
             Number of samples to draw from the posterior distribution for each input.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -377,7 +377,7 @@ def entropy(
         self,
         inputs_loader: InputsLoader,
         n_posterior_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -398,7 +398,7 @@ def entropy(
             A loader of input data points.
         n_posterior_samples : int
             Number of samples to draw from the posterior distribution for each input.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -436,7 +436,7 @@ def credible_set(
         inputs_loader: InputsLoader,
         n_posterior_samples: int = 30,
         error: float = 0.05,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -452,7 +452,7 @@ def credible_set(
         error: float
             The set error. This must be a number between 0 and 1, extremes included. For example,
             `error=0.05` corresponds to a 95% level of credibility.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -488,7 +488,7 @@ def conformal_set(
         test_inputs_loader: InputsLoader,
         n_posterior_samples: int = 30,
         error: float = 0.05,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
         return_ess: bool = False,
     ) -> jnp.ndarray:
@@ -506,7 +506,7 @@ def conformal_set(
         error: float
             The set error. This must be a number between 0 and 1, extremes included. For example,
             `error=0.05` corresponds to a 95% level of confidence.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
diff --git a/fortuna/prob_model/predictive/regression.py b/fortuna/prob_model/predictive/regression.py
index c66d5380..4eef7dce 100644
--- a/fortuna/prob_model/predictive/regression.py
+++ b/fortuna/prob_model/predictive/regression.py
@@ -4,12 +4,12 @@
     Union,
 )
 
+import jax
 from jax import (
     lax,
     random,
     vmap,
 )
-from jax._src.prng import PRNGKeyArray
 import jax.numpy as jnp
 import jax.scipy as jsp
 
@@ -36,7 +36,7 @@ def mode(
         inputs_loader: InputsLoader,
         n_posterior_samples: int = 30,
         means: Optional[jnp.ndarray] = None,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         if means is not None:
@@ -53,7 +53,7 @@ def aleatoric_entropy(
         inputs_loader: InputsLoader,
         n_posterior_samples: int = 30,
         n_target_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -76,7 +76,7 @@ def aleatoric_entropy(
             Number of target samples to draw for each input.
         n_posterior_samples : int
             Number of samples to draw from the posterior distribution for each input.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -120,7 +120,7 @@ def epistemic_entropy(
         inputs_loader: InputsLoader,
         n_posterior_samples: int = 30,
         n_target_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -147,7 +147,7 @@ def epistemic_entropy(
             Number of samples to draw from the posterior distribution for each input.
         n_target_samples: int
             Number of target samples to draw for each input.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -199,7 +199,7 @@ def entropy(
         inputs_loader: InputsLoader,
         n_posterior_samples: int = 30,
         n_target_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -222,7 +222,7 @@ def entropy(
             Number of target samples to draw for each input.
         n_posterior_samples : int
             Number of samples to draw from the posterior distribution for each input.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -272,7 +272,7 @@ def credible_interval(
         n_target_samples: int = 30,
         error: float = 0.05,
         interval_type: str = "two-tailed",
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> jnp.ndarray:
         r"""
@@ -289,7 +289,7 @@ def credible_interval(
             `error=0.05` corresponds to a 95% level of credibility.
         interval_type: str
             The interval type. We support "two-tailed" (default), "right-tailed" and "left-tailed".
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
@@ -333,7 +333,7 @@ def quantile(
         q: Union[float, Array, List],
         inputs_loader: InputsLoader,
         n_target_samples: Optional[int] = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         distribute: bool = True,
     ) -> Union[float, jnp.ndarray]:
         r"""
@@ -347,7 +347,7 @@ def quantile(
             A loader of input data points.
         n_target_samples : int
             Number of target samples to sample for each input data point.
-        rng: Optional[PRNGKeyArray]
+        rng: Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
         distribute: bool
             Whether to distribute computation over multiple devices, if available.
diff --git a/fortuna/prob_model/prior/base.py b/fortuna/prob_model/prior/base.py
index 572504eb..5f9e1dee 100755
--- a/fortuna/prob_model/prior/base.py
+++ b/fortuna/prob_model/prior/base.py
@@ -1,7 +1,7 @@
 import abc
 from typing import Optional
 
-from jax._src.prng import PRNGKeyArray
+import jax
 
 from fortuna.typing import Params
 from fortuna.utils.random import WithRNG
@@ -30,7 +30,7 @@ def log_joint_prob(self, params: Params) -> float:
         pass
 
     @abc.abstractmethod
-    def sample(self, params_like: Params, rng: Optional[PRNGKeyArray] = None) -> Params:
+    def sample(self, params_like: Params, rng: Optional[jax.Array] = None) -> Params:
         """
         Sample parameters from the prior distribution.
 
@@ -38,7 +38,7 @@ def sample(self, params_like: Params, rng: Optional[PRNGKeyArray] = None) -> Par
         ----------
         params_like : PyTree
             An PyTree object with the same structure as the parameters to sample.
-        rng: Optional[PRNGKeyArray]
+        rng: Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
 
         Returns
diff --git a/fortuna/prob_model/prior/gaussian.py b/fortuna/prob_model/prior/gaussian.py
index e2b510db..389b5b2f 100644
--- a/fortuna/prob_model/prior/gaussian.py
+++ b/fortuna/prob_model/prior/gaussian.py
@@ -1,7 +1,7 @@
 from typing import Optional
 
+import jax
 from jax import random
-from jax._src.prng import PRNGKeyArray
 from jax.flatten_util import ravel_pytree
 import jax.numpy as jnp
 
@@ -31,7 +31,7 @@ def log_joint_prob(self, params: Params) -> float:
         n = len(rav)
         return -0.5 * (self.prec * jnp.sum(rav**2) + n * (self.log2pi + self.log_var))
 
-    def sample(self, params_like: Params, rng: Optional[PRNGKeyArray] = None) -> Params:
+    def sample(self, params_like: Params, rng: Optional[jax.Array] = None) -> Params:
         dummy_rav, unravel = ravel_pytree(params_like)
         n = len(dummy_rav)
         if rng is None:
@@ -60,7 +60,7 @@ def log_joint_prob(self, params: Params) -> float:
             jnp.exp(-self.log_var) * rav**2 + self.log2pi + self.log_var
         )
 
-    def sample(self, params_like: Params, rng: Optional[PRNGKeyArray] = None) -> Params:
+    def sample(self, params_like: Params, rng: Optional[jax.Array] = None) -> Params:
         dummy_rav, unravel = ravel_pytree(params_like)
         n = len(dummy_rav)
         if rng is None:
diff --git a/fortuna/prob_model/prob_model_calibrator.py b/fortuna/prob_model/prob_model_calibrator.py
index 24cc1692..0b77a255 100644
--- a/fortuna/prob_model/prob_model_calibrator.py
+++ b/fortuna/prob_model/prob_model_calibrator.py
@@ -8,7 +8,6 @@
 
 from flax import jax_utils
 import jax
-from jax._src.prng import PRNGKeyArray
 import jax.numpy as jnp
 from jax.tree_util import tree_map
 
@@ -35,7 +34,7 @@ def training_loss_step(
         batch: Batch,
         outputs: Array,
         mutable: CalibMutable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
     ) -> Tuple[jnp.ndarray, Dict[str, Any]]:
         return_aux = ["outputs"]
@@ -66,7 +65,7 @@ def val_loss_step(
         batch: Batch,
         outputs: Array,
         loss_fun: Callable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
     ) -> Tuple[jnp.ndarray, Dict[str, jnp.ndarray]]:
         loss, aux = loss_fun(
diff --git a/fortuna/prob_output_layer/base.py b/fortuna/prob_output_layer/base.py
index ff5ec0e7..94236665 100755
--- a/fortuna/prob_output_layer/base.py
+++ b/fortuna/prob_output_layer/base.py
@@ -1,7 +1,7 @@
 import abc
 from typing import Optional
 
-from jax._src.prng import PRNGKeyArray
+import jax
 import jax.numpy as jnp
 
 from fortuna.typing import Array
@@ -56,7 +56,7 @@ def sample(
         self,
         n_target_samples: int,
         outputs: Array,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         **kwargs,
     ) -> jnp.ndarray:
         """
@@ -68,7 +68,7 @@ def sample(
             The number of target samples to draw for each of the outputs.
         outputs : Array
             Calibrated outputs.
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
 
         Returns
diff --git a/fortuna/prob_output_layer/classification.py b/fortuna/prob_output_layer/classification.py
index 67b4bfbc..c98225da 100644
--- a/fortuna/prob_output_layer/classification.py
+++ b/fortuna/prob_output_layer/classification.py
@@ -5,7 +5,6 @@
     random,
     vmap,
 )
-from jax._src.prng import PRNGKeyArray
 import jax.numpy as jnp
 import jax.scipy as jsp
 
@@ -35,7 +34,7 @@ def sample(
         self,
         n_target_samples: int,
         outputs: Array,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         **kwargs,
     ) -> jnp.ndarray:
         probs = (
diff --git a/fortuna/prob_output_layer/regression.py b/fortuna/prob_output_layer/regression.py
index ff9a08d7..6577fb81 100644
--- a/fortuna/prob_output_layer/regression.py
+++ b/fortuna/prob_output_layer/regression.py
@@ -4,11 +4,11 @@
     Union,
 )
 
+import jax
 from jax import (
     random,
     vmap,
 )
-from jax._src.prng import PRNGKeyArray
 import jax.numpy as jnp
 
 from fortuna.prob_output_layer.base import ProbOutputLayer
@@ -40,7 +40,7 @@ def sample(
         self,
         n_target_samples: int,
         outputs: Array,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         **kwargs,
     ) -> jnp.ndarray:
         if rng is None:
@@ -55,7 +55,7 @@ def quantile(
         q: Union[float, Array, List],
         outputs: Array,
         n_target_samples: Optional[int] = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
     ) -> jnp.ndarray:
         if type(q) == list:
             q = jnp.array(q)
@@ -77,7 +77,7 @@ def entropy(
         self,
         outputs: Array,
         n_target_samples: int = 30,
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
         **kwargs,
     ) -> jnp.ndarray:
         samples = self.sample(n_target_samples, outputs, rng=rng, **kwargs)
@@ -94,7 +94,7 @@ def credible_interval(
         n_target_samples: int = 30,
         error: float = 0.05,
         interval_type: str = "two-tailed",
-        rng: Optional[PRNGKeyArray] = None,
+        rng: Optional[jax.Array] = None,
     ) -> jnp.ndarray:
         r"""
         Estimate credible intervals for the target variable. This is supported only if the target variable is scalar.
@@ -110,7 +110,7 @@ def credible_interval(
             `error=0.05` corresponds to a 95% level of credibility.
         interval_type: str
             The interval type. We support "two-tailed" (default), "right-tailed" and "left-tailed".
-        rng : Optional[PRNGKeyArray]
+        rng : Optional[jax.Array]
             A random number generator. If not passed, this will be taken from the attributes of this class.
 
         Returns
diff --git a/fortuna/training/mixin.py b/fortuna/training/mixin.py
index 353c8aa6..3147da60 100755
--- a/fortuna/training/mixin.py
+++ b/fortuna/training/mixin.py
@@ -109,7 +109,7 @@ def __init__(
         if early_stopping_patience is None or early_stopping_patience <= 0:
             if early_stopping_verbose:
                 logging.info(
-                    f"Early stopping not enabled. Set `early_stopping_patience>=0` to enable it."
+                    "Early stopping not enabled. Set `early_stopping_patience>=0` to enable it."
                 )
         elif self.early_stopping_mode is None or self.early_stopping_mode not in (
             "min",
@@ -146,9 +146,8 @@ def early_stopping_update(
             early_stopping_monitor = validation_metrics[self.early_stopping_monitor]
             if self.early_stopping_mode == "max":
                 early_stopping_monitor = -early_stopping_monitor
-            improved, self._early_stopping = self._early_stopping.update(
-                early_stopping_monitor
-            )
+            self._early_stopping = self._early_stopping.update(early_stopping_monitor)
+            improved = self._early_stopping.has_improved
         return improved
 
 
diff --git a/fortuna/training/output_calibrator.py b/fortuna/training/output_calibrator.py
index ca6e7373..7e7a7740 100644
--- a/fortuna/training/output_calibrator.py
+++ b/fortuna/training/output_calibrator.py
@@ -20,7 +20,6 @@
     random,
     value_and_grad,
 )
-from jax._src.prng import PRNGKeyArray
 import jax.numpy as jnp
 from jax.tree_util import tree_map
 from tqdm import trange
@@ -82,7 +81,7 @@ def __init__(
 
     def train(
         self,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         state: OutputCalibState,
         loss_fun: Callable,
         training_data_loader: DataLoader,
@@ -178,7 +177,7 @@ def _training_loop(
         metrics: Optional[
             Tuple[Callable[[jnp.ndarray, jnp.ndarray, Array], Array], ...]
         ],
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         state: OutputCalibState,
         training_data_loader: DataLoader,
         calib_outputs_loader: TargetsLoader,
@@ -237,7 +236,7 @@ def training_step(
         batch: Batch,
         outputs: Array,
         loss_fun: Callable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
     ) -> Tuple[OutputCalibState, Dict[str, Any]]:
         # ensure to use a different key at each step
@@ -270,7 +269,7 @@ def training_loss_step(
         batch: Batch,
         outputs: Array,
         mutable: CalibMutable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
     ) -> Tuple[jnp.ndarray, Dict[str, Any]]:
         pass
@@ -328,7 +327,7 @@ def _val_loop(
         metrics: Optional[
             Tuple[Callable[[jnp.ndarray, jnp.ndarray, Array], Array], ...]
         ],
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         state: OutputCalibState,
         val_data_loader: DataLoader,
         val_outputs_loader: TargetsLoader,
@@ -370,7 +369,7 @@ def val_step(
         batch: Batch,
         outputs: Array,
         loss_fun: Callable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
         metrics: Optional[
             Tuple[Callable[[jnp.ndarray, jnp.ndarray, Array], Array], ...]
@@ -387,7 +386,7 @@ def val_loss_step(
         batch: Batch,
         outputs: Array,
         loss_fun: Callable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
     ) -> Tuple[jnp.ndarray, Dict[str, jnp.ndarray]]:
         pass
@@ -459,8 +458,8 @@ def on_train_start(
         state: OutputCalibState,
         data_loaders: List[DataLoader],
         outputs_loaders: List[TargetsLoader],
-        rng: PRNGKeyArray,
-    ) -> Tuple[OutputCalibState, List[DataLoader], List[TargetsLoader], PRNGKeyArray]:
+        rng: jax.Array,
+    ) -> Tuple[OutputCalibState, List[DataLoader], List[TargetsLoader], jax.Array]:
         return state, data_loaders, outputs_loaders, rng
 
     def on_train_end(self, state: OutputCalibState) -> OutputCalibState:
@@ -498,7 +497,7 @@ def training_step(
         batch: Batch,
         outputs: Array,
         loss_fun: Callable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
     ) -> Tuple[OutputCalibState, Dict[str, Any]]:
         return super().training_step(state, batch, outputs, loss_fun, rng, n_data)
@@ -510,7 +509,7 @@ def val_loss_step(
         batch: Batch,
         outputs: Array,
         loss_fun: Callable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
     ) -> Dict[str, jnp.ndarray]:
         return super().val_loss_step(state, batch, outputs, loss_fun, rng, n_data)
@@ -616,8 +615,8 @@ def on_train_start(
         state: OutputCalibState,
         data_loaders: List[DataLoader],
         outputs_loaders: List[TargetsLoader],
-        rng: PRNGKeyArray,
-    ) -> Tuple[OutputCalibState, List[DataLoader], List[TargetsLoader], PRNGKeyArray]:
+        rng: jax.Array,
+    ) -> Tuple[OutputCalibState, List[DataLoader], List[TargetsLoader], jax.Array]:
         state, data_loaders, outputs_loaders, rng = super(
             MultiDeviceMixin, self
         ).on_train_start(state, data_loaders, outputs_loaders, rng)
@@ -644,7 +643,7 @@ def training_step(
         batch: Batch,
         outputs: Array,
         loss_fun: Callable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
     ) -> Tuple[OutputCalibState, Dict[str, Any]]:
         return super().training_step(state, batch, outputs, loss_fun, rng, n_data)
@@ -675,7 +674,7 @@ def val_loss_step(
         batch: Batch,
         outputs: Array,
         loss_fun: Callable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
     ) -> Dict[str, jnp.ndarray]:
         val_losses = super().val_loss_step(state, batch, outputs, loss_fun, rng, n_data)
diff --git a/fortuna/training/train_state.py b/fortuna/training/train_state.py
index 0707cdb7..7d45afc5 100644
--- a/fortuna/training/train_state.py
+++ b/fortuna/training/train_state.py
@@ -9,7 +9,6 @@
     dynamic_scale,
     train_state,
 )
-import jax.numpy as jnp
 
 from fortuna.typing import Params
 from fortuna.utils.strings import convert_string_to_tuple
diff --git a/fortuna/training/trainer.py b/fortuna/training/trainer.py
index 680415b6..77033685 100755
--- a/fortuna/training/trainer.py
+++ b/fortuna/training/trainer.py
@@ -21,7 +21,6 @@
     random,
     value_and_grad,
 )
-from jax._src.prng import PRNGKeyArray
 import jax.numpy as jnp
 from jax.tree_util import tree_map
 from optax._src.base import PyTree
@@ -100,7 +99,7 @@ def training_loss_step(
         params: Params,
         batch: Batch,
         mutable: Mutable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
         unravel: Optional[Callable[[any], PyTree]] = None,
         calib_params: Optional[CalibParams] = None,
@@ -114,7 +113,7 @@ def training_step(
         state: TrainState,
         batch: Batch,
         loss_fun: Callable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
         unravel: Optional[Callable[[any], PyTree]] = None,
         kwargs: FrozenDict[str, Any] = FrozenDict(),
@@ -232,7 +231,7 @@ def validation_step(
         state: TrainState,
         batch: Batch,
         loss_fun: Callable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
         metrics: Optional[Tuple[Callable[[jnp.ndarray, Array], float], ...]] = None,
         unravel: Optional[Callable[[any], PyTree]] = None,
@@ -354,7 +353,7 @@ def validation_epoch_end(
 
     def train(
         self,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         state: TrainState,
         loss_fun: Callable,
         training_dataloader: DataLoader,
@@ -458,7 +457,7 @@ def _training_loop(
         current_epoch: int,
         loss_fun: Callable,
         metrics: Optional[Tuple[Callable[[jnp.ndarray, Array], jnp.ndarray], ...]],
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         state: TrainState,
         training_dataloader: DataLoader,
         training_dataset_size: int,
@@ -536,7 +535,7 @@ def _validation_loop(
         self,
         loss_fun: Callable,
         metrics: Optional[Tuple[Callable[[jnp.ndarray, Array], float], ...]],
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         state: TrainState,
         training_kwargs: FrozenDict[str, Any],
         validation_dataloader: DataLoader,
@@ -606,8 +605,8 @@ def on_train_start(
         self,
         state: TrainState,
         dataloaders: List[DataLoader],
-        rng: PRNGKeyArray,
-    ) -> Tuple[TrainState, List[DataLoader], PRNGKeyArray]:
+        rng: jax.Array,
+    ) -> Tuple[TrainState, List[DataLoader], jax.Array]:
         if self.freeze_fun is not None:
             frozen_paths = get_frozen_paths(state.params, self.freeze_fun)
             trainable_paths = get_trainable_paths(state.params, self.freeze_fun)
@@ -675,8 +674,8 @@ def compute_metrics(
         return metrics_vals
 
     def training_step_start(
-        self, rng: PRNGKeyArray, step: Union[int, jax.Array]
-    ) -> PRNGKeyArray:
+        self, rng: jax.Array, step: Union[int, jax.Array]
+    ) -> jax.Array:
         step = step if isinstance(step, int) or step.ndim == 0 else step[0]
         return random.fold_in(rng, step)
 
@@ -723,7 +722,7 @@ def training_step(
         state: TrainState,
         batch: Batch,
         loss_fun: Callable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
         unravel: Optional[Callable[[any], PyTree]] = None,
         kwargs: FrozenDict[str, Any] = FrozenDict(),
@@ -738,7 +737,7 @@ def validation_step(
         state: TrainState,
         batch: Batch,
         loss_fun: Callable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
         metrics: Optional[Tuple[Callable[[jnp.ndarray, Array], float], ...]] = None,
         unravel: Optional[Callable[[any], PyTree]] = None,
@@ -800,8 +799,8 @@ def _sync_state(self, state: TrainState) -> TrainState:
         return jax.device_get(tree_map(lambda x: x[0], state))
 
     def on_train_start(
-        self, state: TrainState, dataloaders: List[DataLoader], rng: PRNGKeyArray
-    ) -> Tuple[TrainState, List[DataLoader], PRNGKeyArray]:
+        self, state: TrainState, dataloaders: List[DataLoader], rng: jax.Array
+    ) -> Tuple[TrainState, List[DataLoader], jax.Array]:
         state, dataloaders, rng = super(MultiDeviceMixin, self).on_train_start(
             state, dataloaders, rng
         )
@@ -816,7 +815,7 @@ def on_train_end(self, state: TrainState) -> TrainState:
         state = super(MultiDeviceMixin, self).on_train_end(state)
         return jax.device_get(tree_map(lambda x: x[0], state))
 
-    def training_step_start(self, rng: PRNGKeyArray, step: int) -> PRNGKeyArray:
+    def training_step_start(self, rng: jax.Array, step: int) -> jax.Array:
         step = step if isinstance(step, int) or step.ndim == 0 else step[0]
         return jax.vmap(lambda r: random.fold_in(r, step))(rng)
 
@@ -826,7 +825,7 @@ def training_step(
         state: TrainState,
         batch: Batch,
         loss_fun: Callable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
         unravel: Optional[Callable[[any], PyTree]] = None,
         kwargs: FrozenDict[str, Any] = FrozenDict(),
@@ -863,7 +862,7 @@ def validation_step(
         state: TrainState,
         batch: Batch,
         loss_fun: Callable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
         metrics: Optional[Tuple[Callable[[jnp.ndarray, Array], float], ...]] = None,
         unravel: Optional[Callable[[any], PyTree]] = None,
diff --git a/fortuna/utils/probit.py b/fortuna/utils/probit.py
index ee31aa06..fdb6199a 100644
--- a/fortuna/utils/probit.py
+++ b/fortuna/utils/probit.py
@@ -8,19 +8,14 @@
 
 from flax.core import FrozenDict
 from jax import (
-    ShapeDtypeStruct,
     jit,
     jvp,
     lax,
-    pure_callback,
     vjp,
     vmap,
 )
 import jax.numpy as jnp
-from jax.tree_util import (
-    tree_map,
-    tree_reduce,
-)
+from jax.tree_util import tree_map
 
 from fortuna.typing import (
     AnyKey,
diff --git a/fortuna/utils/random.py b/fortuna/utils/random.py
index be674de3..a34dd09d 100644
--- a/fortuna/utils/random.py
+++ b/fortuna/utils/random.py
@@ -1,5 +1,5 @@
+import jax
 from jax import random
-from jax._src.prng import PRNGKeyArray
 from jax.tree_util import (
     tree_map,
     tree_structure,
@@ -35,14 +35,14 @@ def __init__(self, seed: int):
         """
         self._rng = random.PRNGKey(seed)
 
-    def get(self) -> PRNGKeyArray:
+    def get(self) -> jax.Array:
         """
         Get the internal random number generator key. Whenever this function is called, the random number generator
         key is updated.
 
         Returns
         -------
-        PRNGKeyArray
+        jax.Array
             A random number generator key.
         """
         self._rng = random.split(self._rng)[0]
diff --git a/fortuna/utils/strings.py b/fortuna/utils/strings.py
index 5dae4eaf..f54e50aa 100644
--- a/fortuna/utils/strings.py
+++ b/fortuna/utils/strings.py
@@ -5,7 +5,6 @@
     Tuple,
 )
 
-import jax.numpy as jnp
 from jax.tree_util import tree_map
 import numpy as np
 
@@ -31,7 +30,7 @@ def encode_tuple_of_lists_of_strings_to_numpy(
 
 
 def decode_encoded_tuple_of_lists_of_strings_to_array(
-    encoded: Optional[Dict[str, List[Array]]]
+    encoded: Optional[Dict[str, List[Array]]],
 ) -> Optional[Tuple[List[str], ...]]:
     if encoded is None:
         return None
diff --git a/poetry.lock b/poetry.lock
index 43e8537f..18e971c0 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,14 +1,14 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
-version = "1.4.0"
+version = "2.1.0"
 description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
-    {file = "absl-py-1.4.0.tar.gz", hash = "sha256:d2c244d01048ba476e7c080bd2c6df5e141d211de80223460d5b3b8a2a58433d"},
-    {file = "absl_py-1.4.0-py3-none-any.whl", hash = "sha256:0d3fe606adfa4f7db64792dd4c7aee4ee0c38ab75dfd353b7a83ed3e957fcb47"},
+    {file = "absl-py-2.1.0.tar.gz", hash = "sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff"},
+    {file = "absl_py-2.1.0-py3-none-any.whl", hash = "sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308"},
 ]
 
 [[package]]
@@ -24,87 +24,87 @@ files = [
 
 [[package]]
 name = "aiohttp"
-version = "3.9.3"
+version = "3.9.5"
 description = "Async http client/server framework (asyncio)"
 optional = true
 python-versions = ">=3.8"
 files = [
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-    {file = "aiohttp-3.9.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f56455b0c2c7cc3b0c584815264461d07b177f903a04481dfc33e08a89f0c26b"},
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 [package.dependencies]
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 [[package]]
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-description = "A configurable sidebar-enabled Sphinx theme"
+version = "0.7.16"
+description = "A light, configurable Sphinx theme"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.9"
 files = [
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 ]
 
 [[package]]
@@ -155,64 +155,56 @@ files = [
 
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 name = "anyio"
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+version = "4.4.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = true
-python-versions = ">=3.6.2"
+python-versions = ">=3.8"
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+    {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
+    {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
 ]
 
 [package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
 idna = ">=2.8"
 sniffio = ">=1.1"
+typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-doc = ["packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["contextlib2", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (<0.15)", "uvloop (>=0.15)"]
-trio = ["trio (>=0.16,<0.22)"]
-
-[[package]]
-name = "appdirs"
-version = "1.4.4"
-description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
-optional = false
-python-versions = "*"
-files = [
-    {file = "appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"},
-    {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"},
-]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.23)"]
 
 [[package]]
 name = "appnope"
-version = "0.1.3"
+version = "0.1.4"
 description = "Disable App Nap on macOS >= 10.9"
 optional = false
-python-versions = "*"
+python-versions = ">=3.6"
 files = [
-    {file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"},
-    {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"},
+    {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"},
+    {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
 ]
 
 [[package]]
 name = "argon2-cffi"
-version = "21.3.0"
-description = "The secure Argon2 password hashing algorithm."
+version = "23.1.0"
+description = "Argon2 for Python"
 optional = true
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
-    {file = "argon2-cffi-21.3.0.tar.gz", hash = "sha256:d384164d944190a7dd7ef22c6aa3ff197da12962bd04b17f64d4e93d934dba5b"},
-    {file = "argon2_cffi-21.3.0-py3-none-any.whl", hash = "sha256:8c976986f2c5c0e5000919e6de187906cfd81fb1c72bf9d88c01177e77da7f80"},
+    {file = "argon2_cffi-23.1.0-py3-none-any.whl", hash = "sha256:c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea"},
+    {file = "argon2_cffi-23.1.0.tar.gz", hash = "sha256:879c3e79a2729ce768ebb7d36d4609e3a78a4ca2ec3a9f12286ca057e3d0db08"},
 ]
 
 [package.dependencies]
 argon2-cffi-bindings = "*"
 
 [package.extras]
-dev = ["cogapp", "coverage[toml] (>=5.0.2)", "furo", "hypothesis", "pre-commit", "pytest", "sphinx", "sphinx-notfound-page", "tomli"]
-docs = ["furo", "sphinx", "sphinx-notfound-page"]
-tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"]
+dev = ["argon2-cffi[tests,typing]", "tox (>4)"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-copybutton", "sphinx-notfound-page"]
+tests = ["hypothesis", "pytest"]
+typing = ["mypy"]
 
 [[package]]
 name = "argon2-cffi-bindings"
@@ -253,14 +245,13 @@ tests = ["pytest"]
 
 [[package]]
 name = "array-record"
-version = "0.2.0"
+version = "0.4.1"
 description = "A file format that achieves a new frontier of IO efficiency"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "array_record-0.2.0-py310-none-any.whl", hash = "sha256:4b9335c7e21b54f559bada68b26f79309903015ff65101d4a3c3c42c62658398"},
-    {file = "array_record-0.2.0-py38-none-any.whl", hash = "sha256:12ce6844f8acb2e65f0bc4d8bcecbe19ac45a39cd2ba5bb56828668f118b1e87"},
-    {file = "array_record-0.2.0-py39-none-any.whl", hash = "sha256:d3b9a3a0d11f43a06a37fd8129d78e2894d7ff65b5fa53def198698c5592562a"},
+    {file = "array_record-0.4.1-py310-none-any.whl", hash = "sha256:6a0c8ed6fdfaaf2cecd3d5c6b9c13e116ad3299649611c8fd184d64557fbaba8"},
+    {file = "array_record-0.4.1-py39-none-any.whl", hash = "sha256:a74e9c0075860777b79e4b3ac278f67add270acf78520d3b9cf8c325aef42951"},
 ]
 
 [package.dependencies]
@@ -269,34 +260,40 @@ etils = {version = "*", extras = ["epath"]}
 
 [[package]]
 name = "arrow"
-version = "1.2.3"
+version = "1.3.0"
 description = "Better dates & times for Python"
 optional = true
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
-    {file = "arrow-1.2.3-py3-none-any.whl", hash = "sha256:5a49ab92e3b7b71d96cd6bfcc4df14efefc9dfa96ea19045815914a6ab6b1fe2"},
-    {file = "arrow-1.2.3.tar.gz", hash = "sha256:3934b30ca1b9f292376d9db15b19446088d12ec58629bc3f0da28fd55fb633a1"},
+    {file = "arrow-1.3.0-py3-none-any.whl", hash = "sha256:c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80"},
+    {file = "arrow-1.3.0.tar.gz", hash = "sha256:d4540617648cb5f895730f1ad8c82a65f2dad0166f57b75f3ca54759c4d67a85"},
 ]
 
 [package.dependencies]
 python-dateutil = ">=2.7.0"
+types-python-dateutil = ">=2.8.10"
+
+[package.extras]
+doc = ["doc8", "sphinx (>=7.0.0)", "sphinx-autobuild", "sphinx-autodoc-typehints", "sphinx_rtd_theme (>=1.3.0)"]
+test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock", "pytz (==2021.1)", "simplejson (==3.*)"]
 
 [[package]]
 name = "asttokens"
-version = "2.2.1"
+version = "2.4.1"
 description = "Annotate AST trees with source code positions"
 optional = false
 python-versions = "*"
 files = [
-    {file = "asttokens-2.2.1-py2.py3-none-any.whl", hash = "sha256:6b0ac9e93fb0335014d382b8fa9b3afa7df546984258005da0b9e7095b3deb1c"},
-    {file = "asttokens-2.2.1.tar.gz", hash = "sha256:4622110b2a6f30b77e1473affaa97e711bc2f07d3f10848420ff1898edbe94f3"},
+    {file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"},
+    {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"},
 ]
 
 [package.dependencies]
-six = "*"
+six = ">=1.12.0"
 
 [package.extras]
-test = ["astroid", "pytest"]
+astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
+test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
 
 [[package]]
 name = "astunparse"
@@ -313,87 +310,94 @@ files = [
 six = ">=1.6.1,<2.0"
 wheel = ">=0.23.0,<1.0"
 
+[[package]]
+name = "async-lru"
+version = "2.0.4"
+description = "Simple LRU cache for asyncio"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "async-lru-2.0.4.tar.gz", hash = "sha256:b8a59a5df60805ff63220b2a0c5b5393da5521b113cd5465a44eb037d81a5627"},
+    {file = "async_lru-2.0.4-py3-none-any.whl", hash = "sha256:ff02944ce3c288c5be660c42dbcca0742b32c3b279d6dceda655190240b99224"},
+]
+
+[package.dependencies]
+typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
+
 [[package]]
 name = "async-timeout"
-version = "4.0.2"
+version = "4.0.3"
 description = "Timeout context manager for asyncio programs"
 optional = true
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
-    {file = "async-timeout-4.0.2.tar.gz", hash = "sha256:2163e1640ddb52b7a8c80d0a67a08587e5d245cc9c553a74a847056bc2976b15"},
-    {file = "async_timeout-4.0.2-py3-none-any.whl", hash = "sha256:8ca1e4fcf50d07413d66d1a5e416e42cfdf5851c981d679a09851a6853383b3c"},
+    {file = "async-timeout-4.0.3.tar.gz", hash = "sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f"},
+    {file = "async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028"},
 ]
 
 [[package]]
 name = "attrs"
-version = "23.1.0"
+version = "23.2.0"
 description = "Classes Without Boilerplate"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "attrs-23.1.0-py3-none-any.whl", hash = "sha256:1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04"},
-    {file = "attrs-23.1.0.tar.gz", hash = "sha256:6279836d581513a26f1bf235f9acd333bc9115683f14f7e8fae46c98fc50e015"},
+    {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"},
+    {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"},
 ]
 
 [package.extras]
 cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
-dev = ["attrs[docs,tests]", "pre-commit"]
+dev = ["attrs[tests]", "pre-commit"]
 docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
 tests = ["attrs[tests-no-zope]", "zope-interface"]
-tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
+tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
 
 [[package]]
 name = "babel"
-version = "2.12.1"
+version = "2.15.0"
 description = "Internationalization utilities"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "Babel-2.12.1-py3-none-any.whl", hash = "sha256:b4246fb7677d3b98f501a39d43396d3cafdc8eadb045f4a31be01863f655c610"},
-    {file = "Babel-2.12.1.tar.gz", hash = "sha256:cc2d99999cd01d44420ae725a21c9e3711b3aadc7976d6147f622d8581963455"},
+    {file = "Babel-2.15.0-py3-none-any.whl", hash = "sha256:08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb"},
+    {file = "babel-2.15.0.tar.gz", hash = "sha256:8daf0e265d05768bc6c7a314cf1321e9a123afc328cc635c18622a2f30a04413"},
 ]
 
-[package.dependencies]
-pytz = {version = ">=2015.7", markers = "python_version < \"3.9\""}
-
-[[package]]
-name = "backcall"
-version = "0.2.0"
-description = "Specifications for callback functions passed in to an API"
-optional = false
-python-versions = "*"
-files = [
-    {file = "backcall-0.2.0-py2.py3-none-any.whl", hash = "sha256:fbbce6a29f263178a1f7915c1940bde0ec2b2a967566fe1c65c1dfb7422bd255"},
-    {file = "backcall-0.2.0.tar.gz", hash = "sha256:5cbdbf27be5e7cfadb448baf0aa95508f91f2bbc6c6437cd9cd06e2a4c215e1e"},
-]
+[package.extras]
+dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
 
 [[package]]
 name = "beautifulsoup4"
-version = "4.12.2"
+version = "4.12.3"
 description = "Screen-scraping library"
 optional = false
 python-versions = ">=3.6.0"
 files = [
-    {file = "beautifulsoup4-4.12.2-py3-none-any.whl", hash = "sha256:bd2520ca0d9d7d12694a53d44ac482d181b4ec1888909b035a3dbf40d0f57d4a"},
-    {file = "beautifulsoup4-4.12.2.tar.gz", hash = "sha256:492bbc69dca35d12daac71c4db1bfff0c876c00ef4a2ffacce226d4638eb72da"},
+    {file = "beautifulsoup4-4.12.3-py3-none-any.whl", hash = "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"},
+    {file = "beautifulsoup4-4.12.3.tar.gz", hash = "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051"},
 ]
 
 [package.dependencies]
 soupsieve = ">1.2"
 
 [package.extras]
+cchardet = ["cchardet"]
+chardet = ["chardet"]
+charset-normalizer = ["charset-normalizer"]
 html5lib = ["html5lib"]
 lxml = ["lxml"]
 
 [[package]]
 name = "bleach"
-version = "6.0.0"
+version = "6.1.0"
 description = "An easy safelist-based HTML-sanitizing tool."
 optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "bleach-6.0.0-py3-none-any.whl", hash = "sha256:33c16e3353dbd13028ab4799a0f89a83f113405c766e9c122df8a06f5b85b3f4"},
-    {file = "bleach-6.0.0.tar.gz", hash = "sha256:1a1a85c1595e07d8db14c5f09f09e6433502c51c595970edc090551f0db99414"},
+    {file = "bleach-6.1.0-py3-none-any.whl", hash = "sha256:3225f354cfc436b9789c66c4ee030194bee0568fbf9cbdad3bc8b5c26c5f12b6"},
+    {file = "bleach-6.1.0.tar.gz", hash = "sha256:0a31f1837963c41d46bbf1331b8778e1308ea0791db03cc4e7357b97cf42a8fe"},
 ]
 
 [package.dependencies]
@@ -401,150 +405,130 @@ six = ">=1.9.0"
 webencodings = "*"
 
 [package.extras]
-css = ["tinycss2 (>=1.1.0,<1.2)"]
+css = ["tinycss2 (>=1.1.0,<1.3)"]
 
 [[package]]
 name = "boto3"
-version = "1.26.145"
+version = "1.34.136"
 description = "The AWS SDK for Python"
 optional = true
-python-versions = ">= 3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "boto3-1.26.145-py3-none-any.whl", hash = "sha256:77e8fa7c257f9ed8bfe0c3ffc2ccc47b1cfa27058f99415b6003699d1202e0c0"},
-    {file = "boto3-1.26.145.tar.gz", hash = "sha256:30f8ab1cf89d5864a80ba2d5eb5316dbd2a63c9469877e0cffb522630438aa85"},
+    {file = "boto3-1.34.136-py3-none-any.whl", hash = "sha256:d41037e2c680ab8d6c61a0a4ee6bf1fdd9e857f43996672830a95d62d6f6fa79"},
+    {file = "boto3-1.34.136.tar.gz", hash = "sha256:0314e6598f59ee0f34eb4e6d1a0f69fa65c146d2b88a6e837a527a9956ec2731"},
 ]
 
 [package.dependencies]
-botocore = ">=1.29.145,<1.30.0"
+botocore = ">=1.34.136,<1.35.0"
 jmespath = ">=0.7.1,<2.0.0"
-s3transfer = ">=0.6.0,<0.7.0"
+s3transfer = ">=0.10.0,<0.11.0"
 
 [package.extras]
 crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.29.145"
+version = "1.34.136"
 description = "Low-level, data-driven core of boto 3."
 optional = true
-python-versions = ">= 3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "botocore-1.29.145-py3-none-any.whl", hash = "sha256:264a3f19ed280d80711b7e278be09acff7ed379a96432fdf179b4e6e3a687e6a"},
-    {file = "botocore-1.29.145.tar.gz", hash = "sha256:65e2a2b1cc70583225f87d6d63736215f93c6234721967bdab872270ba7a1f45"},
+    {file = "botocore-1.34.136-py3-none-any.whl", hash = "sha256:c63fe9032091fb9e9477706a3ebfa4d0c109b807907051d892ed574f9b573e61"},
+    {file = "botocore-1.34.136.tar.gz", hash = "sha256:7f7135178692b39143c8f152a618d2a3b71065a317569a7102d2306d4946f42f"},
 ]
 
 [package.dependencies]
 jmespath = ">=0.7.1,<2.0.0"
 python-dateutil = ">=2.1,<3.0.0"
-urllib3 = ">=1.25.4,<1.27"
+urllib3 = [
+    {version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version >= \"3.10\""},
+    {version = ">=1.25.4,<1.27", markers = "python_version < \"3.10\""},
+]
 
 [package.extras]
-crt = ["awscrt (==0.16.9)"]
-
-[[package]]
-name = "cached-property"
-version = "1.5.2"
-description = "A decorator for caching properties in classes."
-optional = false
-python-versions = "*"
-files = [
-    {file = "cached-property-1.5.2.tar.gz", hash = "sha256:9fa5755838eecbb2d234c3aa390bd80fbd3ac6b6869109bfc1b499f7bd89a130"},
-    {file = "cached_property-1.5.2-py2.py3-none-any.whl", hash = "sha256:df4f613cf7ad9a588cc381aaf4a512d26265ecebd5eb9e1ba12f1319eb85a6a0"},
-]
+crt = ["awscrt (==0.20.11)"]
 
 [[package]]
 name = "cachetools"
-version = "5.3.0"
+version = "5.3.3"
 description = "Extensible memoizing collections and decorators"
 optional = false
-python-versions = "~=3.7"
+python-versions = ">=3.7"
 files = [
-    {file = "cachetools-5.3.0-py3-none-any.whl", hash = "sha256:429e1a1e845c008ea6c85aa35d4b98b65d6a9763eeef3e37e92728a12d1de9d4"},
-    {file = "cachetools-5.3.0.tar.gz", hash = "sha256:13dfddc7b8df938c21a940dfa6557ce6e94a2f1cdfa58eb90c805721d58f2c14"},
+    {file = "cachetools-5.3.3-py3-none-any.whl", hash = "sha256:0abad1021d3f8325b2fc1d2e9c8b9c9d57b04c3932657a72465447332c24d945"},
+    {file = "cachetools-5.3.3.tar.gz", hash = "sha256:ba29e2dfa0b8b556606f097407ed1aa62080ee108ab0dc5ec9d6a723a007d105"},
 ]
 
 [[package]]
 name = "certifi"
-version = "2023.5.7"
+version = "2024.6.2"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
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 [package.dependencies]
@@ -552,128 +536,142 @@ pycparser = "*"
 
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 optional = false
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 hard-encoding-detection = ["chardet"]
 toml = ["tomli"]
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 [package.dependencies]
@@ -888,139 +863,94 @@ tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.1
 [package.extras]
 toml = ["tomli"]
 
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+    {file = "dill-0.3.8.tar.gz", hash = "sha256:3ebe3c479ad625c4553aca177444d89b486b1d84982eeacded644afc0cf797ca"},
 ]
 
 [package.extras]
 graph = ["objgraph (>=1.7.2)"]
+profile = ["gprof2dot (>=2022.7.29)"]
 
 [[package]]
 name = "distlib"
-version = "0.3.6"
+version = "0.3.8"
 description = "Distribution utilities"
 optional = false
 python-versions = "*"
 files = [
-    {file = "distlib-0.3.6-py2.py3-none-any.whl", hash = "sha256:f35c4b692542ca110de7ef0bea44d73981caeb34ca0b9b6b2e6d7790dda8f80e"},
-    {file = "distlib-0.3.6.tar.gz", hash = "sha256:14bad2d9b04d3a36127ac97f30b12a19268f211063d8f8ee4f47108896e11b46"},
+    {file = "distlib-0.3.8-py2.py3-none-any.whl", hash = "sha256:034db59a0b96f8ca18035f36290806a9a6e6bd9d1ff91e45a7f172eb17e51784"},
+    {file = "distlib-0.3.8.tar.gz", hash = "sha256:1530ea13e350031b6312d8580ddb6b27a104275a31106523b8f123787f494f64"},
 ]
 
 [[package]]
@@ -1097,8 +1028,6 @@ files = [
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     {file = "dm_tree-0.1.8-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:94d3f0826311f45ee19b75f5b48c99466e4218a0489e81c0f0167bda50cacf22"},
     {file = "dm_tree-0.1.8-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:435227cf3c5dc63f4de054cf3d00183790bd9ead4c3623138c74dde7f67f521b"},
-    {file = "dm_tree-0.1.8-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:09964470f76a5201aff2e8f9b26842976de7889300676f927930f6285e256760"},
-    {file = "dm_tree-0.1.8-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:75c5d528bb992981c20793b6b453e91560784215dffb8a5440ba999753c14ceb"},
     {file = "dm_tree-0.1.8-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c0a94aba18a35457a1b5cd716fd7b46c5dafdc4cf7869b4bae665b91c4682a8e"},
     {file = "dm_tree-0.1.8-cp312-cp312-win_amd64.whl", hash = "sha256:96a548a406a6fb15fe58f6a30a57ff2f2aafbf25f05afab00c8f5e5977b6c715"},
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@@ -1125,6 +1054,28 @@ files = [
     {file = "dm_tree-0.1.8-cp39-cp39-win_amd64.whl", hash = "sha256:8ed3564abed97c806db122c2d3e1a2b64c74a63debe9903aad795167cc301368"},
 ]
 
+[[package]]
+name = "docker"
+version = "7.1.0"
+description = "A Python library for the Docker Engine API."
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "docker-7.1.0-py3-none-any.whl", hash = "sha256:c96b93b7f0a746f9e77d325bcfb87422a3d8bd4f03136ae8a85b37f1898d5fc0"},
+    {file = "docker-7.1.0.tar.gz", hash = "sha256:ad8c70e6e3f8926cb8a92619b832b4ea5299e2831c14284663184e200546fa6c"},
+]
+
+[package.dependencies]
+pywin32 = {version = ">=304", markers = "sys_platform == \"win32\""}
+requests = ">=2.26.0"
+urllib3 = ">=1.26.0"
+
+[package.extras]
+dev = ["coverage (==7.2.7)", "pytest (==7.4.2)", "pytest-cov (==4.1.0)", "pytest-timeout (==2.1.0)", "ruff (==0.1.8)"]
+docs = ["myst-parser (==0.18.0)", "sphinx (==5.1.1)"]
+ssh = ["paramiko (>=2.4.3)"]
+websockets = ["websocket-client (>=1.3.0)"]
+
 [[package]]
 name = "docutils"
 version = "0.19"
@@ -1149,30 +1100,36 @@ files = [
 
 [[package]]
 name = "etils"
-version = "1.2.0"
+version = "1.5.2"
 description = "Collection of common python utils"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "etils-1.2.0-py3-none-any.whl", hash = "sha256:c6585069b387fdbeed6a2c571b8bcf312ecdb577c95065461e5fad9ed1973989"},
-    {file = "etils-1.2.0.tar.gz", hash = "sha256:29d369e2dcf43960d9ee338330579d04badd606c88f015f4e1a38d3adbe446d8"},
+    {file = "etils-1.5.2-py3-none-any.whl", hash = "sha256:6dc882d355e1e98a5d1a148d6323679dc47c9a5792939b9de72615aa4737eb0b"},
+    {file = "etils-1.5.2.tar.gz", hash = "sha256:ba6a3e1aff95c769130776aa176c11540637f5dd881f3b79172a5149b6b1c446"},
 ]
 
 [package.dependencies]
+absl-py = {version = "*", optional = true, markers = "extra == \"etqdm\""}
+fsspec = {version = "*", optional = true, markers = "extra == \"epath\""}
 importlib_resources = {version = "*", optional = true, markers = "extra == \"epath\""}
 numpy = {version = "*", optional = true, markers = "extra == \"enp\""}
+tqdm = {version = "*", optional = true, markers = "extra == \"etqdm\""}
 typing_extensions = {version = "*", optional = true, markers = "extra == \"epy\""}
 zipp = {version = "*", optional = true, markers = "extra == \"epath\""}
 
 [package.extras]
-all = ["etils[array-types]", "etils[eapp]", "etils[ecolab]", "etils[edc]", "etils[enp]", "etils[epath]", "etils[epy]", "etils[etqdm]", "etils[etree-dm]", "etils[etree-jax]", "etils[etree-tf]", "etils[etree]"]
+all = ["etils[array-types]", "etils[eapp]", "etils[ecolab]", "etils[edc]", "etils[enp]", "etils[epath-gcs]", "etils[epath-s3]", "etils[epath]", "etils[epy]", "etils[etqdm]", "etils[etree-dm]", "etils[etree-jax]", "etils[etree-tf]", "etils[etree]"]
 array-types = ["etils[enp]"]
-dev = ["chex", "optree", "pyink", "pylint (>=2.6.0)", "pytest", "pytest-subtests", "pytest-xdist", "torch"]
+dev = ["chex", "dataclass_array", "optree", "pyink", "pylint (>=2.6.0)", "pytest", "pytest-subtests", "pytest-xdist", "torch"]
+docs = ["etils[all,dev]", "sphinx-apitree[ext]"]
 eapp = ["absl-py", "etils[epy]", "simple_parsing"]
-ecolab = ["etils[enp]", "etils[epy]", "jupyter", "mediapy", "numpy"]
-edc = ["etils[epy]", "typing_extensions"]
+ecolab = ["etils[enp]", "etils[epy]", "jupyter", "mediapy", "numpy", "packaging"]
+edc = ["etils[epy]"]
 enp = ["etils[epy]", "numpy"]
-epath = ["etils[epy]", "importlib_resources", "typing_extensions", "zipp"]
+epath = ["etils[epy]", "fsspec", "importlib_resources", "typing_extensions", "zipp"]
+epath-gcs = ["etils[epath]", "gcsfs"]
+epath-s3 = ["etils[epath]", "s3fs"]
 epy = ["typing_extensions"]
 etqdm = ["absl-py", "etils[epy]", "tqdm"]
 etree = ["etils[array-types]", "etils[enp]", "etils[epy]", "etils[etqdm]"]
@@ -1183,13 +1140,13 @@ lazy-imports = ["etils[ecolab]"]
 
 [[package]]
 name = "exceptiongroup"
-version = "1.1.1"
+version = "1.2.1"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "exceptiongroup-1.1.1-py3-none-any.whl", hash = "sha256:232c37c63e4f682982c8b6459f33a8981039e5fb8756b2074364e5055c498c9e"},
-    {file = "exceptiongroup-1.1.1.tar.gz", hash = "sha256:d484c3090ba2889ae2928419117447a14daf3c1231d5e30d0aae34f354f01785"},
+    {file = "exceptiongroup-1.2.1-py3-none-any.whl", hash = "sha256:5258b9ed329c5bbdd31a309f53cbfb0b155341807f6ff7606a1e801a891b29ad"},
+    {file = "exceptiongroup-1.2.1.tar.gz", hash = "sha256:a4785e48b045528f5bfe627b6ad554ff32def154f42372786903b7abcfe1aa16"},
 ]
 
 [package.extras]
@@ -1197,27 +1154,27 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "executing"
-version = "1.2.0"
+version = "2.0.1"
 description = "Get the currently executing AST node of a frame, and other information"
 optional = false
-python-versions = "*"
+python-versions = ">=3.5"
 files = [
-    {file = "executing-1.2.0-py2.py3-none-any.whl", hash = "sha256:0314a69e37426e3608aada02473b4161d4caf5a4b244d1d0c48072b8fee7bacc"},
-    {file = "executing-1.2.0.tar.gz", hash = "sha256:19da64c18d2d851112f09c287f8d3dbbdf725ab0e569077efb6cdcbd3497c107"},
+    {file = "executing-2.0.1-py2.py3-none-any.whl", hash = "sha256:eac49ca94516ccc753f9fb5ce82603156e590b27525a8bc32cce8ae302eb61bc"},
+    {file = "executing-2.0.1.tar.gz", hash = "sha256:35afe2ce3affba8ee97f2d69927fa823b08b472b7b994e36a52a964b93d16147"},
 ]
 
 [package.extras]
-tests = ["asttokens", "littleutils", "pytest", "rich"]
+tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"]
 
 [[package]]
 name = "fastjsonschema"
-version = "2.16.3"
+version = "2.20.0"
 description = "Fastest Python implementation of JSON schema"
 optional = false
 python-versions = "*"
 files = [
-    {file = "fastjsonschema-2.16.3-py3-none-any.whl", hash = "sha256:04fbecc94300436f628517b05741b7ea009506ce8f946d40996567c669318490"},
-    {file = "fastjsonschema-2.16.3.tar.gz", hash = "sha256:4a30d6315a68c253cfa8f963b9697246315aa3db89f98b97235e345dedfb0b8e"},
+    {file = "fastjsonschema-2.20.0-py3-none-any.whl", hash = "sha256:5875f0b0fa7a0043a91e93a9b8f793bcbbba9691e7fd83dca95c28ba26d21f0a"},
+    {file = "fastjsonschema-2.20.0.tar.gz", hash = "sha256:3d48fc5300ee96f5d116f10fe6f28d938e6008f59a6a025c2649475b87f76a23"},
 ]
 
 [package.extras]
@@ -1225,105 +1182,109 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 
 [[package]]
 name = "filelock"
-version = "3.12.0"
+version = "3.15.4"
 description = "A platform independent file lock."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "filelock-3.12.0-py3-none-any.whl", hash = "sha256:ad98852315c2ab702aeb628412cbf7e95b7ce8c3bf9565670b4eaecf1db370a9"},
-    {file = "filelock-3.12.0.tar.gz", hash = "sha256:fc03ae43288c013d2ea83c8597001b1129db351aad9c57fe2409327916b8e718"},
+    {file = "filelock-3.15.4-py3-none-any.whl", hash = "sha256:6ca1fffae96225dab4c6eaf1c4f4f28cd2568d3ec2a44e15a08520504de468e7"},
+    {file = "filelock-3.15.4.tar.gz", hash = "sha256:2207938cbc1844345cb01a5a95524dae30f0ce089eba5b00378295a17e3e90cb"},
 ]
 
 [package.extras]
-docs = ["furo (>=2023.3.27)", "sphinx (>=6.1.3)", "sphinx-autodoc-typehints (>=1.23,!=1.23.4)"]
-testing = ["covdefaults (>=2.3)", "coverage (>=7.2.3)", "diff-cover (>=7.5)", "pytest (>=7.3.1)", "pytest-cov (>=4)", "pytest-mock (>=3.10)", "pytest-timeout (>=2.1)"]
+docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
+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]]
 name = "flatbuffers"
-version = "23.5.9"
+version = "24.3.25"
 description = "The FlatBuffers serialization format for Python"
 optional = false
 python-versions = "*"
 files = [
-    {file = "flatbuffers-23.5.9-py2.py3-none-any.whl", hash = "sha256:a02eb8c2d61cba153cd211937de8f8f7764b6a7510971b2c4684ed8b02e6e571"},
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+    {file = "flatbuffers-24.3.25-py2.py3-none-any.whl", hash = "sha256:8dbdec58f935f3765e4f7f3cf635ac3a77f83568138d6a2311f524ec96364812"},
+    {file = "flatbuffers-24.3.25.tar.gz", hash = "sha256:de2ec5b203f21441716617f38443e0a8ebf3d25bf0d9c0bb0ce68fa00ad546a4"},
 ]
 
 [[package]]
 name = "flax"
-version = "0.6.10"
+version = "0.8.5"
 description = "Flax: A neural network library for JAX designed for flexibility"
 optional = false
-python-versions = "*"
+python-versions = ">=3.9"
 files = [
-    {file = "flax-0.6.10-py3-none-any.whl", hash = "sha256:8dccc7b84b00ff6f59a36dc0e79f5919498cfeb009a41f8c07f68bf2513198db"},
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+    {file = "flax-0.8.5-py3-none-any.whl", hash = "sha256:c96e46d1c48a300d010ebf5c4846f163bdd7acc6efff5ff2bfb1cb5b08aa65d8"},
+    {file = "flax-0.8.5.tar.gz", hash = "sha256:4a9cb7950ece54b0addaa73d77eba24e46138dbe783d01987be79d20ccb2b09b"},
 ]
 
 [package.dependencies]
-jax = ">=0.4.2"
+jax = ">=0.4.27"
 msgpack = "*"
-numpy = ">=1.12"
+numpy = [
+    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
+    {version = ">=1.22", markers = "python_version < \"3.11\""},
+]
 optax = "*"
 orbax-checkpoint = "*"
 PyYAML = ">=5.4.1"
 rich = ">=11.1"
 tensorstore = "*"
-typing-extensions = ">=4.1.1"
+typing-extensions = ">=4.2"
 
 [package.extras]
 all = ["matplotlib"]
-testing = ["atari-py (==0.2.5)", "clu", "einops", "gym (==0.18.3)", "jaxlib", "jraph (>=0.0.6dev0)", "ml-collections", "mypy", "nbstripout", "opencv-python", "pytest", "pytest-cov", "pytest-custom-exit-code", "pytest-xdist (==1.34.0)", "pytype", "sentencepiece", "tensorflow", "tensorflow-datasets", "tensorflow-text (>=2.11.0)", "torch"]
+testing = ["black[jupyter] (==23.7.0)", "clu", "clu (<=0.0.9)", "einops", "gymnasium[accept-rom-license,atari]", "jaxlib", "jaxtyping", "jraph (>=0.0.6dev0)", "ml-collections", "mypy", "nbstripout", "opencv-python", "penzai (>=0.1.2)", "pytest", "pytest-cov", "pytest-custom-exit-code", "pytest-xdist", "pytype", "sentencepiece", "tensorflow (>=2.12.0)", "tensorflow-datasets", "tensorflow-text (>=2.11.0)", "torch"]
 
 [[package]]
 name = "fonttools"
-version = "4.49.0"
+version = "4.53.0"
 description = "Tools to manipulate font files"
 optional = false
 python-versions = ">=3.8"
 files = [
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 name = "fsspec"
-version = "2023.5.0"
+version = "2024.5.0"
 description = "File-system specification"
-optional = true
+optional = false
 python-versions = ">=3.8"
 files = [
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 [package.extras]
 abfs = ["adlfs"]
 adl = ["adlfs"]
 arrow = ["pyarrow (>=1)"]
 dask = ["dask", "distributed"]
-devel = ["pytest", "pytest-cov"]
+dev = ["pre-commit", "ruff"]
 dropbox = ["dropbox", "dropboxdrivefs", "requests"]
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 fuse = ["fusepy"]
@@ -1510,13 +1469,16 @@ github = ["requests"]
 gs = ["gcsfs"]
 gui = ["panel"]
 hdfs = ["pyarrow (>=1)"]
-http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
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 oci = ["ocifs"]
 s3 = ["s3fs"]
 sftp = ["paramiko"]
 smb = ["smbprotocol"]
 ssh = ["paramiko"]
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 tqdm = ["tqdm"]
 
 [[package]]
@@ -1532,28 +1494,26 @@ files = [
 
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-rsa = {version = ">=3.1.4,<5", markers = "python_version >= \"3.6\""}
-six = ">=1.9.0"
-urllib3 = "<2.0"
+rsa = ">=3.1.4,<5"
 
 [package.extras]
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+aiohttp = ["aiohttp (>=3.6.2,<4.0.0.dev0)", "requests (>=2.20.0,<3.0.0.dev0)"]
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-requests = ["requests (>=2.20.0,<3.0.0dev)"]
+requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
 
 [[package]]
 name = "google-auth-oauthlib"
@@ -1590,200 +1550,193 @@ six = "*"
 
 [[package]]
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 [package.extras]
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+grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
 
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+[package.dependencies]
+numpy = ">=1.17.3"
 
 [[package]]
 name = "html5lib"
@@ -1806,15 +1759,60 @@ chardet = ["chardet (>=2.2)"]
 genshi = ["genshi"]
 lxml = ["lxml"]
 
+[[package]]
+name = "httpcore"
+version = "1.0.5"
+description = "A minimal low-level HTTP client."
+optional = true
+python-versions = ">=3.8"
+files = [
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+[package.dependencies]
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+h11 = ">=0.13,<0.15"
+
+[package.extras]
+asyncio = ["anyio (>=4.0,<5.0)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+trio = ["trio (>=0.22.0,<0.26.0)"]
+
+[[package]]
+name = "httpx"
+version = "0.27.0"
+description = "The next generation HTTP client."
+optional = true
+python-versions = ">=3.8"
+files = [
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+
+[package.dependencies]
+anyio = "*"
+certifi = "*"
+httpcore = "==1.*"
+idna = "*"
+sniffio = "*"
+
+[package.extras]
+brotli = ["brotli", "brotlicffi"]
+cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+
 [[package]]
 name = "huggingface-hub"
-version = "0.20.1"
+version = "0.23.4"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = true
 python-versions = ">=3.8.0"
 files = [
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 [package.dependencies]
@@ -1827,15 +1825,17 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
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+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
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 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
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-quality = ["mypy (==1.5.1)", "ruff (>=0.1.3)"]
+hf-transfer = ["hf-transfer (>=0.1.4)"]
+inference = ["aiohttp", "minijinja (>=1.0)"]
+quality = ["mypy (==1.5.1)", "ruff (>=0.3.0)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
-testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
-torch = ["torch"]
+tensorflow-testing = ["keras (<3.0)", "tensorflow"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+torch = ["safetensors", "torch"]
 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
 [[package]]
@@ -1851,19 +1851,18 @@ files = [
 
 [package.dependencies]
 antlr4-python3-runtime = "==4.9.*"
-importlib-resources = {version = "*", markers = "python_version < \"3.9\""}
 omegaconf = ">=2.2,<2.4"
 packaging = "*"
 
 [[package]]
 name = "identify"
-version = "2.5.24"
+version = "2.5.36"
 description = "File identification library for Python"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+    {file = "identify-2.5.36-py2.py3-none-any.whl", hash = "sha256:37d93f380f4de590500d9dba7db359d0d3da95ffe7f9de1753faa159e71e7dfa"},
+    {file = "identify-2.5.36.tar.gz", hash = "sha256:e5e00f54165f9047fbebeb4a560f9acfb8af4c88232be60a488e9b68d122745d"},
 ]
 
 [package.extras]
@@ -1871,13 +1870,13 @@ license = ["ukkonen"]
 
 [[package]]
 name = "idna"
-version = "3.4"
+version = "3.7"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.5"
 files = [
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-    {file = "idna-3.4.tar.gz", hash = "sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4"},
+    {file = "idna-3.7-py3-none-any.whl", hash = "sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0"},
+    {file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"},
 ]
 
 [[package]]
@@ -1893,40 +1892,40 @@ files = [
 
 [[package]]
 name = "importlib-metadata"
-version = "4.13.0"
+version = "6.11.0"
 description = "Read metadata from Python packages"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+    {file = "importlib_metadata-6.11.0-py3-none-any.whl", hash = "sha256:f0afba6205ad8f8947c7d338b5342d5db2afbfd82f9cbef7879a9539cc12eb9b"},
+    {file = "importlib_metadata-6.11.0.tar.gz", hash = "sha256:1231cf92d825c9e03cfc4da076a16de6422c863558229ea0b22b675657463443"},
 ]
 
 [package.dependencies]
 zipp = ">=0.5"
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
 perf = ["ipython"]
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+testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
 
 [[package]]
 name = "importlib-resources"
-version = "5.12.0"
+version = "6.4.0"
 description = "Read resources from Python packages"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["flake8 (<5)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["jaraco.test (>=5.4)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
 
 [[package]]
 name = "iniconfig"
@@ -1941,13 +1940,13 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.23.0"
+version = "6.29.4"
 description = "IPython Kernel for Jupyter"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.23.0-py3-none-any.whl", hash = "sha256:fc886f1dcdc0ec17f277e4d21fd071c857d381adcb04f3f3735d25325ca323c6"},
-    {file = "ipykernel-6.23.0.tar.gz", hash = "sha256:bd6f487d9e2744c84f6e667d46462d7647a4c862e70e08282f05a52b9d4b705f"},
+    {file = "ipykernel-6.29.4-py3-none-any.whl", hash = "sha256:1181e653d95c6808039c509ef8e67c4126b3b3af7781496c7cbfb5ed938a27da"},
+    {file = "ipykernel-6.29.4.tar.gz", hash = "sha256:3d44070060f9475ac2092b760123fadf105d2e2493c24848b6691a7c4f42af5c"},
 ]
 
 [package.dependencies]
@@ -1961,7 +1960,7 @@ matplotlib-inline = ">=0.1"
 nest-asyncio = "*"
 packaging = "*"
 psutil = "*"
-pyzmq = ">=20"
+pyzmq = ">=24"
 tornado = ">=6.1"
 traitlets = ">=5.4.0"
 
@@ -1970,75 +1969,62 @@ cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
 pyqt5 = ["pyqt5"]
 pyside6 = ["pyside6"]
-test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov", "pytest-timeout"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "ipython"
-version = "8.12.2"
+version = "8.18.1"
 description = "IPython: Productive Interactive Computing"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "ipython-8.12.2-py3-none-any.whl", hash = "sha256:ea8801f15dfe4ffb76dea1b09b847430ffd70d827b41735c64a0638a04103bfc"},
-    {file = "ipython-8.12.2.tar.gz", hash = "sha256:c7b80eb7f5a855a88efc971fda506ff7a91c280b42cdae26643e0f601ea281ea"},
+    {file = "ipython-8.18.1-py3-none-any.whl", hash = "sha256:e8267419d72d81955ec1177f8a29aaa90ac80ad647499201119e2f05e99aa397"},
+    {file = "ipython-8.18.1.tar.gz", hash = "sha256:ca6f079bb33457c66e233e4580ebfc4128855b4cf6370dddd73842a9563e8a27"},
 ]
 
 [package.dependencies]
-appnope = {version = "*", markers = "sys_platform == \"darwin\""}
-backcall = "*"
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
 decorator = "*"
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
 jedi = ">=0.16"
 matplotlib-inline = "*"
 pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
-pickleshare = "*"
-prompt-toolkit = ">=3.0.30,<3.0.37 || >3.0.37,<3.1.0"
+prompt-toolkit = ">=3.0.41,<3.1.0"
 pygments = ">=2.4.0"
 stack-data = "*"
 traitlets = ">=5"
 typing-extensions = {version = "*", markers = "python_version < \"3.10\""}
 
 [package.extras]
-all = ["black", "curio", "docrepr", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.21)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
+all = ["black", "curio", "docrepr", "exceptiongroup", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
 black = ["black"]
-doc = ["docrepr", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
+doc = ["docrepr", "exceptiongroup", "ipykernel", "matplotlib", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
 kernel = ["ipykernel"]
 nbconvert = ["nbconvert"]
 nbformat = ["nbformat"]
 notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
-test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
-test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"]
-
-[[package]]
-name = "ipython-genutils"
-version = "0.2.0"
-description = "Vestigial utilities from IPython"
-optional = true
-python-versions = "*"
-files = [
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+test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath", "trio"]
 
 [[package]]
 name = "ipywidgets"
-version = "8.0.6"
+version = "8.1.3"
 description = "Jupyter interactive widgets"
 optional = true
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
-ipykernel = ">=4.5.1"
+comm = ">=0.1.3"
 ipython = ">=6.1.0"
-jupyterlab-widgets = ">=3.0.7,<3.1.0"
+jupyterlab-widgets = ">=3.0.11,<3.1.0"
 traitlets = ">=4.3.1"
-widgetsnbextension = ">=4.0.7,<4.1.0"
+widgetsnbextension = ">=4.0.11,<4.1.0"
 
 [package.extras]
 test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
@@ -2059,88 +2045,97 @@ arrow = ">=0.15.0"
 
 [[package]]
 name = "jax"
-version = "0.4.10"
+version = "0.4.30"
 description = "Differentiate, compile, and transform Numpy code."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
-ml_dtypes = ">=0.1.0"
-numpy = ">=1.21"
-opt_einsum = "*"
-scipy = ">=1.7"
+importlib-metadata = {version = ">=4.6", markers = "python_version < \"3.10\""}
+jaxlib = ">=0.4.27,<=0.4.30"
+ml-dtypes = ">=0.2.0"
+numpy = [
+    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
+    {version = ">=1.22", markers = "python_version < \"3.11\""},
+]
+opt-einsum = "*"
+scipy = ">=1.9"
 
 [package.extras]
-australis = ["protobuf (>=3.13,<4)"]
-ci = ["jaxlib (==0.4.9)"]
-cpu = ["jaxlib (==0.4.10)"]
-cuda = ["jaxlib (==0.4.10+cuda11.cudnn86)"]
-cuda11-cudnn82 = ["jaxlib (==0.4.10+cuda11.cudnn82)"]
-cuda11-cudnn86 = ["jaxlib (==0.4.10+cuda11.cudnn86)"]
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-minimum-jaxlib = ["jaxlib (==0.4.7)"]
-tpu = ["jaxlib (==0.4.10)", "libtpu-nightly (==0.1.dev20230511)", "requests"]
+ci = ["jaxlib (==0.4.29)"]
+cuda = ["jax-cuda12-plugin[with-cuda] (==0.4.30)", "jaxlib (==0.4.30)"]
+cuda12 = ["jax-cuda12-plugin[with-cuda] (==0.4.30)", "jaxlib (==0.4.30)"]
+cuda12-local = ["jax-cuda12-plugin (==0.4.30)", "jaxlib (==0.4.30)"]
+cuda12-pip = ["jax-cuda12-plugin[with-cuda] (==0.4.30)", "jaxlib (==0.4.30)"]
+minimum-jaxlib = ["jaxlib (==0.4.27)"]
+tpu = ["jaxlib (==0.4.30)", "libtpu-nightly (==0.1.dev20240617)", "requests"]
 
 [[package]]
 name = "jaxlib"
-version = "0.4.10"
+version = "0.4.30"
 description = "XLA library for JAX"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
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-scipy = ">=1.7"
+ml-dtypes = ">=0.2.0"
+numpy = ">=1.22"
+scipy = ">=1.9"
 
 [[package]]
 name = "jedi"
-version = "0.18.2"
+version = "0.19.1"
 description = "An autocompletion tool for Python that can be used for text editors."
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
 [package.dependencies]
-parso = ">=0.8.0,<0.9.0"
+parso = ">=0.8.3,<0.9.0"
 
 [package.extras]
 docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx (==1.8.5)", "sphinx-rtd-theme (==0.4.3)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"]
-qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
-testing = ["Django (<3.1)", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
+qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
+testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
 [[package]]
 name = "jinja2"
-version = "3.1.3"
+version = "3.1.4"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "Jinja2-3.1.3-py3-none-any.whl", hash = "sha256:7d6d50dd97d52cbc355597bd845fabfbac3f551e1f99619e39a35ce8c370b5fa"},
-    {file = "Jinja2-3.1.3.tar.gz", hash = "sha256:ac8bd6544d4bb2c9792bf3a159e80bba8fda7f07e81bc3aed565432d5925ba90"},
+    {file = "jinja2-3.1.4-py3-none-any.whl", hash = "sha256:bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d"},
+    {file = "jinja2-3.1.4.tar.gz", hash = "sha256:4a3aee7acbbe7303aede8e9648d13b8bf88a429282aa6122a993f0ac800cb369"},
 ]
 
 [package.dependencies]
@@ -2162,46 +2157,55 @@ files = [
 
 [[package]]
 name = "joblib"
-version = "1.2.0"
+version = "1.4.2"
 description = "Lightweight pipelining with Python functions"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "joblib-1.2.0-py3-none-any.whl", hash = "sha256:091138ed78f800342968c523bdde947e7a305b8594b910a0fea2ab83c3c6d385"},
-    {file = "joblib-1.2.0.tar.gz", hash = "sha256:e1cee4a79e4af22881164f218d4311f60074197fb707e082e803b61f6d137018"},
+    {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"},
+    {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
+]
+
+[[package]]
+name = "json5"
+version = "0.9.25"
+description = "A Python implementation of the JSON5 data format."
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "json5-0.9.25-py3-none-any.whl", hash = "sha256:34ed7d834b1341a86987ed52f3f76cd8ee184394906b6e22a1e0deb9ab294e8f"},
+    {file = "json5-0.9.25.tar.gz", hash = "sha256:548e41b9be043f9426776f05df8635a00fe06104ea51ed24b67f908856e151ae"},
 ]
 
 [[package]]
 name = "jsonpointer"
-version = "2.3"
+version = "3.0.0"
 description = "Identify specific nodes in a JSON document (RFC 6901)"
 optional = true
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = ">=3.7"
 files = [
-    {file = "jsonpointer-2.3-py2.py3-none-any.whl", hash = "sha256:51801e558539b4e9cd268638c078c6c5746c9ac96bc38152d443400e4f3793e9"},
-    {file = "jsonpointer-2.3.tar.gz", hash = "sha256:97cba51526c829282218feb99dab1b1e6bdf8efd1c43dc9d57be093c0d69c99a"},
+    {file = "jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942"},
+    {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"},
 ]
 
 [[package]]
 name = "jsonschema"
-version = "4.20.0"
+version = "4.22.0"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jsonschema-4.20.0-py3-none-any.whl", hash = "sha256:ed6231f0429ecf966f5bc8dfef245998220549cbbcf140f913b7464c52c3b6b3"},
-    {file = "jsonschema-4.20.0.tar.gz", hash = "sha256:4f614fd46d8d61258610998997743ec5492a648b33cf478c1ddc23ed4598a5fa"},
+    {file = "jsonschema-4.22.0-py3-none-any.whl", hash = "sha256:ff4cfd6b1367a40e7bc6411caec72effadd3db0bbe5017de188f2d6108335802"},
+    {file = "jsonschema-4.22.0.tar.gz", hash = "sha256:5b22d434a45935119af990552c862e5d6d564e8f6601206b305a61fdf661a2b7"},
 ]
 
 [package.dependencies]
 attrs = ">=22.2.0"
 fqdn = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
 idna = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
-importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
 isoduration = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
 jsonpointer = {version = ">1.13", optional = true, markers = "extra == \"format-nongpl\""}
 jsonschema-specifications = ">=2023.03.6"
-pkgutil-resolve-name = {version = ">=1.3.10", markers = "python_version < \"3.9\""}
 referencing = ">=0.28.4"
 rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
 rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""}
@@ -2215,17 +2219,16 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-
 
 [[package]]
 name = "jsonschema-specifications"
-version = "2023.11.2"
+version = "2023.12.1"
 description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jsonschema_specifications-2023.11.2-py3-none-any.whl", hash = "sha256:e74ba7c0a65e8cb49dc26837d6cfe576557084a8b423ed16a420984228104f93"},
-    {file = "jsonschema_specifications-2023.11.2.tar.gz", hash = "sha256:9472fc4fea474cd74bea4a2b190daeccb5a9e4db2ea80efcf7a1b582fc9a81b8"},
+    {file = "jsonschema_specifications-2023.12.1-py3-none-any.whl", hash = "sha256:87e4fdf3a94858b8a2ba2778d9ba57d8a9cafca7c7489c46ba0d30a8bc6a9c3c"},
+    {file = "jsonschema_specifications-2023.12.1.tar.gz", hash = "sha256:48a76787b3e70f5ed53f1160d2b81f586e4ca6d1548c5de7085d1682674764cc"},
 ]
 
 [package.dependencies]
-importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
 referencing = ">=0.31.0"
 
 [[package]]
@@ -2277,13 +2280,13 @@ testing = ["coverage", "ipykernel", "jupytext", "matplotlib", "nbdime", "nbforma
 
 [[package]]
 name = "jupyter-client"
-version = "8.2.0"
+version = "8.6.2"
 description = "Jupyter protocol implementation and client libraries"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_client-8.2.0-py3-none-any.whl", hash = "sha256:b18219aa695d39e2ad570533e0d71fb7881d35a873051054a84ee2a17c4b7389"},
-    {file = "jupyter_client-8.2.0.tar.gz", hash = "sha256:9fe233834edd0e6c0aa5f05ca2ab4bdea1842bfd2d8a932878212fc5301ddaf0"},
+    {file = "jupyter_client-8.6.2-py3-none-any.whl", hash = "sha256:50cbc5c66fd1b8f65ecb66bc490ab73217993632809b6e505687de18e9dea39f"},
+    {file = "jupyter_client-8.6.2.tar.gz", hash = "sha256:2bda14d55ee5ba58552a8c53ae43d215ad9868853489213f37da060ced54d8df"},
 ]
 
 [package.dependencies]
@@ -2296,7 +2299,7 @@ traitlets = ">=5.3"
 
 [package.extras]
 docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
-test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
+test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest (<8.2.0)", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-console"
@@ -2324,13 +2327,13 @@ test = ["flaky", "pexpect", "pytest"]
 
 [[package]]
 name = "jupyter-core"
-version = "5.3.0"
+version = "5.7.2"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_core-5.3.0-py3-none-any.whl", hash = "sha256:d4201af84559bc8c70cead287e1ab94aeef3c512848dde077b7684b54d67730d"},
-    {file = "jupyter_core-5.3.0.tar.gz", hash = "sha256:6db75be0c83edbf1b7c9f91ec266a9a24ef945da630f3120e1a0046dc13713fc"},
+    {file = "jupyter_core-5.7.2-py3-none-any.whl", hash = "sha256:4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409"},
+    {file = "jupyter_core-5.7.2.tar.gz", hash = "sha256:aa5f8d32bbf6b431ac830496da7392035d6f61b4f54872f15c4bd2a9c3f536d9"},
 ]
 
 [package.dependencies]
@@ -2339,18 +2342,18 @@ pywin32 = {version = ">=300", markers = "sys_platform == \"win32\" and platform_
 traitlets = ">=5.3"
 
 [package.extras]
-docs = ["myst-parser", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
-test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
+test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-events"
-version = "0.9.0"
+version = "0.10.0"
 description = "Jupyter Event System library"
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_events-0.9.0-py3-none-any.whl", hash = "sha256:d853b3c10273ff9bc8bb8b30076d65e2c9685579db736873de6c2232dde148bf"},
-    {file = "jupyter_events-0.9.0.tar.gz", hash = "sha256:81ad2e4bc710881ec274d31c6c50669d71bbaa5dd9d01e600b56faa85700d399"},
+    {file = "jupyter_events-0.10.0-py3-none-any.whl", hash = "sha256:4b72130875e59d57716d327ea70d3ebc3af1944d3717e5a498b8a06c6c159960"},
+    {file = "jupyter_events-0.10.0.tar.gz", hash = "sha256:670b8229d3cc882ec782144ed22e0d29e1c2d639263f92ca8383e66682845e22"},
 ]
 
 [package.dependencies]
@@ -2367,51 +2370,66 @@ cli = ["click", "rich"]
 docs = ["jupyterlite-sphinx", "myst-parser", "pydata-sphinx-theme", "sphinxcontrib-spelling"]
 test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "pytest-console-scripts", "rich"]
 
+[[package]]
+name = "jupyter-lsp"
+version = "2.2.5"
+description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "jupyter-lsp-2.2.5.tar.gz", hash = "sha256:793147a05ad446f809fd53ef1cd19a9f5256fd0a2d6b7ce943a982cb4f545001"},
+    {file = "jupyter_lsp-2.2.5-py3-none-any.whl", hash = "sha256:45fbddbd505f3fbfb0b6cb2f1bc5e15e83ab7c79cd6e89416b248cb3c00c11da"},
+]
+
+[package.dependencies]
+importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
+jupyter-server = ">=1.1.2"
+
 [[package]]
 name = "jupyter-server"
-version = "2.11.2"
+version = "2.14.1"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_server-2.11.2-py3-none-any.whl", hash = "sha256:0c548151b54bcb516ca466ec628f7f021545be137d01b5467877e87f6fff4374"},
-    {file = "jupyter_server-2.11.2.tar.gz", hash = "sha256:0c99f9367b0f24141e527544522430176613f9249849be80504c6d2b955004bb"},
+    {file = "jupyter_server-2.14.1-py3-none-any.whl", hash = "sha256:16f7177c3a4ea8fe37784e2d31271981a812f0b2874af17339031dc3510cc2a5"},
+    {file = "jupyter_server-2.14.1.tar.gz", hash = "sha256:12558d158ec7a0653bf96cc272bc7ad79e0127d503b982ed144399346694f726"},
 ]
 
 [package.dependencies]
 anyio = ">=3.1.0"
-argon2-cffi = "*"
-jinja2 = "*"
+argon2-cffi = ">=21.1"
+jinja2 = ">=3.0.3"
 jupyter-client = ">=7.4.4"
 jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
 jupyter-events = ">=0.9.0"
-jupyter-server-terminals = "*"
+jupyter-server-terminals = ">=0.4.4"
 nbconvert = ">=6.4.4"
 nbformat = ">=5.3.0"
-overrides = "*"
-packaging = "*"
-prometheus-client = "*"
-pywinpty = {version = "*", markers = "os_name == \"nt\""}
+overrides = ">=5.0"
+packaging = ">=22.0"
+prometheus-client = ">=0.9"
+pywinpty = {version = ">=2.0.1", markers = "os_name == \"nt\""}
 pyzmq = ">=24"
 send2trash = ">=1.8.2"
 terminado = ">=0.8.3"
 tornado = ">=6.2.0"
 traitlets = ">=5.6.0"
-websocket-client = "*"
+websocket-client = ">=1.7"
 
 [package.extras]
-docs = ["ipykernel", "jinja2", "jupyter-client", "jupyter-server", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-openapi (>=0.8.0)", "sphinxcontrib-spelling", "sphinxemoji", "tornado", "typing-extensions"]
-test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.4)", "pytest-timeout", "requests"]
+docs = ["ipykernel", "jinja2", "jupyter-client", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-openapi (>=0.8.0)", "sphinxcontrib-spelling", "sphinxemoji", "tornado", "typing-extensions"]
+test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0,<9)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.7)", "pytest-timeout", "requests"]
 
 [[package]]
 name = "jupyter-server-terminals"
-version = "0.4.4"
+version = "0.5.3"
 description = "A Jupyter Server Extension Providing Terminals."
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_server_terminals-0.4.4-py3-none-any.whl", hash = "sha256:75779164661cec02a8758a5311e18bb8eb70c4e86c6b699403100f1585a12a36"},
-    {file = "jupyter_server_terminals-0.4.4.tar.gz", hash = "sha256:57ab779797c25a7ba68e97bcfb5d7740f2b5e8a83b5e8102b10438041a7eac5d"},
+    {file = "jupyter_server_terminals-0.5.3-py3-none-any.whl", hash = "sha256:41ee0d7dc0ebf2809c668e0fc726dfaf258fcd3e769568996ca731b6194ae9aa"},
+    {file = "jupyter_server_terminals-0.5.3.tar.gz", hash = "sha256:5ae0295167220e9ace0edcfdb212afd2b01ee8d179fe6f23c899590e9b8a5269"},
 ]
 
 [package.dependencies]
@@ -2419,52 +2437,120 @@ pywinpty = {version = ">=2.0.3", markers = "os_name == \"nt\""}
 terminado = ">=0.8.3"
 
 [package.extras]
-docs = ["jinja2", "jupyter-server", "mistune (<3.0)", "myst-parser", "nbformat", "packaging", "pydata-sphinx-theme", "sphinxcontrib-github-alt", "sphinxcontrib-openapi", "sphinxcontrib-spelling", "sphinxemoji", "tornado"]
-test = ["coverage", "jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-cov", "pytest-jupyter[server] (>=0.5.3)", "pytest-timeout"]
+docs = ["jinja2", "jupyter-server", "mistune (<4.0)", "myst-parser", "nbformat", "packaging", "pydata-sphinx-theme", "sphinxcontrib-github-alt", "sphinxcontrib-openapi", "sphinxcontrib-spelling", "sphinxemoji", "tornado"]
+test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>=0.5.3)", "pytest-timeout"]
+
+[[package]]
+name = "jupyterlab"
+version = "4.2.3"
+description = "JupyterLab computational environment"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "jupyterlab-4.2.3-py3-none-any.whl", hash = "sha256:0b59d11808e84bb84105c73364edfa867dd475492429ab34ea388a52f2e2e596"},
+    {file = "jupyterlab-4.2.3.tar.gz", hash = "sha256:df6e46969ea51d66815167f23d92f105423b7f1f06fa604d4f44aeb018c82c7b"},
+]
+
+[package.dependencies]
+async-lru = ">=1.0.0"
+httpx = ">=0.25.0"
+importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
+ipykernel = ">=6.5.0"
+jinja2 = ">=3.0.3"
+jupyter-core = "*"
+jupyter-lsp = ">=2.0.0"
+jupyter-server = ">=2.4.0,<3"
+jupyterlab-server = ">=2.27.1,<3"
+notebook-shim = ">=0.2"
+packaging = "*"
+setuptools = ">=40.1.0"
+tomli = {version = ">=1.2.2", markers = "python_version < \"3.11\""}
+tornado = ">=6.2.0"
+traitlets = "*"
+
+[package.extras]
+dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.3.5)"]
+docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<7.3.0)", "sphinx-copybutton"]
+docs-screenshots = ["altair (==5.3.0)", "ipython (==8.16.1)", "ipywidgets (==8.1.2)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.1.post2)", "matplotlib (==3.8.3)", "nbconvert (>=7.0.0)", "pandas (==2.2.1)", "scipy (==1.12.0)", "vega-datasets (==0.9.0)"]
+test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
+upgrade-extension = ["copier (>=8,<10)", "jinja2-time (<0.3)", "pydantic (<2.0)", "pyyaml-include (<2.0)", "tomli-w (<2.0)"]
 
 [[package]]
 name = "jupyterlab-pygments"
-version = "0.2.2"
+version = "0.3.0"
 description = "Pygments theme using JupyterLab CSS variables"
 optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+test-ui = ["calysto-bash"]
 
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 name = "keras"
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 testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
 
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 importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
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 python-dateutil = ">=2.7"
 
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+dev = ["meson-python (>=0.13.1)", "numpy (>=1.25)", "pybind11 (>=2.6)", "setuptools (>=64)", "setuptools_scm (>=7)"]
+
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@@ -2874,21 +3063,21 @@ traitlets = "*"
 
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 testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
 
 [[package]]
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 [package.dependencies]
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+    {version = ">1.20", markers = "python_version < \"3.10\""},
 ]
 
 [package.extras]
@@ -2968,184 +3157,192 @@ tests = ["pytest (>=4.6)"]
 
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-dill = ">=0.3.6"
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 [[package]]
 name = "multitasking"
@@ -3160,92 +3357,57 @@ files = [
 
 [[package]]
 name = "myst-nb"
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+version = "1.1.1"
 description = "A Jupyter Notebook Sphinx reader built on top of the MyST markdown parser."
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-
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-version = "0.18.1"
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-optional = false
-python-versions = ">=3.7"
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 pyyaml = "*"
-sphinx = ">=4,<6"
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 typing-extensions = "*"
 
 [package.extras]
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 [[package]]
-name = "nbclassic"
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-description = "Jupyter Notebook as a Jupyter Server extension."
-optional = true
-python-versions = ">=3.7"
+name = "myst-parser"
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+description = "An extended [CommonMark](https://spec.commonmark.org/) compliant parser,"
+optional = false
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
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 jinja2 = "*"
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-Send2Trash = ">=1.8.0"
-terminado = ">=0.8.3"
-tornado = ">=6.1"
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+markdown-it-py = ">=3.0,<4.0"
+mdit-py-plugins = ">=0.4,<1.0"
+pyyaml = "*"
+sphinx = ">=6,<8"
 
 [package.extras]
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-json-logging = ["json-logging"]
-test = ["coverage", "nbval", "pytest", "pytest-cov", "pytest-jupyter", "pytest-playwright", "pytest-tornasync", "requests", "requests-unixsocket", "testpath"]
+code-style = ["pre-commit (>=3.0,<4.0)"]
+linkify = ["linkify-it-py (>=2.0,<3.0)"]
+rtd = ["ipython", "sphinx (>=7)", "sphinx-autodoc2 (>=0.5.0,<0.6.0)", "sphinx-book-theme (>=1.1,<2.0)", "sphinx-copybutton", "sphinx-design", "sphinx-pyscript", "sphinx-tippy (>=0.4.3)", "sphinx-togglebutton", "sphinxext-opengraph (>=0.9.0,<0.10.0)", "sphinxext-rediraffe (>=0.2.7,<0.3.0)"]
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+testing-docutils = ["pygments", "pytest (>=8,<9)", "pytest-param-files (>=0.6.0,<0.7.0)"]
 
 [[package]]
 name = "nbclient"
@@ -3271,57 +3433,57 @@ test = ["flaky", "ipykernel", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "p
 
 [[package]]
 name = "nbconvert"
-version = "7.4.0"
-description = "Converting Jupyter Notebooks"
+version = "7.16.4"
+description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
 optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 beautifulsoup4 = "*"
-bleach = "*"
+bleach = "!=5.0.0"
 defusedxml = "*"
 importlib-metadata = {version = ">=3.6", markers = "python_version < \"3.10\""}
 jinja2 = ">=3.0"
 jupyter-core = ">=4.7"
 jupyterlab-pygments = "*"
 markupsafe = ">=2.0"
-mistune = ">=2.0.3,<3"
+mistune = ">=2.0.3,<4"
 nbclient = ">=0.5.0"
-nbformat = ">=5.1"
+nbformat = ">=5.7"
 packaging = "*"
 pandocfilters = ">=1.4.1"
 pygments = ">=2.4.1"
 tinycss2 = "*"
-traitlets = ">=5.0"
+traitlets = ">=5.1"
 
 [package.extras]
-all = ["nbconvert[docs,qtpdf,serve,test,webpdf]"]
+all = ["flaky", "ipykernel", "ipython", "ipywidgets (>=7.5)", "myst-parser", "nbsphinx (>=0.2.12)", "playwright", "pydata-sphinx-theme", "pyqtwebengine (>=5.15)", "pytest (>=7)", "sphinx (==5.0.2)", "sphinxcontrib-spelling", "tornado (>=6.1)"]
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-qtpdf = ["nbconvert[qtpng]"]
+qtpdf = ["pyqtwebengine (>=5.15)"]
 qtpng = ["pyqtwebengine (>=5.15)"]
 serve = ["tornado (>=6.1)"]
-test = ["ipykernel", "ipywidgets (>=7)", "pre-commit", "pytest", "pytest-dependency"]
-webpdf = ["pyppeteer (>=1,<1.1)"]
+test = ["flaky", "ipykernel", "ipywidgets (>=7.5)", "pytest (>=7)"]
+webpdf = ["playwright"]
 
 [[package]]
 name = "nbformat"
-version = "5.8.0"
+version = "5.10.4"
 description = "The Jupyter Notebook format"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 [package.dependencies]
-fastjsonschema = "*"
+fastjsonschema = ">=2.15"
 jsonschema = ">=2.6"
-jupyter-core = "*"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
 traitlets = ">=5.1"
 
 [package.extras]
@@ -3364,90 +3526,76 @@ sphinx = ">=1.8"
 
 [[package]]
 name = "nest-asyncio"
-version = "1.5.6"
+version = "1.6.0"
 description = "Patch asyncio to allow nested event loops"
 optional = false
 python-versions = ">=3.5"
 files = [
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 ]
 
 [[package]]
 name = "networkx"
-version = "3.1"
+version = "3.2.1"
 description = "Python package for creating and manipulating graphs and networks"
 optional = true
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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+test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
 [[package]]
 name = "nodeenv"
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 description = "Node.js virtual environment builder"
 optional = false
-python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*"
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-[package.dependencies]
-setuptools = "*"
-
 [[package]]
 name = "notebook"
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+version = "7.2.1"
+description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
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-terminado = ">=0.8.3"
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+jupyterlab-server = ">=2.27.1,<3"
+notebook-shim = ">=0.2,<0.3"
+tornado = ">=6.2.0"
 
 [package.extras]
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-json-logging = ["json-logging"]
-test = ["coverage", "nbval", "pytest", "pytest-cov", "requests", "requests-unixsocket", "selenium (==4.1.5)", "testpath"]
+dev = ["hatch", "pre-commit"]
+docs = ["myst-parser", "nbsphinx", "pydata-sphinx-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
+test = ["importlib-resources (>=5.0)", "ipykernel", "jupyter-server[test] (>=2.4.0,<3)", "jupyterlab-server[test] (>=2.27.1,<3)", "nbval", "pytest (>=7.0)", "pytest-console-scripts", "pytest-timeout", "pytest-tornasync", "requests"]
 
 [[package]]
 name = "notebook-shim"
-version = "0.2.3"
+version = "0.2.4"
 description = "A shim layer for notebook traits and config"
 optional = true
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -3458,74 +3606,73 @@ test = ["pytest", "pytest-console-scripts", "pytest-jupyter", "pytest-tornasync"
 
 [[package]]
 name = "numba"
-version = "0.58.1"
+version = "0.60.0"
 description = "compiling Python code using LLVM"
 optional = true
-python-versions = ">=3.8"
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 name = "numpy"
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 python-versions = ">=3"
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@@ -3735,282 +3882,292 @@ tests = ["pytest", "pytest-cov", "pytest-pep8"]
 
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 jax = ">=0.1.55"
 jaxlib = ">=0.1.37"
 numpy = ">=1.18.0"
 
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 numpy = "*"
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+tensorstore = ">=0.1.51"
 typing_extensions = "*"
 
 [package.extras]
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+testing = ["flax", "google-cloud-logging", "mock", "pytest", "pytest-xdist"]
 
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 ]
 
 [package.extras]
@@ -4021,41 +4178,31 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa
 typing = ["typing-extensions"]
 xmp = ["defusedxml"]
 
-[[package]]
-name = "pkgutil-resolve-name"
-version = "1.3.10"
-description = "Resolve a name to an object."
-optional = false
-python-versions = ">=3.6"
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-
 [[package]]
 name = "platformdirs"
-version = "3.5.1"
-description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+version = "4.2.2"
+description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
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-test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.3.1)", "pytest-cov (>=4)", "pytest-mock (>=3.10)"]
+docs = ["furo (>=2023.9.10)", "proselint (>=0.13)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
+type = ["mypy (>=1.8)"]
 
 [[package]]
 name = "pluggy"
-version = "1.0.0"
+version = "1.5.0"
 description = "plugin and hook calling mechanisms for python"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -4064,38 +4211,38 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "pox"
-version = "0.3.2"
+version = "0.3.4"
 description = "utilities for filesystem exploration and automated builds"
 optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
 name = "ppft"
-version = "1.7.6.6"
-description = "distributed and parallel python"
+version = "1.7.6.8"
+description = "distributed and parallel Python"
 optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
-dill = ["dill (>=0.3.6)"]
+dill = ["dill (>=0.3.8)"]
 
 [[package]]
 name = "pre-commit"
-version = "3.3.1"
+version = "3.7.1"
 description = "A framework for managing and maintaining multi-language pre-commit hooks."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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+    {file = "pre_commit-3.7.1-py2.py3-none-any.whl", hash = "sha256:fae36fd1d7ad7d6a5a1c0b0d5adb2ed1a3bda5a21bf6c3e5372073d7a11cd4c5"},
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 ]
 
 [package.dependencies]
@@ -4107,13 +4254,13 @@ virtualenv = ">=20.10.0"
 
 [[package]]
 name = "prometheus-client"
-version = "0.16.0"
+version = "0.20.0"
 description = "Python client for the Prometheus monitoring system."
 optional = true
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
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-    {file = "prometheus_client-0.16.0.tar.gz", hash = "sha256:a03e35b359f14dd1630898543e2120addfdeacd1a6069c1367ae90fd93ad3f48"},
+    {file = "prometheus_client-0.20.0-py3-none-any.whl", hash = "sha256:cde524a85bce83ca359cc837f28b8c0db5cac7aa653a588fd7e84ba061c329e7"},
+    {file = "prometheus_client-0.20.0.tar.gz", hash = "sha256:287629d00b147a32dcb2be0b9df905da599b2d82f80377083ec8463309a4bb89"},
 ]
 
 [package.extras]
@@ -4137,13 +4284,13 @@ test = ["coveralls", "futures", "mock", "pytest (>=2.7.3)", "pytest-benchmark",
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.38"
+version = "3.0.47"
 description = "Library for building powerful interactive command lines in Python"
 optional = false
 python-versions = ">=3.7.0"
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 [package.dependencies]
 numpy = ">=1.16.6"
 
+[[package]]
+name = "pyarrow-hotfix"
+version = "0.6"
+description = ""
+optional = true
+python-versions = ">=3.5"
+files = [
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+
 [[package]]
 name = "pyasn1"
-version = "0.5.0"
+version = "0.6.0"
 description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs (X.208)"
 optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
 name = "pyasn1-modules"
-version = "0.3.0"
+version = "0.4.0"
 description = "A collection of ASN.1-based protocols modules"
 optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
-pyasn1 = ">=0.4.6,<0.6.0"
+pyasn1 = ">=0.4.6,<0.7.0"
 
 [[package]]
 name = "pycparser"
-version = "2.21"
+version = "2.22"
 description = "C parser in Python"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
@@ -4355,27 +4522,27 @@ test = ["pydata-sphinx-theme[doc]", "pytest"]
 
 [[package]]
 name = "pygments"
-version = "2.15.1"
+version = "2.18.0"
 description = "Pygments is a syntax highlighting package written in Python."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
-plugins = ["importlib-metadata"]
+windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pynndescent"
-version = "0.5.11"
+version = "0.5.13"
 description = "Nearest Neighbor Descent"
 optional = true
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -4387,13 +4554,13 @@ scipy = ">=1.0"
 
 [[package]]
 name = "pyparsing"
-version = "3.0.9"
+version = "3.1.2"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 optional = false
 python-versions = ">=3.6.8"
 files = [
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 ]
 
 [package.extras]
@@ -4401,13 +4568,13 @@ diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pytest"
-version = "7.3.1"
+version = "7.4.4"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -4419,17 +4586,17 @@ pluggy = ">=0.12,<2.0"
 tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
 
 [package.extras]
-testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "xmlschema"]
+testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
 [[package]]
 name = "pytest-cov"
-version = "4.0.0"
+version = "4.1.0"
 description = "Pytest plugin for measuring coverage."
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
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+    {file = "pytest-cov-4.1.0.tar.gz", hash = "sha256:3904b13dfbfec47f003b8e77fd5b589cd11904a21ddf1ab38a64f204d6a10ef6"},
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 ]
 
 [package.dependencies]
@@ -4441,13 +4608,13 @@ testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtuale
 
 [[package]]
 name = "python-dateutil"
-version = "2.8.2"
+version = "2.9.0.post0"
 description = "Extensions to the standard Python datetime module"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -4466,13 +4633,13 @@ files = [
 
 [[package]]
 name = "pytz"
-version = "2023.3"
+version = "2024.1"
 description = "World timezone definitions, modern and historical"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [[package]]
@@ -4500,152 +4667,174 @@ files = [
 
 [[package]]
 name = "pywinpty"
-version = "2.0.10"
+version = "2.0.13"
 description = "Pseudo terminal support for Windows from Python."
 optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
 name = "pyyaml"
-version = "6.0"
+version = "6.0.1"
 description = "YAML parser and emitter for Python"
 optional = false
 python-versions = ">=3.6"
 files = [
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@@ -4653,24 +4842,23 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""}
 
 [[package]]
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 [package.extras]
@@ -4679,13 +4867,13 @@ test = ["flaky", "pytest", "pytest-qt"]
 
 [[package]]
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 [[package]]
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 [[package]]
 name = "regex"
-version = "2023.5.5"
+version = "2024.5.15"
 description = "Alternative regular expression module, to replace re."
 optional = true
-python-versions = ">=3.6"
+python-versions = ">=3.8"
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 ]
 
 [[package]]
 name = "requests"
-version = "2.31.0"
+version = "2.32.3"
 description = "Python HTTP for Humans."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+    {file = "requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760"},
 ]
 
 [package.dependencies]
@@ -4829,13 +5008,13 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
 name = "requests-oauthlib"
-version = "1.3.1"
+version = "2.0.0"
 description = "OAuthlib authentication support for Requests."
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = ">=3.4"
 files = [
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 ]
 
 [package.dependencies]
@@ -4845,24 +5024,6 @@ requests = ">=2.0.0"
 [package.extras]
 rsa = ["oauthlib[signedtoken] (>=3.0.0)"]
 
-[[package]]
-name = "responses"
-version = "0.18.0"
-description = "A utility library for mocking out the `requests` Python library."
-optional = true
-python-versions = ">=3.7"
-files = [
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-]
-
-[package.dependencies]
-requests = ">=2.0,<3.0"
-urllib3 = ">=1.25.10"
-
-[package.extras]
-tests = ["coverage (>=6.0.0)", "flake8", "mypy", "pytest (>=4.6)", "pytest-cov", "pytest-localserver", "types-mock", "types-requests"]
-
 [[package]]
 name = "rfc3339-validator"
 version = "0.1.4"
@@ -4890,129 +5051,128 @@ files = [
 
 [[package]]
 name = "rich"
-version = "13.3.5"
+version = "13.7.1"
 description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
 optional = false
 python-versions = ">=3.7.0"
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 ]
 
 [package.dependencies]
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+numpy = ">=1.22.4,<2.3"
 
 [package.extras]
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+dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
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 [[package]]
 name = "send2trash"
-version = "1.8.2"
+version = "1.8.3"
 description = "Send file to trash natively under Mac OS X, Windows and Linux"
 optional = true
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 files = [
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 ]
 
 [package.extras]
@@ -5275,19 +5503,18 @@ win32 = ["pywin32"]
 
 [[package]]
 name = "setuptools"
-version = "67.7.2"
+version = "70.1.1"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 [package.extras]
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 [[package]]
 name = "six"
@@ -5313,13 +5540,13 @@ files = [
 
 [[package]]
 name = "sniffio"
-version = "1.3.0"
+version = "1.3.1"
 description = "Sniff out which async library your code is running under"
 optional = true
 python-versions = ">=3.7"
 files = [
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 ]
 
 [[package]]
@@ -5335,49 +5562,50 @@ files = [
 
 [[package]]
 name = "soupsieve"
-version = "2.4.1"
+version = "2.5"
 description = "A modern CSS selector implementation for Beautiful Soup."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
 name = "sphinx"
-version = "5.3.0"
+version = "7.3.7"
 description = "Python documentation generator"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
-alabaster = ">=0.7,<0.8"
+alabaster = ">=0.7.14,<0.8.0"
 babel = ">=2.9"
 colorama = {version = ">=0.4.5", markers = "sys_platform == \"win32\""}
-docutils = ">=0.14,<0.20"
+docutils = ">=0.18.1,<0.22"
 imagesize = ">=1.3"
 importlib-metadata = {version = ">=4.8", markers = "python_version < \"3.10\""}
 Jinja2 = ">=3.0"
 packaging = ">=21.0"
-Pygments = ">=2.12"
-requests = ">=2.5.0"
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+requests = ">=2.25.0"
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 sphinxcontrib-applehelp = "*"
 sphinxcontrib-devhelp = "*"
 sphinxcontrib-htmlhelp = ">=2.0.0"
 sphinxcontrib-jsmath = "*"
 sphinxcontrib-qthelp = "*"
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 [[package]]
 name = "sphinx-autodoc-typehints"
@@ -5414,47 +5642,50 @@ sphinx = ">=3"
 
 [[package]]
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+version = "1.0.8"
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 [package.extras]
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 test = ["pytest"]
 
 [[package]]
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-description = "sphinxcontrib-devhelp is a sphinx extension which outputs Devhelp document."
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+python-versions = ">=3.9"
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 [package.extras]
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 test = ["pytest"]
 
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 ]
 
 [package.extras]
 lint = ["docutils-stubs", "flake8", "mypy"]
+standalone = ["Sphinx (>=5)"]
 test = ["html5lib", "pytest"]
 
 [[package]]
@@ -5473,82 +5704,92 @@ test = ["flake8", "mypy", "pytest"]
 
 [[package]]
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 [package.extras]
 lint = ["docutils-stubs", "flake8", "mypy"]
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 test = ["pytest"]
 
 [[package]]
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-version = "1.1.5"
-description = "sphinxcontrib-serializinghtml is a sphinx extension which outputs \"serialized\" HTML files (json and pickle)."
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 [package.extras]
 lint = ["docutils-stubs", "flake8", "mypy"]
+standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
 [[package]]
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 ]
 
 [package.dependencies]
@@ -5556,8 +5797,9 @@ greenlet = {version = "!=0.4.17", markers = "platform_machine == \"aarch64\" or
 typing-extensions = ">=4.2.0"
 
 [package.extras]
-aiomysql = ["aiomysql", "greenlet (!=0.4.17)"]
-aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing-extensions (!=3.10.0.1)"]
+aiomysql = ["aiomysql (>=0.2.0)", "greenlet (!=0.4.17)"]
+aioodbc = ["aioodbc", "greenlet (!=0.4.17)"]
+aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing_extensions (!=3.10.0.1)"]
 asyncio = ["greenlet (!=0.4.17)"]
 asyncmy = ["asyncmy (>=0.2.3,!=0.2.4,!=0.2.6)", "greenlet (!=0.4.17)"]
 mariadb-connector = ["mariadb (>=1.0.1,!=1.1.2,!=1.1.5)"]
@@ -5567,7 +5809,7 @@ mssql-pyodbc = ["pyodbc"]
 mypy = ["mypy (>=0.910)"]
 mysql = ["mysqlclient (>=1.4.0)"]
 mysql-connector = ["mysql-connector-python"]
-oracle = ["cx-oracle (>=7)"]
+oracle = ["cx_oracle (>=8)"]
 oracle-oracledb = ["oracledb (>=1.0.1)"]
 postgresql = ["psycopg2 (>=2.7)"]
 postgresql-asyncpg = ["asyncpg", "greenlet (!=0.4.17)"]
@@ -5575,18 +5817,19 @@ postgresql-pg8000 = ["pg8000 (>=1.29.1)"]
 postgresql-psycopg = ["psycopg (>=3.0.7)"]
 postgresql-psycopg2binary = ["psycopg2-binary"]
 postgresql-psycopg2cffi = ["psycopg2cffi"]
+postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
 pymysql = ["pymysql"]
-sqlcipher = ["sqlcipher3-binary"]
+sqlcipher = ["sqlcipher3_binary"]
 
 [[package]]
 name = "stack-data"
-version = "0.6.2"
+version = "0.6.3"
 description = "Extract data from python stack frames and tracebacks for informative displays"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -5599,17 +5842,17 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
 [[package]]
 name = "sympy"
-version = "1.12"
+version = "1.12.1"
 description = "Computer algebra system (CAS) in Python"
 optional = true
 python-versions = ">=3.8"
 files = [
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+    {file = "sympy-1.12.1.tar.gz", hash = "sha256:2877b03f998cd8c08f07cd0de5b767119cd3ef40d09f41c30d722f6686b0fb88"},
 ]
 
 [package.dependencies]
-mpmath = ">=0.19"
+mpmath = ">=1.1.0,<1.4.0"
 
 [[package]]
 name = "tabulate"
@@ -5627,13 +5870,13 @@ widechars = ["wcwidth"]
 
 [[package]]
 name = "tblib"
-version = "1.7.0"
+version = "3.0.0"
 description = "Traceback serialization library."
 optional = true
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
@@ -5662,14 +5905,14 @@ wheel = ">=0.26"
 
 [[package]]
 name = "tensorboard-data-server"
-version = "0.7.0"
+version = "0.7.2"
 description = "Fast data loading for TensorBoard"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [[package]]
@@ -5718,13 +5961,13 @@ wrapt = ">=1.11.0"
 
 [[package]]
 name = "tensorflow-datasets"
-version = "4.9.2"
+version = "4.9.3"
 description = "tensorflow/datasets is a library of datasets ready to use with TensorFlow."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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+    {file = "tensorflow-datasets-4.9.3.tar.gz", hash = "sha256:90390077dde2c9e4e240754ddfc5bb50b482946d421c8a34677c3afdb0463427"},
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 ]
 
 [package.dependencies]
@@ -5732,8 +5975,7 @@ absl-py = "*"
 array-record = "*"
 click = "*"
 dm-tree = "*"
-etils = {version = ">=0.9.0", extras = ["enp", "epath"]}
-importlib-resources = {version = "*", markers = "python_version < \"3.9\""}
+etils = {version = ">=0.9.0", extras = ["enp", "epath", "etree"]}
 numpy = "*"
 promise = "*"
 protobuf = ">=3.20"
@@ -5754,16 +5996,16 @@ c4-wsrs = ["apache-beam"]
 cats-vs-dogs = ["matplotlib"]
 colorectal-histology = ["Pillow"]
 common-voice = ["pydub"]
-dev = ["apache-beam", "conllu", "datasets", "dill", "jax[cpu]", "jupyter", "pandas", "pydub", "pylint (>=2.6.0)", "pytest", "pytest-shard", "pytest-xdist", "pyyaml", "tensorflow-io", "yapf"]
+dev = ["apache-beam", "conllu", "datasets", "dill", "jax[cpu]", "jupyter", "pandas", "pydub", "pylint (>=2.6.0)", "pytest", "pytest-shard", "pytest-xdist", "pyyaml", "tensorflow-io[tensorflow]", "yapf"]
 duke-ultrasound = ["scipy"]
 eurosat = ["imagecodecs", "scikit-image", "tifffile"]
 groove = ["pretty-midi", "pydub"]
 gtzan = ["pydub"]
-huggingface = ["Pillow", "bs4", "conllu", "datasets", "dill", "envlogger", "gcld3", "gcsfs", "h5py", "imagecodecs", "jax[cpu]", "jupyter", "langdetect", "lxml", "matplotlib", "mwparserfromhell", "mwxml", "networkx", "nltk", "opencv-python", "pandas", "pretty-midi", "pycocotools", "pydub", "pytest", "pytest-shard", "pytest-xdist", "pyyaml", "scikit-image", "scipy", "tensorflow-io", "tifffile", "tldextract", "zarr"]
+huggingface = ["Pillow", "bs4", "conllu", "datasets", "dill", "envlogger", "gcld3", "gcsfs", "h5py", "imagecodecs", "jax[cpu]", "jupyter", "langdetect", "lxml", "matplotlib", "mwparserfromhell", "mwxml", "networkx", "nltk", "opencv-python", "pandas", "pretty-midi", "pycocotools", "pydub", "pytest", "pytest-shard", "pytest-xdist", "pyyaml", "scikit-image", "scipy", "tensorflow-io[tensorflow]", "tifffile", "tldextract", "zarr"]
 imagenet2012-corrupted = ["opencv-python", "scikit-image", "scipy"]
 librispeech = ["pydub"]
 locomotion = ["envlogger"]
-lsun = ["tensorflow-io"]
+lsun = ["tensorflow-io[tensorflow]"]
 matplotlib = ["matplotlib"]
 nsynth = ["crepe (>=0.0.11)", "librosa", "scikit-learn (==0.20.3)"]
 ogbg-molpcba = ["networkx", "pandas"]
@@ -5774,7 +6016,8 @@ smartwatch-gestures = ["pandas"]
 svhn = ["scipy"]
 tensorflow = ["tensorflow (>=2.1)"]
 tensorflow-data-validation = ["tensorflow-data-validation"]
-tests-all = ["Pillow", "apache-beam", "bs4", "conllu", "datasets", "dill", "envlogger", "gcld3", "gcsfs", "h5py", "imagecodecs", "jax[cpu]", "jupyter", "langdetect", "lxml", "matplotlib", "mwparserfromhell", "mwxml", "networkx", "nltk", "opencv-python", "pandas", "pretty-midi", "pycocotools", "pydub", "pytest", "pytest-shard", "pytest-xdist", "pyyaml", "scikit-image", "scipy", "tensorflow-io", "tifffile", "tldextract", "zarr"]
+tests-all = ["Pillow", "apache-beam", "bs4", "conllu", "datasets", "dill", "envlogger", "gcld3", "gcsfs", "h5py", "imagecodecs", "jax[cpu]", "jupyter", "langdetect", "lxml", "matplotlib", "mwparserfromhell", "mwxml", "networkx", "nltk", "opencv-python", "pandas", "pretty-midi", "pycocotools", "pydub", "pytest", "pytest-shard", "pytest-xdist", "pyyaml", "scikit-image", "scipy", "tensorflow-io[tensorflow]", "tifffile", "tldextract", "zarr"]
+tf-nightly = ["tf-nightly"]
 the300w-lp = ["scipy"]
 wider-face = ["Pillow"]
 wiki-dialog = ["apache-beam"]
@@ -5794,33 +6037,38 @@ files = [
 
 [[package]]
 name = "tensorflow-io-gcs-filesystem"
-version = "0.32.0"
+version = "0.29.0"
 description = "TensorFlow IO"
 optional = false
 python-versions = ">=3.7, <3.12"
 files = [
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 [[package]]
 name = "tensorflow-macos"
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 [[package]]
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 [package.dependencies]
-numpy = ">=1.16.0"
+ml-dtypes = ">=0.3.1"
+numpy = ">=1.22.0"
 
 [[package]]
 name = "termcolor"
@@ -5917,13 +6173,13 @@ tests = ["pytest", "pytest-cov"]
 
 [[package]]
 name = "terminado"
-version = "0.17.1"
+version = "0.18.1"
 description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
 optional = true
-python-versions = ">=3.7"
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 ]
 
 [package.dependencies]
@@ -5934,27 +6190,28 @@ tornado = ">=6.1.0"
 [package.extras]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
 test = ["pre-commit", "pytest (>=7.0)", "pytest-timeout"]
+typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"]
 
 [[package]]
 name = "threadpoolctl"
-version = "3.1.0"
+version = "3.5.0"
 description = "threadpoolctl"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
 name = "tinycss2"
-version = "1.2.1"
+version = "1.3.0"
 description = "A tiny CSS parser"
 optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -5962,122 +6219,124 @@ webencodings = ">=0.4"
 
 [package.extras]
 doc = ["sphinx", "sphinx_rtd_theme"]
-test = ["flake8", "isort", "pytest"]
+test = ["pytest", "ruff"]
 
 [[package]]
 name = "tokenizers"
-version = "0.15.0"
+version = "0.19.1"
 description = ""
 optional = true
 python-versions = ">=3.7"
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+
+[package.dependencies]
+huggingface-hub = ">=0.16.4,<1.0"
 
 [package.extras]
 dev = ["tokenizers[testing]"]
-docs = ["setuptools_rust", "sphinx", "sphinx_rtd_theme"]
-testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
+docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
+testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"]
 
 [[package]]
 name = "toml"
@@ -6103,42 +6362,42 @@ files = [
 
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 name = "toolz"
-version = "0.12.0"
+version = "0.12.1"
 description = "List processing tools and functional utilities"
 optional = false
-python-versions = ">=3.5"
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 ]
 
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 optional = true
 python-versions = ">=3.8.0"
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 ]
 
 [package.dependencies]
@@ -6167,124 +6426,124 @@ opt-einsum = ["opt-einsum (>=3.3)"]
 
 [[package]]
 name = "tornado"
-version = "6.3.2"
+version = "6.4.1"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 optional = false
-python-versions = ">= 3.8"
+python-versions = ">=3.8"
 files = [
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 [[package]]
 name = "tqdm"
-version = "4.65.0"
+version = "4.66.4"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [package.extras]
-dev = ["py-make (>=0.1.0)", "twine", "wheel"]
+dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
 notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
 [[package]]
 name = "traitlets"
-version = "5.9.0"
+version = "5.14.3"
 description = "Traitlets Python configuration system"
 optional = false
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 name = "transformers"
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 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = true
 python-versions = ">=3.8.0"
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 flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
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 modelcreation = ["cookiecutter (==1.7.3)"]
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 ray = ["ray[tune] (>=2.7.0)"]
 retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
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 sagemaker = ["sagemaker (>=2.31.0)"]
 sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
-serving = ["fastapi", "pydantic (<2)", "starlette", "uvicorn"]
+serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
 sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
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+tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
-timm = ["timm"]
-tokenizers = ["tokenizers (>=0.14,<0.19)"]
-torch = ["accelerate (>=0.21.0)", "torch (>=1.10,!=1.12.0)"]
+timm = ["timm (<=0.9.16)"]
+tokenizers = ["tokenizers (>=0.19,<0.20)"]
+torch = ["accelerate (>=0.21.0)", "torch"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.19)", "torch (>=1.10,!=1.12.0)", "tqdm (>=4.27)"]
+torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17,<2.0)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
 video = ["av (==9.2.0)", "decord (==0.6.0)"]
 vision = ["Pillow (>=10.0.1,<=15.0)"]
 
@@ -6313,6 +6572,17 @@ build = ["cmake (>=3.18)", "lit"]
 tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)"]
 tutorials = ["matplotlib", "pandas", "tabulate"]
 
+[[package]]
+name = "types-python-dateutil"
+version = "2.9.0.20240316"
+description = "Typing stubs for python-dateutil"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "types-python-dateutil-2.9.0.20240316.tar.gz", hash = "sha256:5d2f2e240b86905e40944dd787db6da9263f0deabef1076ddaed797351ec0202"},
+    {file = "types_python_dateutil-2.9.0.20240316-py3-none-any.whl", hash = "sha256:6b8cb66d960771ce5ff974e9dd45e38facb81718cc1e208b10b1baccbfdbee3b"},
+]
+
 [[package]]
 name = "typing-extensions"
 version = "4.5.0"
@@ -6326,23 +6596,24 @@ files = [
 
 [[package]]
 name = "tzdata"
-version = "2023.3"
+version = "2024.1"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
 files = [
-    {file = "tzdata-2023.3-py2.py3-none-any.whl", hash = "sha256:7e65763eef3120314099b6939b5546db7adce1e7d6f2e179e3df563c70511eda"},
-    {file = "tzdata-2023.3.tar.gz", hash = "sha256:11ef1e08e54acb0d4f95bdb1be05da659673de4acbd21bf9c69e94cc5e907a3a"},
+    {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
+    {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
 ]
 
 [[package]]
 name = "umap-learn"
-version = "0.5.5"
+version = "0.5.6"
 description = "Uniform Manifold Approximation and Projection"
 optional = true
 python-versions = "*"
 files = [
-    {file = "umap-learn-0.5.5.tar.gz", hash = "sha256:c54d607364413eade968b73ba07c8b3ea14412817f53cd07b6f720ac957293c4"},
+    {file = "umap-learn-0.5.6.tar.gz", hash = "sha256:5b3917a862c23ba0fc83bfcd67a7b719dec85b3d9c01fdc7d894cce455df4e03"},
+    {file = "umap_learn-0.5.6-py3-none-any.whl", hash = "sha256:881cc0c2ee845b790bf0455aa1664f9f68b838d9d0fe12a1291b85c5a559c913"},
 ]
 
 [package.dependencies]
@@ -6354,33 +6625,33 @@ scipy = ">=1.3.1"
 tqdm = "*"
 
 [package.extras]
-parametric-umap = ["tensorflow (>=2.1)", "tensorflow-probability (>=0.10)"]
+parametric-umap = ["tensorflow (>=2.1)"]
 plot = ["bokeh", "colorcet", "datashader", "holoviews", "matplotlib", "pandas", "scikit-image", "seaborn"]
 tbb = ["tbb (>=2019.0)"]
 
 [[package]]
 name = "uri-template"
-version = "1.2.0"
+version = "1.3.0"
 description = "RFC 6570 URI Template Processor"
 optional = true
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
-    {file = "uri_template-1.2.0-py3-none-any.whl", hash = "sha256:f1699c77b73b925cf4937eae31ab282a86dc885c333f2e942513f08f691fc7db"},
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+    {file = "uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7"},
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 ]
 
 [package.extras]
-dev = ["flake8 (<4.0.0)", "flake8-annotations", "flake8-bugbear", "flake8-commas", "flake8-comprehensions", "flake8-continuation", "flake8-datetimez", "flake8-docstrings", "flake8-import-order", "flake8-literal", "flake8-noqa", "flake8-requirements", "flake8-type-annotations", "flake8-use-fstring", "mypy", "pep8-naming"]
+dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake8-commas", "flake8-comprehensions", "flake8-continuation", "flake8-datetimez", "flake8-docstrings", "flake8-import-order", "flake8-literal", "flake8-modern-annotations", "flake8-noqa", "flake8-pyproject", "flake8-requirements", "flake8-typechecking-import", "flake8-use-fstring", "mypy", "pep8-naming", "types-PyYAML"]
 
 [[package]]
 name = "urllib3"
-version = "1.26.18"
+version = "1.26.19"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 files = [
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+    {file = "urllib3-1.26.19-py2.py3-none-any.whl", hash = "sha256:37a0344459b199fce0e80b0d3569837ec6b6937435c5244e7fd73fa6006830f3"},
+    {file = "urllib3-1.26.19.tar.gz", hash = "sha256:3e3d753a8618b86d7de333b4223005f68720bcd6a7d2bcb9fbd2229ec7c1e429"},
 ]
 
 [package.extras]
@@ -6388,51 +6659,68 @@ brotli = ["brotli (==1.0.9)", "brotli (>=1.0.9)", "brotlicffi (>=0.8.0)", "brotl
 secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress", "pyOpenSSL (>=0.14)", "urllib3-secure-extra"]
 socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
 
+[[package]]
+name = "urllib3"
+version = "2.2.2"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"},
+    {file = "urllib3-2.2.2.tar.gz", hash = "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"},
+]
+
+[package.extras]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+h2 = ["h2 (>=4,<5)"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
+
 [[package]]
 name = "virtualenv"
-version = "20.23.0"
+version = "20.26.3"
 description = "Virtual Python Environment builder"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "virtualenv-20.23.0-py3-none-any.whl", hash = "sha256:6abec7670e5802a528357fdc75b26b9f57d5d92f29c5462ba0fbe45feacc685e"},
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+    {file = "virtualenv-20.26.3-py3-none-any.whl", hash = "sha256:8cc4a31139e796e9a7de2cd5cf2489de1217193116a8fd42328f1bd65f434589"},
+    {file = "virtualenv-20.26.3.tar.gz", hash = "sha256:4c43a2a236279d9ea36a0d76f98d84bd6ca94ac4e0f4a3b9d46d05e10fea542a"},
 ]
 
 [package.dependencies]
-distlib = ">=0.3.6,<1"
-filelock = ">=3.11,<4"
-platformdirs = ">=3.2,<4"
+distlib = ">=0.3.7,<1"
+filelock = ">=3.12.2,<4"
+platformdirs = ">=3.9.1,<5"
 
 [package.extras]
-docs = ["furo (>=2023.3.27)", "proselint (>=0.13)", "sphinx (>=6.1.3)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=22.12)"]
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+docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2,!=7.3)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
+test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"]
 
 [[package]]
 name = "wcwidth"
-version = "0.2.6"
+version = "0.2.13"
 description = "Measures the displayed width of unicode strings in a terminal"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [[package]]
 name = "webcolors"
-version = "1.13"
+version = "24.6.0"
 description = "A library for working with the color formats defined by HTML and CSS."
 optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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-    {file = "webcolors-1.13.tar.gz", hash = "sha256:c225b674c83fa923be93d235330ce0300373d02885cef23238813b0d5668304a"},
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+    {file = "webcolors-24.6.0.tar.gz", hash = "sha256:1d160d1de46b3e81e58d0a280d0c78b467dc80f47294b91b1ad8029d2cedb55b"},
 ]
 
 [package.extras]
 docs = ["furo", "sphinx", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-notfound-page", "sphinxext-opengraph"]
-tests = ["pytest", "pytest-cov"]
+tests = ["coverage[toml]"]
 
 [[package]]
 name = "webencodings"
@@ -6447,17 +6735,17 @@ files = [
 
 [[package]]
 name = "websocket-client"
-version = "1.5.1"
+version = "1.8.0"
 description = "WebSocket client for Python with low level API options"
 optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+    {file = "websocket_client-1.8.0.tar.gz", hash = "sha256:3239df9f44da632f96012472805d40a23281a991027ce11d2f45a6f24ac4c3da"},
 ]
 
 [package.extras]
-docs = ["Sphinx (>=3.4)", "sphinx-rtd-theme (>=0.5)"]
+docs = ["Sphinx (>=6.0)", "myst-parser (>=2.0.0)", "sphinx-rtd-theme (>=1.1.0)"]
 optional = ["python-socks", "wsaccel"]
 test = ["websockets"]
 
@@ -6480,110 +6768,106 @@ watchdog = ["watchdog (>=2.3)"]
 
 [[package]]
 name = "wheel"
-version = "0.40.0"
+version = "0.43.0"
 description = "A built-package format for Python"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
-test = ["pytest (>=6.0.0)"]
+test = ["pytest (>=6.0.0)", "setuptools (>=65)"]
 
 [[package]]
 name = "widgetsnbextension"
-version = "4.0.7"
+version = "4.0.11"
 description = "Jupyter interactive widgets for Jupyter Notebook"
 optional = true
 python-versions = ">=3.7"
 files = [
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 ]
 
 [[package]]
 name = "wrapt"
-version = "1.14.1"
+version = "1.16.0"
 description = "Module for decorators, wrappers and monkey patching."
 optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
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+    {file = "yarl-1.9.4-cp38-cp38-win_amd64.whl", hash = "sha256:a00862fb23195b6b8322f7d781b0dc1d82cb3bcac346d1e38689370cc1cc398b"},
+    {file = "yarl-1.9.4-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:604f31d97fa493083ea21bd9b92c419012531c4e17ea6da0f65cacdcf5d0bd27"},
+    {file = "yarl-1.9.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:8a854227cf581330ffa2c4824d96e52ee621dd571078a252c25e3a3b3d94a1b1"},
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+    {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8397a3817d7dcdd14bb266283cd1d6fc7264a48c186b986f32e86d86d35fbac5"},
+    {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e0381b4ce23ff92f8170080c97678040fc5b08da85e9e292292aba67fdac6c34"},
+    {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:23d32a2594cb5d565d358a92e151315d1b2268bc10f4610d098f96b147370136"},
+    {file = "yarl-1.9.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ddb2a5c08a4eaaba605340fdee8fc08e406c56617566d9643ad8bf6852778fc7"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:26a1dc6285e03f3cc9e839a2da83bcbf31dcb0d004c72d0730e755b33466c30e"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:18580f672e44ce1238b82f7fb87d727c4a131f3a9d33a5e0e82b793362bf18b4"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:29e0f83f37610f173eb7e7b5562dd71467993495e568e708d99e9d1944f561ec"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:1f23e4fe1e8794f74b6027d7cf19dc25f8b63af1483d91d595d4a07eca1fb26c"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:db8e58b9d79200c76956cefd14d5c90af54416ff5353c5bfd7cbe58818e26ef0"},
+    {file = "yarl-1.9.4-cp39-cp39-win32.whl", hash = "sha256:c7224cab95645c7ab53791022ae77a4509472613e839dab722a72abe5a684575"},
+    {file = "yarl-1.9.4-cp39-cp39-win_amd64.whl", hash = "sha256:824d6c50492add5da9374875ce72db7a0733b29c2394890aef23d533106e2b15"},
+    {file = "yarl-1.9.4-py3-none-any.whl", hash = "sha256:928cecb0ef9d5a7946eb6ff58417ad2fe9375762382f1bf5c55e61645f2c43ad"},
+    {file = "yarl-1.9.4.tar.gz", hash = "sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf"},
 ]
 
 [package.dependencies]
@@ -6798,56 +7108,60 @@ multidict = ">=4.0"
 
 [[package]]
 name = "yaspin"
-version = "2.3.0"
+version = "3.0.2"
 description = "Yet Another Terminal Spinner"
 optional = true
-python-versions = ">=3.7.2,<4.0.0"
+python-versions = "<4.0,>=3.9"
 files = [
-    {file = "yaspin-2.3.0-py3-none-any.whl", hash = "sha256:17b5548479b3d5b30adec7a87ffcdcddb403d14a2bb86fbcee97f37951e13427"},
-    {file = "yaspin-2.3.0.tar.gz", hash = "sha256:547afd1a9700ac3a29a9f5591c70343bef186ed5dfb5e545a9bb0c77e561a1c9"},
+    {file = "yaspin-3.0.2-py3-none-any.whl", hash = "sha256:5c9b6549b84c8aa7f92426272b670e1302941d72f0275caf32d2ea7db3c269f9"},
+    {file = "yaspin-3.0.2.tar.gz", hash = "sha256:35cae59c682506794a218310445e8326cd8fec410879d1c44953b494b1121e77"},
 ]
 
 [package.dependencies]
-termcolor = ">=2.2,<3.0"
+termcolor = "2.3.0"
 
 [[package]]
 name = "yfinance"
-version = "0.2.18"
+version = "0.2.40"
 description = "Download market data from Yahoo! Finance API"
 optional = false
 python-versions = "*"
 files = [
-    {file = "yfinance-0.2.18-py2.py3-none-any.whl", hash = "sha256:73348d7a8cf7d3048c3a8c150790c5361dbbf58599b78962727ceeb9b012f3cc"},
-    {file = "yfinance-0.2.18.tar.gz", hash = "sha256:3874ee22eb35563a797348d2a5a00b665c9a1c50d21287050db1c5341ecc5c1b"},
+    {file = "yfinance-0.2.40-py2.py3-none-any.whl", hash = "sha256:328176b5690de7aa192456a15b351c20ddde31b35d479f8179f5325bd340fc0b"},
+    {file = "yfinance-0.2.40.tar.gz", hash = "sha256:b053ac31229b5dc7f49a17a057f66aa7f688de2f5ddeb95c2455ec13cd89511a"},
 ]
 
 [package.dependencies]
-appdirs = ">=1.4.4"
 beautifulsoup4 = ">=4.11.1"
-cryptography = ">=3.3.2"
 frozendict = ">=2.3.4"
 html5lib = ">=1.1"
 lxml = ">=4.9.1"
 multitasking = ">=0.0.7"
 numpy = ">=1.16.5"
 pandas = ">=1.3.0"
+peewee = ">=3.16.2"
+platformdirs = ">=2.0.0"
 pytz = ">=2022.5"
-requests = ">=2.26"
+requests = ">=2.31"
+
+[package.extras]
+nospam = ["requests-cache (>=1.0)", "requests-ratelimiter (>=0.3.1)"]
+repair = ["scipy (>=1.6.3)"]
 
 [[package]]
 name = "zipp"
-version = "3.15.0"
+version = "3.19.2"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.15.0-py3-none-any.whl", hash = "sha256:48904fc76a60e542af151aded95726c1a5c34ed43ab4134b597665c86d7ad556"},
-    {file = "zipp-3.15.0.tar.gz", hash = "sha256:112929ad649da941c23de50f356a2b5570c954b65150642bccdd66bf194d224b"},
+    {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
+    {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [extras]
 docs = ["Sphinx", "ipython", "nbsphinx", "nbsphinx-link", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinx-gallery", "tabulate"]
@@ -6858,5 +7172,5 @@ transformers = ["datasets", "transformers"]
 
 [metadata]
 lock-version = "2.0"
-python-versions = ">=3.8,<3.12"
-content-hash = "b34c204be3b52795303212087daf972a93ebf936b80e1ac4d684214e9fe23187"
+python-versions = ">=3.9,<3.12"
+content-hash = "6f8223d984a6fdd7d16f20ad52b0c3c9246294cee75af39c0ea659963e9f2e92"
diff --git a/pyproject.toml b/pyproject.toml
index 05aceeb1..5d22135a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "aws-fortuna"
-version = "0.1.45"
+version = "0.2.0"
 description = "A Library for Uncertainty Quantification."
 authors = ["Gianluca Detommaso <gianluca.detommaso@gmail.com>", "Alberto Gasparin <albgas@amazon.de>"]
 license = "Apache-2.0"
@@ -9,15 +9,16 @@ documentation = "https://aws-fortuna.readthedocs.io/en/latest/"
 packages = [{include = "fortuna"}]
 
 [tool.poetry.dependencies]
-python = ">=3.8,<3.12"
-flax = "^0.6.2"
+python = ">=3.9,<3.12"
+flax = "^0.8.5"
 optax = "^0.1.3"
 matplotlib = "^3.6.2"
 tqdm = "^4.64.1"
 numpy = "^1.23.4"
 tensorflow-cpu = { version = "^2.11.0", markers = "sys_platform != 'darwin'" }
-tensorflow-macos = { version = "^2.13.1", markers = "sys_platform == 'darwin'" }
-Sphinx = { version = "^5.3.0", optional = true }
+tensorflow-macos = { version = "2.13.1", markers = "sys_platform == 'darwin'" }
+array-record = { version = "0.4.1", markers = "sys_platform == 'darwin'" }
+Sphinx = { version = "^7.0.0", optional = true }
 sphinx-autodoc-typehints = { version = "^1.19.5", optional = true }
 nbsphinx = { version = "^0.8.10", optional = true }
 nbsphinx-link = { version = "^1.3.0", optional = true }
@@ -35,6 +36,7 @@ torch = {version = "^2.1.0", optional = true}
 scikit-learn = {version = "^1.3.2", optional = true}
 umap-learn = {version = "^0.5.5", optional = true}
 tabulate = "^0.9.0"
+jax = "~0.4.30"
 
 [tool.poetry.extras]
 docs = ["Sphinx", "sphinx-autodoc-typehints", "pydata-sphinx-theme", "nbsphinx", "nbsphinx-link",
@@ -63,7 +65,7 @@ tabulate = "^0.9.0"
 tensorflow-datasets = "^4.8.3"
 xlrd = "^2.0.1"
 openpyxl = "^3.1.2"
-myst-nb = "^0.17.1"
+myst-nb = "^1.1.0"
 jupytext = "^1.14.5"
 
 [build-system]
@@ -72,14 +74,14 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.black]  # https://black.readthedocs.io/en/stable/usage_and_configuration/the_basics.html#configuration-via-a-file
 line-length = 88
-target-version = ["py38"]
+target-version = ["py39"]
 
 [tool.isort]
 profile = "black"
 combine_as_imports = true
 force_sort_within_sections = true
 force_grid_wrap = 2
-skip = 'docs'
+skip = "docs"
 
 [tool.codespell]
 ignore-words-list = "ACI"
diff --git a/tests/fortuna/prob_model/test_diagnostic.py b/tests/fortuna/prob_model/test_diagnostic.py
index a3f3c112..096465e6 100755
--- a/tests/fortuna/prob_model/test_diagnostic.py
+++ b/tests/fortuna/prob_model/test_diagnostic.py
@@ -59,7 +59,7 @@ def test_ess(self):
         samp1_flat = self.rng.multivariate_normal(self.mu, self.sigma, size=DATA_SIZE)
         ess = effective_sample_size(samp1_flat)
         assert samp1_flat.shape[-1] == ess.shape[0]
-        assert jnp.alltrue(0 <= ess) and jnp.alltrue(ess <= len(samp1_flat))
+        assert jnp.all(0 <= ess) and jnp.all(ess <= len(samp1_flat))
         samp1_tree = self.unflatten(samp1_flat)
         ess_tree = effective_sample_size(samp1_tree)
         vals, _unravel_fn = ravel_pytree(ess_tree)
diff --git a/tests/fortuna/prob_model/test_prob_model.py b/tests/fortuna/prob_model/test_prob_model.py
index a5e8f401..4faea295 100755
--- a/tests/fortuna/prob_model/test_prob_model.py
+++ b/tests/fortuna/prob_model/test_prob_model.py
@@ -105,30 +105,30 @@ def test_rng(self):
             prior=IsotropicGaussianPrior(),
             output_calibrator=ClassificationTemperatureScaler(),
         )
-        assert jnp.alltrue(prob_model.rng._rng == jnp.array([0.0, 0.0]))
-        assert jnp.alltrue(prob_model.rng._rng == prob_model.posterior.rng._rng)
-        assert jnp.alltrue(prob_model.rng._rng == prob_model.likelihood.rng._rng)
-        assert jnp.alltrue(prob_model.rng._rng == prob_model.joint.rng._rng)
-        assert jnp.alltrue(prob_model.rng._rng == prob_model.prior.rng._rng)
-        assert jnp.alltrue(prob_model.rng._rng == prob_model.predictive.rng._rng)
-        assert jnp.alltrue(
+        assert jnp.all(prob_model.rng._rng == jnp.array([0.0, 0.0]))
+        assert jnp.all(prob_model.rng._rng == prob_model.posterior.rng._rng)
+        assert jnp.all(prob_model.rng._rng == prob_model.likelihood.rng._rng)
+        assert jnp.all(prob_model.rng._rng == prob_model.joint.rng._rng)
+        assert jnp.all(prob_model.rng._rng == prob_model.prior.rng._rng)
+        assert jnp.all(prob_model.rng._rng == prob_model.predictive.rng._rng)
+        assert jnp.all(
             prob_model.rng._rng == prob_model.output_calib_manager.rng._rng
         )
         prob_model.rng.get()
-        assert jnp.alltrue(prob_model.rng._rng == prob_model.posterior.rng._rng)
-        assert jnp.alltrue(prob_model.rng._rng == prob_model.likelihood.rng._rng)
-        assert jnp.alltrue(prob_model.rng._rng == prob_model.joint.rng._rng)
-        assert jnp.alltrue(prob_model.rng._rng == prob_model.prior.rng._rng)
-        assert jnp.alltrue(prob_model.rng._rng == prob_model.predictive.rng._rng)
-        assert jnp.alltrue(
+        assert jnp.all(prob_model.rng._rng == prob_model.posterior.rng._rng)
+        assert jnp.all(prob_model.rng._rng == prob_model.likelihood.rng._rng)
+        assert jnp.all(prob_model.rng._rng == prob_model.joint.rng._rng)
+        assert jnp.all(prob_model.rng._rng == prob_model.prior.rng._rng)
+        assert jnp.all(prob_model.rng._rng == prob_model.predictive.rng._rng)
+        assert jnp.all(
             prob_model.rng._rng == prob_model.output_calib_manager.rng._rng
         )
         prob_model.posterior.rng.get()
-        assert jnp.alltrue(prob_model.rng._rng == prob_model.posterior.rng._rng)
-        assert jnp.alltrue(prob_model.rng._rng == prob_model.likelihood.rng._rng)
-        assert jnp.alltrue(prob_model.rng._rng == prob_model.joint.rng._rng)
-        assert jnp.alltrue(prob_model.rng._rng == prob_model.prior.rng._rng)
-        assert jnp.alltrue(prob_model.rng._rng == prob_model.predictive.rng._rng)
-        assert jnp.alltrue(
+        assert jnp.all(prob_model.rng._rng == prob_model.posterior.rng._rng)
+        assert jnp.all(prob_model.rng._rng == prob_model.likelihood.rng._rng)
+        assert jnp.all(prob_model.rng._rng == prob_model.joint.rng._rng)
+        assert jnp.all(prob_model.rng._rng == prob_model.prior.rng._rng)
+        assert jnp.all(prob_model.rng._rng == prob_model.predictive.rng._rng)
+        assert jnp.all(
             prob_model.rng._rng == prob_model.output_calib_manager.rng._rng
         )
diff --git a/tests/fortuna/test_model.py b/tests/fortuna/test_model.py
index 33b8e59c..e9e07059 100755
--- a/tests/fortuna/test_model.py
+++ b/tests/fortuna/test_model.py
@@ -129,8 +129,8 @@ def __init__(self, *args, **kwargs):
     def test_constant(self):
         constant = ConstantModel(output_dim=self.output_dim)
         params = constant.init(self.rng, self.inputs)
-        assert jnp.alltrue(params["params"]["constant"] == jnp.zeros(self.output_dim))
-        assert jnp.alltrue(
+        assert jnp.all(params["params"]["constant"] == jnp.zeros(self.output_dim))
+        assert jnp.all(
             params["params"]["constant"] == constant.apply(params, self.inputs)
         )
 
@@ -138,7 +138,7 @@ def test_constant(self):
             output_dim=self.output_dim, initializer_fun=nn.initializers.ones
         )
         params = constant.init(self.rng, self.inputs)
-        assert jnp.alltrue(params["params"]["constant"] == jnp.ones(self.output_dim))
+        assert jnp.all(params["params"]["constant"] == jnp.ones(self.output_dim))
 
 
 class TestScalarConstant(unittest.TestCase):
@@ -155,8 +155,8 @@ def __init__(self, *args, **kwargs):
     def test_scalar_constant(self):
         scalar = ScalarConstantModel(output_dim=self.output_dim)
         params = scalar.init(self.rng, self.inputs)
-        assert jnp.alltrue(params["params"]["scalar"] == jnp.zeros(self.output_dim))
-        assert jnp.alltrue(
+        assert jnp.all(params["params"]["scalar"] == jnp.zeros(self.output_dim))
+        assert jnp.all(
             params["params"]["scalar"] == scalar.apply(params, self.inputs)
         )
 
@@ -176,8 +176,8 @@ def test_hyper(self):
         value = jnp.array([1.0, 2.0])
         hyper = HyperparameterModel(value=value)
         params = hyper.init(self.rng, self.inputs)
-        assert jnp.alltrue(params["params"]["none"] == jnp.zeros(0))
-        assert jnp.alltrue(hyper.apply(params, self.inputs) == value)
+        assert jnp.all(params["params"]["none"] == jnp.zeros(0))
+        assert jnp.all(hyper.apply(params, self.inputs) == value)
 
 
 class TestScalarHyperparameter(unittest.TestCase):
@@ -195,8 +195,8 @@ def test_scalar_hyper(self):
         value = 3.0
         scalar = ScalarHyperparameterModel(output_dim=2, value=value)
         params = scalar.init(self.rng, self.inputs)
-        assert jnp.alltrue(params["params"]["none"] == jnp.zeros(0))
-        assert jnp.alltrue(
+        assert jnp.all(params["params"]["none"] == jnp.zeros(0))
+        assert jnp.all(
             scalar.apply(params, self.inputs)
             == value * jnp.ones((self.inputs.shape[0], self.output_dim))
         )
diff --git a/tests/fortuna/test_trainer.py b/tests/fortuna/test_trainer.py
index fd243915..15d03d23 100755
--- a/tests/fortuna/test_trainer.py
+++ b/tests/fortuna/test_trainer.py
@@ -13,7 +13,7 @@
 from flax.core import FrozenDict
 import flax.linen as nn
 from jax import numpy as jnp
-from jax._src.prng import PRNGKeyArray
+import jax
 import jax.random
 import numpy as np
 import optax
@@ -72,7 +72,7 @@ def training_loss_step(
         params: Params,
         batch: Batch,
         mutable: Mutable,
-        rng: PRNGKeyArray,
+        rng: jax.Array,
         n_data: int,
         unravel: Optional[Callable[[any], PyTree]] = None,
         calib_params: Optional[CalibParams] = None,
@@ -684,7 +684,7 @@ def loss_fn(params, batch, **kwargs):
             None,
             FrozenDict({"max_grad_norm": max_grad_norm}),
         )
-        loss = float(aux["loss"])
+        loss = float(aux["loss"][0])
 
         # grad acc update
         optimizer = optax.sgd(learning_rate=1e-4)
@@ -693,8 +693,8 @@ def loss_fn(params, batch, **kwargs):
         loss_grad_acc = 0.0
         batches = [
             (
-                inputs[start : start + gradient_accumulation_steps][None],
-                targets[start : start + gradient_accumulation_steps][None],
+                inputs[start: start + gradient_accumulation_steps][None],
+                targets[start: start + gradient_accumulation_steps][None],
             )
             for start in range(0, 9, gradient_accumulation_steps)
         ]
@@ -715,8 +715,8 @@ def loss_fn(params, batch, **kwargs):
                     }
                 ),
             )
-            loss_grad_acc += float(aux_grad_acc["loss"])
-        chex.assert_tree_all_close(new_state.params, new_state_acc.params, atol=1e-7)
+            loss_grad_acc += float(aux_grad_acc["loss"][0])
+        chex.assert_trees_all_close(new_state.params, new_state_acc.params, atol=1e-7)
         self.assertAlmostEqual(loss, loss_grad_acc / (i + 1), places=6)
 
     def test_grad_acc_no_clipping(self):
@@ -775,5 +775,5 @@ def loss_fn(params, batch, **kwargs):
                 ),
             )
             loss_grad_acc += float(aux_grad_acc["loss"])
-        chex.assert_tree_all_close(new_state.params, new_state_acc.params, atol=1e-7)
+        chex.assert_trees_all_close(new_state.params, new_state_acc.params, atol=1e-7)
         self.assertAlmostEqual(loss, loss_grad_acc / 3, places=6)
diff --git a/tests/fortuna/utils/freeze.py b/tests/fortuna/utils/freeze.py
index c6be05f4..e0952928 100644
--- a/tests/fortuna/utils/freeze.py
+++ b/tests/fortuna/utils/freeze.py
@@ -61,7 +61,7 @@ def test_freeze_optimizer(self):
         opt_state = optimizer.init(params)
         updates, opt_state = optimizer.update(grads, opt_state, params)
         updated_params = optax.apply_updates(params, updates)
-        assert jnp.alltrue(params["a"]["b"]["c"] == updated_params["a"]["b"]["c"])
-        assert jnp.alltrue(params["a"]["b"]["d"] != updated_params["a"]["b"]["d"])
-        assert jnp.alltrue(params["a"]["f"]["g"] == updated_params["a"]["f"]["g"])
-        assert jnp.alltrue(params["h"] != updated_params["h"])
+        assert jnp.all(params["a"]["b"]["c"] == updated_params["a"]["b"]["c"])
+        assert jnp.all(params["a"]["b"]["d"] != updated_params["a"]["b"]["d"])
+        assert jnp.all(params["a"]["f"]["g"] == updated_params["a"]["f"]["g"])
+        assert jnp.all(params["h"] != updated_params["h"])