diff --git a/.github/workflows/package.yml b/.github/workflows/package.yml
index 2a60d6a7c..a9ce69ec3 100644
--- a/.github/workflows/package.yml
+++ b/.github/workflows/package.yml
@@ -51,17 +51,3 @@ jobs:
           pip install -e ".[models]"
           pip install pytest
           python3 -m pytest tests/package/extras/models.py
-  extra-report-package-install-check:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v3
-      - name: Install pip
-        run: python3 -m pip install --upgrade pip
-      - uses: actions/setup-python@v4.7.1
-        with:
-          python-version: '3.10'
-      - name: Install package and test import
-        run: |
-          pip install -e ".[report]"
-          pip install pytest
-          python3 -m pytest tests/package/extras/report.py
diff --git a/README.md b/README.md
index 311033aa9..381d9207c 100644
--- a/README.md
+++ b/README.md
@@ -13,20 +13,17 @@
 
 ``cyclops`` is a toolkit for facilitating research and deployment of ML models for healthcare. It provides a few high-level APIs namely:
 
-* `data` - Create datasets for training, inference and evaluation. We use the popular 🤗 [datasets](https://github.com/huggingface/datasets) to efficiently load and slice different modalities of data.
-* `models` - Use common model implementations using [scikit-learn](https://scikit-learn.org/stable/) and [PyTorch](https://pytorch.org/).
-* `tasks` - Use canonical Healthcare ML tasks such as
-    * Mortality prediction
-    * Chest X-ray classification
+* `data` - Create datasets for training, inference and evaluation. We use the popular 🤗 [datasets](https://github.com/huggingface/datasets) to efficiently load and slice different modalities of data
+* `models` - Use common model implementations using [scikit-learn](https://scikit-learn.org/stable/) and [PyTorch](https://pytorch.org/)
+* `tasks` - Use common ML task formulations such as binary classification or multi-label classification on tabular, time-series and image data
 * `evaluate` - Evaluate models on clinical prediction tasks
 * `monitor` - Detect dataset shift relevant for clinical use cases
 * `report` - Create [model report cards](https://vectorinstitute.github.io/cyclops/api/tutorials/nihcxr/nihcxr_report_periodic.html) for clinical ML models
 
-``cyclops`` also provides a library of end-to-end use cases on clinical datasets such as
+``cyclops`` also provides example end-to-end use case implementations on clinical datasets such as
 
-* [MIMIC-III](https://physionet.org/content/mimiciii/1.4/)
+* [NIH chest x-ray](https://www.nih.gov/news-events/news-releases/nih-clinical-center-provides-one-largest-publicly-available-chest-x-ray-datasets-scientific-community)
 * [MIMIC-IV](https://physionet.org/content/mimiciv/2.0/)
-* [eICU-CRD](https://eicu-crd.mit.edu/about/eicu/)
 
 
 ## 🐣 Getting Started
@@ -37,31 +34,15 @@
 python3 -m pip install pycyclops
 ```
 
-The base package installation supports the use of the `data` and `process` APIs to load
-and transform clinical data, for downstream tasks.
+The base cyclops installation doesn't include modelling packages.
 
-To install additional functionality from the other APIs, they can be installed as extras.
+To install additional dependencies for using models,
 
 
-To install with `models`, `tasks`, `evaluate` and `monitor` API support,
-
 ```bash
 python3 -m pip install 'pycyclops[models]'
 ```
 
-To install with `report` API support,
-
-```bash
-python3 -m pip install 'pycyclops[report]'
-```
-
-Multiple extras could also be combined, for example to install with both `report` and
-`models` support:
-
-```bash
-python3 -m pip install 'pycyclops[report,models]'
-```
-
 
 ## 🧑🏿‍💻 Developing
 
diff --git a/cyclops/data/features/medical_image.py b/cyclops/data/features/medical_image.py
index 58942c87f..7b7d9cae9 100644
--- a/cyclops/data/features/medical_image.py
+++ b/cyclops/data/features/medical_image.py
@@ -1,11 +1,10 @@
 """Medical image feature."""
 
-import logging
 import os
 import tempfile
 from dataclasses import dataclass, field
 from io import BytesIO
-from typing import Any, ClassVar, Dict, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Tuple, Union
 
 import numpy as np
 import numpy.typing as npt
@@ -15,18 +14,56 @@
 from datasets.features import Image, features
 from datasets.utils.file_utils import is_local_path
 from datasets.utils.py_utils import string_to_dict
-from monai.data.image_reader import ImageReader
-from monai.data.image_writer import ITKWriter
-from monai.transforms.compose import Compose
-from monai.transforms.io.array import LoadImage
-from monai.transforms.utility.array import ToNumpy
 
-from cyclops.utils.log import setup_logging
+from cyclops.utils.optional import import_optional_module
 
 
-# Logging.
-LOGGER = logging.getLogger(__name__)
-setup_logging(print_level="INFO", logger=LOGGER)
+if TYPE_CHECKING:
+    from monai.data.image_reader import ImageReader
+    from monai.data.image_writer import ITKWriter
+    from monai.transforms.compose import Compose
+    from monai.transforms.io.array import LoadImage
+    from monai.transforms.utility.array import ToNumpy
+else:
+    ImageReader = import_optional_module(
+        "monai.data.image_reader",
+        attribute="ImageReader",
+        error="warn",
+    )
+    ITKWriter = import_optional_module(
+        "monai.data.image_writer",
+        attribute="ITKWriter",
+        error="warn",
+    )
+    Compose = import_optional_module(
+        "monai.transforms.compose",
+        attribute="Compose",
+        error="warn",
+    )
+    LoadImage = import_optional_module(
+        "monai.transforms.io.array",
+        attribute="LoadImage",
+        error="warn",
+    )
+    ToNumpy = import_optional_module(
+        "monai.transforms.utility.array",
+        attribute="ToNumpy",
+        error="warn",
+    )
+_monai_available = all(
+    module is not None
+    for module in (
+        ImageReader,
+        ITKWriter,
+        Compose,
+        LoadImage,
+        ToNumpy,
+    )
+)
+_monai_unavailable_message = (
+    "The MONAI library is required to use the `MedicalImage` feature. "
+    "Please install it with `pip install monai`."
+)
 
 
 @dataclass
@@ -35,24 +72,35 @@ class MedicalImage(Image):  # type: ignore
 
     Parameters
     ----------
-    decode : bool, optional, default=True
-        Whether to decode the image. If False, the image will be returned as a
-        dictionary in the format `{"path": image_path, "bytes": image_bytes}`.
     reader : Union[str, ImageReader], optional, default="ITKReader"
         The MONAI image reader to use.
     suffix : str, optional, default=".jpg"
         The suffix to use when decoding bytes to image.
+    decode : bool, optional, default=True
+        Whether to decode the image. If False, the image will be returned as a
+        dictionary in the format `{"path": image_path, "bytes": image_bytes}`.
+    id : str, optional, default=None
+        The id of the feature.
 
     """
 
     reader: Union[str, ImageReader] = "ITKReader"
     suffix: str = ".jpg"  # used when decoding/encoding bytes to image
-    _loader = Compose(
-        [
-            LoadImage(reader=reader, simple_keys=True, dtype=None, image_only=True),
-            ToNumpy(),
-        ],
-    )
+
+    _loader = None
+    if _monai_available:
+        _loader = Compose(
+            [
+                LoadImage(
+                    reader=reader,
+                    simple_keys=True,
+                    dtype=None,
+                    image_only=False,
+                ),
+                ToNumpy(),
+            ],
+        )
+
     # Automatically constructed
     dtype: ClassVar[str] = "dict"
     pa_type: ClassVar[Any] = pa.struct({"bytes": pa.binary(), "path": pa.string()})
@@ -76,12 +124,14 @@ def encode_example(
 
         """
         if isinstance(value, list):
-            value = np.array(value)
+            value = np.asarray(value)
 
         if isinstance(value, str):
             return {"path": value, "bytes": None}
+
         if isinstance(value, np.ndarray):
             return _encode_ndarray(value, image_format=self.suffix)
+
         if "array" in value and "metadata" in value:
             output_ext_ = self.suffix
             metadata_ = value["metadata"]
@@ -132,7 +182,7 @@ def decode_example(
         if not self.decode:
             raise RuntimeError(
                 "Decoding is disabled for this feature. "
-                "Please use MedicalImage(decode=True) instead.",
+                "Please use `MedicalImage(decode=True)` instead.",
             )
 
         if token_per_repo_id is None:
@@ -147,6 +197,8 @@ def decode_example(
                 )
 
             if is_local_path(path):
+                if self._loader is None:
+                    raise RuntimeError(_monai_unavailable_message)
                 image, metadata = self._loader(path)
             else:
                 source_url = path.split("::")[-1]
@@ -188,6 +240,9 @@ def _read_file_from_bytes(
             Image as numpy array and metadata as dictionary.
 
         """
+        if self._loader is None:
+            raise RuntimeError(_monai_unavailable_message)
+
         # XXX: Can we avoid writing to disk?
         with tempfile.NamedTemporaryFile(mode="wb", suffix=self.suffix) as fp:
             fp.write(buffer.getvalue())
@@ -219,6 +274,9 @@ def _encode_ndarray(
         Dictionary containing the image bytes and path.
 
     """
+    if not _monai_available:
+        raise RuntimeError(_monai_unavailable_message)
+
     if not image_format.startswith("."):
         image_format = "." + image_format
 
@@ -240,5 +298,5 @@ def _encode_ndarray(
         return {"path": None, "bytes": temp_file_bytes}
 
 
-# add the `MedicalImage` feature to the `features` module
+# add the `MedicalImage` feature to the `features` module namespace
 features.MedicalImage = MedicalImage
diff --git a/cyclops/data/packaged_loading_scripts/medical_imagefolder/medical_imagefolder.py b/cyclops/data/packaged_loading_scripts/medical_imagefolder/medical_imagefolder.py
index ab3c4b7d5..62ed9ba4e 100644
--- a/cyclops/data/packaged_loading_scripts/medical_imagefolder/medical_imagefolder.py
+++ b/cyclops/data/packaged_loading_scripts/medical_imagefolder/medical_imagefolder.py
@@ -33,7 +33,7 @@ class MedicalImageFolderConfig(
 class MedicalImageFolder(folder_based_builder.FolderBasedBuilder):  # type: ignore
     """MedicalImageFolder."""
 
-    BASE_FEATURE = MedicalImage()
+    BASE_FEATURE = MedicalImage
     BASE_COLUMN_NAME = "image"
     BUILDER_CONFIG_CLASS = MedicalImageFolderConfig
     EXTENSIONS: List[str]  # definition at the bottom of the script
diff --git a/cyclops/data/transforms.py b/cyclops/data/transforms.py
index 4b902e757..5c2e4216d 100644
--- a/cyclops/data/transforms.py
+++ b/cyclops/data/transforms.py
@@ -1,28 +1,84 @@
 """Transforms for the datasets."""
+from typing import Any, Callable, Tuple
 
-from typing import Tuple
-
-from monai.transforms import Lambdad, Resized, ToDeviced  # type: ignore
-from torchvision.transforms import Compose
-
-
-def txrv_transforms(
-    keys: Tuple[str, ...] = ("features",),
-    device: str = "cpu",
-) -> Compose:
-    """Set of transforms for the models in the TXRV library."""
-    return Compose(
-        [
-            Resized(
-                keys=keys,
-                spatial_size=(1, 224, 224),
-                allow_missing_keys=True,
-            ),
-            Lambdad(
-                keys=keys,
-                func=lambda x: ((2 * (x / 255.0)) - 1.0) * 1024,
-                allow_missing_keys=True,
-            ),
-            ToDeviced(keys=keys, device=device, allow_missing_keys=True),
-        ],
-    )
+from torchvision.transforms import Lambda, Resize
+
+
+# generic dictionary-based wrapper for any transform
+class Dictd:
+    """Generic dictionary-based wrapper for any transform."""
+
+    def __init__(
+        self,
+        transform: Callable[..., Any],
+        keys: Tuple[str, ...],
+        allow_missing_keys: bool = False,
+    ):
+        self.transform = transform
+        self.keys = keys
+        self.allow_missing_keys = allow_missing_keys
+
+    def __call__(self, data: Any) -> Any:
+        """Apply the transform to the data."""
+        for key in self.keys:
+            if self.allow_missing_keys and key not in data:
+                continue
+            data[key] = self.transform(data[key])
+        return data
+
+    def __repr__(self) -> str:
+        """Return a string representation of the transform."""
+        return (
+            f"{self.__class__.__name__}(transform={self.transform}, "
+            f"keys={self.keys}, allow_missing_keys={self.allow_missing_keys})"
+        )
+
+
+# dictionary-based wrapper of Lambda transform using Dictd
+class Lambdad:
+    """Dictionary-based wrapper of Lambda transform using Dictd."""
+
+    def __init__(
+        self,
+        func: Callable[..., Any],
+        keys: Tuple[str, ...],
+        allow_missing_keys: bool = False,
+    ):
+        self.transform = Dictd(
+            transform=Lambda(func),
+            keys=keys,
+            allow_missing_keys=allow_missing_keys,
+        )
+
+    def __call__(self, data: Any) -> Any:
+        """Apply the transform to the data."""
+        return self.transform(data)
+
+    def __repr__(self) -> str:
+        """Return a string representation of the transform."""
+        return f"{self.__class__.__name__}(keys={self.transform.keys}, allow_missing_keys={self.transform.allow_missing_keys})"
+
+
+# dictionary-based wrapper of Resize transform using Dictd
+class Resized:
+    """Dictionary-based wrapper of Resize transform using Dictd."""
+
+    def __init__(
+        self,
+        spatial_size: Tuple[int, int],
+        keys: Tuple[str, ...],
+        allow_missing_keys: bool = False,
+    ):
+        self.transform = Dictd(
+            transform=Resize(size=spatial_size),
+            keys=keys,
+            allow_missing_keys=allow_missing_keys,
+        )
+
+    def __call__(self, data: Any) -> Any:
+        """Apply the transform to the data."""
+        return self.transform(data)
+
+    def __repr__(self) -> str:
+        """Return a string representation of the transform."""
+        return f"{self.__class__.__name__}(keys={self.transform.keys}, allow_missing_keys={self.transform.allow_missing_keys})"
diff --git a/cyclops/evaluate/metrics/experimental/distributed_backends/mpi4py.py b/cyclops/evaluate/metrics/experimental/distributed_backends/mpi4py.py
index 3ae9997d4..a958a0efa 100644
--- a/cyclops/evaluate/metrics/experimental/distributed_backends/mpi4py.py
+++ b/cyclops/evaluate/metrics/experimental/distributed_backends/mpi4py.py
@@ -15,7 +15,7 @@
 if TYPE_CHECKING:
     from mpi4py import MPI
 else:
-    MPI = import_optional_module("mpi4py.MPI", error="ignore")
+    MPI = import_optional_module("mpi4py.MPI", error="warn")
 # mypy: disable-error-code="no-any-return"
 
 
diff --git a/cyclops/evaluate/metrics/experimental/distributed_backends/torch_distributed.py b/cyclops/evaluate/metrics/experimental/distributed_backends/torch_distributed.py
index b4f29eb26..475142dfc 100644
--- a/cyclops/evaluate/metrics/experimental/distributed_backends/torch_distributed.py
+++ b/cyclops/evaluate/metrics/experimental/distributed_backends/torch_distributed.py
@@ -1,5 +1,5 @@
 """`torch.distributed` backend for synchronizing `torch.Tensor` objects."""
-from typing import TYPE_CHECKING, List, TypeVar
+from typing import TYPE_CHECKING, List
 
 from cyclops.evaluate.metrics.experimental.distributed_backends.base import (
     DistributedBackend,
@@ -11,10 +11,8 @@
     import torch
     import torch.distributed as torch_dist
 else:
-    torch = import_optional_module("torch", error="ignore")
-    torch_dist = import_optional_module("torch.distributed", error="ignore")
-
-Tensor = TypeVar("Tensor", bound="torch.Tensor")
+    torch = import_optional_module("torch", error="warn")
+    torch_dist = import_optional_module("torch.distributed", error="warn")
 
 
 class TorchDistributed(DistributedBackend, registry_key="torch_distributed"):
@@ -25,13 +23,13 @@ def __init__(self) -> None:
         super().__init__()
         if torch is None:
             raise ImportError(
-                f"For availability of {self.__class__.__name__},"
-                " please install pytorch first.",
+                f"For availability of `{self.__class__.__name__}`,"
+                " please install `torch` first.",
             )
         if not torch_dist.is_available():
             raise RuntimeError(
-                f"For availability of {self.__class__.__name__},"
-                " make sure torch.distributed is available.",
+                f"For availability of `{self.__class__.__name__}`,"
+                " make sure `torch.distributed` is available.",
             )
 
     @property
diff --git a/cyclops/models/wrappers/pt_model.py b/cyclops/models/wrappers/pt_model.py
index 462eaeb65..78c62e5dd 100644
--- a/cyclops/models/wrappers/pt_model.py
+++ b/cyclops/models/wrappers/pt_model.py
@@ -3,14 +3,23 @@
 import contextlib
 import logging
 import os
-from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Union
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Dict,
+    List,
+    Literal,
+    Optional,
+    Sequence,
+    Union,
+)
 
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
 from datasets import Dataset, DatasetDict
 from datasets.combine import concatenate_datasets
-from monai.data.meta_tensor import MetaTensor
 from torch import nn
 from torch.optim import Optimizer
 from torch.optim.lr_scheduler import _LRScheduler as TorchLRScheduler
@@ -37,6 +46,17 @@
 )
 from cyclops.utils.file import join, process_dir_save_path
 from cyclops.utils.log import setup_logging
+from cyclops.utils.optional import import_optional_module
+
+
+if TYPE_CHECKING:
+    from monai.data.meta_tensor import MetaTensor
+else:
+    MetaTensor = import_optional_module(
+        "monai.data.meta_tensor",
+        attribute="MetaTensor",
+        error="warn",
+    )
 
 
 LOGGER = logging.getLogger(__name__)
@@ -660,7 +680,7 @@ def _get_dataset(
         if isinstance(X, (Dataset, TorchDataset, DatasetDict)):
             return X
 
-        if isinstance(X, MetaTensor):
+        if MetaTensor is not None and isinstance(X, MetaTensor):
             return PTDataset(X.data, y)
 
         if isinstance(X, (np.ndarray, torch.Tensor)):
diff --git a/cyclops/monitor/reductor.py b/cyclops/monitor/reductor.py
index e27aae6c9..ec29af800 100644
--- a/cyclops/monitor/reductor.py
+++ b/cyclops/monitor/reductor.py
@@ -7,7 +7,6 @@
 import torch
 import torchxrayvision as xrv
 from datasets import Dataset, DatasetDict
-from monai.transforms import Compose
 from sklearn.base import BaseEstimator
 from sklearn.decomposition import PCA, KernelPCA
 from sklearn.manifold import Isomap
@@ -15,6 +14,7 @@
 from sklearn.random_projection import SparseRandomProjection
 from torch import nn
 from torch.utils.data import Dataset as TorchDataset
+from torchvision.transforms import Compose
 
 from cyclops.data.utils import apply_transforms
 from cyclops.models.catalog import SKModel, wrap_model
diff --git a/cyclops/monitor/tester.py b/cyclops/monitor/tester.py
index 4e1c30542..51d831e39 100644
--- a/cyclops/monitor/tester.py
+++ b/cyclops/monitor/tester.py
@@ -22,13 +22,13 @@
 )
 from alibi_detect.utils.pytorch.kernels import DeepKernel, GaussianRBF
 from datasets import Dataset, DatasetDict, concatenate_datasets
-from monai.transforms import Lambdad
 from scipy.special import expit as sigmoid
 from scipy.special import softmax
 from sklearn.base import BaseEstimator
 from torch import nn
 from torch.utils.data import Dataset as TorchDataset
 
+from cyclops.data.transforms import Lambdad
 from cyclops.data.utils import apply_transforms
 from cyclops.models.catalog import wrap_model
 from cyclops.monitor.utils import DetectronModule, DummyCriterion, get_args
diff --git a/cyclops/tasks/classification.py b/cyclops/tasks/classification.py
index 673fa39e4..458e12f4e 100644
--- a/cyclops/tasks/classification.py
+++ b/cyclops/tasks/classification.py
@@ -7,9 +7,9 @@
 import numpy as np
 import pandas as pd
 from datasets import Dataset, DatasetDict, config
-from monai.transforms import Compose  # type: ignore
 from sklearn.compose import ColumnTransformer
 from sklearn.exceptions import NotFittedError
+from torchvision.transforms import Compose
 
 from cyclops.data.slicer import SliceSpec
 from cyclops.evaluate.evaluator import evaluate
@@ -419,7 +419,7 @@ def predict(
             splits_mapping = {"test": "test"}
         model_name, model = self.get_model(model_name)
         if transforms:
-            transforms = partial(apply_image_transforms, transforms=transforms)  # type: ignore
+            transforms = partial(apply_image_transforms, transforms=transforms)
         if isinstance(dataset, (Dataset, DatasetDict)):
             return model.predict(
                 dataset,
diff --git a/cyclops/utils/optional.py b/cyclops/utils/optional.py
index a52ffbb7b..88a6d6261 100644
--- a/cyclops/utils/optional.py
+++ b/cyclops/utils/optional.py
@@ -1,28 +1,23 @@
 """Utilities for handling optional dependencies."""
 import importlib
 import importlib.util
-import logging
 import warnings
-from types import ModuleType
-from typing import Literal, Optional
-
-from cyclops.utils.log import setup_logging
-
-
-LOGGER = logging.getLogger(__name__)
-setup_logging(print_level="WARN", logger=LOGGER)
+from typing import Any, Literal, Optional
 
 
 def import_optional_module(
     name: str,
+    attribute: Optional[str] = None,
     error: Literal["raise", "warn", "ignore"] = "raise",
-) -> Optional[ModuleType]:
+) -> Optional[Any]:
     """Import an optional module.
 
     Parameters
     ----------
     name : str
         The name of the module to import.
+    attribute : Optional[str], optional
+        The name of an attribute to import from the module.
     error : ErrorOption, optional
         How to handle errors. One of:
         - "raise": raise an error if the module cannot be imported.
@@ -31,8 +26,9 @@ def import_optional_module(
 
     Returns
     -------
-    Optional[ModuleType]
-        The imported module, if it exists. Otherwise, `None`.
+    Optional[Any]
+        The imported module or attribute from the module, or `None` if the
+        module could not be imported.
 
     """
     if error not in ("raise", "warn", "ignore"):
@@ -42,7 +38,10 @@ def import_optional_module(
         )
 
     try:
-        return importlib.import_module(name)
+        module = importlib.import_module(name)
+        if attribute is not None and module is not None:
+            module = getattr(module, attribute)
+        return module
     except ModuleNotFoundError as exc:
         msg = (
             f"Missing optional dependency '{name}'. "
diff --git a/docs/source/intro.rst b/docs/source/intro.rst
index 36c728c5a..0bdbd94a8 100644
--- a/docs/source/intro.rst
+++ b/docs/source/intro.rst
@@ -13,27 +13,25 @@ models for healthcare. It provides a few high-level APIs namely:
 -  ``data`` - Create datasets for training, inference and evaluation. We
    use the popular 🤗
    `datasets <https://github.com/huggingface/datasets>`__ to efficiently
-   load and slice different modalities of data.
+   load and slice different modalities of data
 -  ``models`` - Use common model implementations using
    `scikit-learn <https://scikit-learn.org/stable/>`__ and
-   `PyTorch <https://pytorch.org/>`__.
--  ``tasks`` - Use canonical Healthcare ML tasks such as
-
-   -  Mortality prediction
-   -  Chest X-ray classification
-
+   `PyTorch <https://pytorch.org/>`__
+-  ``tasks`` - Use common ML task formulations such as binary
+   classification or multi-label classification on tabular, time-series
+   and image data
 -  ``evaluate`` - Evaluate models on clinical prediction tasks
 -  ``monitor`` - Detect dataset shift relevant for clinical use cases
 -  ``report`` - Create `model report
    cards <https://vectorinstitute.github.io/cyclops/api/tutorials/nihcxr/nihcxr_report_periodic.html>`__
    for clinical ML models
 
-``cyclops`` also provides a library of end-to-end use cases on clinical
-datasets such as
+``cyclops`` also provides example end-to-end use case implementations on
+clinical datasets such as
 
--  `MIMIC-III <https://physionet.org/content/mimiciii/1.4/>`__
+-  `NIH chest
+   x-ray <https://www.nih.gov/news-events/news-releases/nih-clinical-center-provides-one-largest-publicly-available-chest-x-ray-datasets-scientific-community>`__
 -  `MIMIC-IV <https://physionet.org/content/mimiciv/2.0/>`__
--  `eICU-CRD <https://eicu-crd.mit.edu/about/eicu/>`__
 
 🐣 Getting Started
 ==================
@@ -45,33 +43,14 @@ Installing cyclops using pip
 
    python3 -m pip install pycyclops
 
-The base package installation supports the use of the ``data`` and
-``process`` APIs to load and transform clinical data, for downstream
-tasks.
-
-To install additional functionality from the other APIs, they can be
-installed as extras.
+The base cyclops installation doesn’t include modelling packages.
 
-To install with ``models``, ``tasks``, ``evaluate`` and ``monitor`` API
-support,
+To install additional dependencies for using models,
 
 .. code:: bash
 
    python3 -m pip install 'pycyclops[models]'
 
-To install with ``report`` API support,
-
-.. code:: bash
-
-   python3 -m pip install 'pycyclops[report]'
-
-Multiple extras could also be combined, for example to install with both
-``report`` and ``models`` support:
-
-.. code:: bash
-
-   python3 -m pip install 'pycyclops[report,models]'
-
 🧑🏿‍💻 Developing
 =======================
 
diff --git a/docs/source/tutorials/nihcxr/cxr_classification.ipynb b/docs/source/tutorials/nihcxr/cxr_classification.ipynb
index 32a96e026..06de21e8a 100644
--- a/docs/source/tutorials/nihcxr/cxr_classification.ipynb
+++ b/docs/source/tutorials/nihcxr/cxr_classification.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Chest X-Ray Disease Classification\n",
     "\n",
-    "This notebook shows chest x-ray classification on the NIH dataset using a pretrained model from the TorchXRayVision library and CyclOps to generate a model card."
+    "This notebook shows chest x-ray classification on the [NIH dataset](https://www.nih.gov/news-events/news-releases/nih-clinical-center-provides-one-largest-publicly-available-chest-x-ray-datasets-scientific-community) using a pretrained model from the TorchXRayVision library and CyclOps to generate a model card."
    ]
   },
   {
@@ -33,7 +33,7 @@
     "\n",
     "import numpy as np\n",
     "import plotly.express as px\n",
-    "from monai.transforms import Compose, Lambdad, Resized\n",
+    "from torchvision.transforms import Compose\n",
     "from torchxrayvision.models import DenseNet\n",
     "\n",
     "from cyclops.data.loader import load_nihcxr\n",
@@ -41,6 +41,7 @@
     "    SliceSpec,\n",
     "    filter_value,  # noqa: E402\n",
     ")\n",
+    "from cyclops.data.transforms import Lambdad, Resized\n",
     "from cyclops.data.utils import apply_transforms\n",
     "from cyclops.evaluate import evaluator\n",
     "from cyclops.evaluate.metrics.factory import create_metric\n",
@@ -143,8 +144,9 @@
     "            allow_missing_keys=True,\n",
     "        ),\n",
     "        Lambdad(\n",
-    "            (\"image\",),\n",
-    "            func=lambda x: np.mean(x, axis=0)[np.newaxis, :] if x.shape[0] != 1 else x,\n",
+    "            keys=(\"image\",),\n",
+    "            func=lambda x: x[0][np.newaxis, :] if x.shape[0] != 1 else x,\n",
+    "            allow_missing_keys=True,\n",
     "        ),\n",
     "    ],\n",
     ")"
diff --git a/docs/source/tutorials/nihcxr/generate_nihcxr_report.py b/docs/source/tutorials/nihcxr/generate_nihcxr_report.py
index 364742352..7ae4d8ef5 100644
--- a/docs/source/tutorials/nihcxr/generate_nihcxr_report.py
+++ b/docs/source/tutorials/nihcxr/generate_nihcxr_report.py
@@ -8,7 +8,7 @@
 import numpy as np
 import numpy.typing as npt
 import plotly.express as px
-from monai.transforms import Compose, Lambdad, Resized  # type: ignore[attr-defined]
+from torchvision.transforms import Compose
 from torchxrayvision.models import DenseNet
 
 from cyclops.data.loader import load_nihcxr
@@ -16,6 +16,7 @@
     SliceSpec,
     filter_value,  # noqa: E402
 )
+from cyclops.data.transforms import Lambdad, Resized
 from cyclops.data.utils import apply_transforms
 from cyclops.evaluate import evaluator
 from cyclops.evaluate.metrics.factory import create_metric
@@ -55,8 +56,8 @@
             allow_missing_keys=True,
         ),
         Lambdad(
-            ("image",),
-            func=lambda x: np.mean(x, axis=0)[np.newaxis, :] if x.shape[0] != 1 else x,
+            keys=("image",),
+            func=lambda x: x[0][np.newaxis, :] if x.shape[0] != 1 else x,
         ),
     ],
 )
diff --git a/docs/source/tutorials/nihcxr/monitor_api.ipynb b/docs/source/tutorials/nihcxr/monitor_api.ipynb
index 929fc939f..a8ab67050 100644
--- a/docs/source/tutorials/nihcxr/monitor_api.ipynb
+++ b/docs/source/tutorials/nihcxr/monitor_api.ipynb
@@ -26,17 +26,25 @@
     "\"\"\"NIHCXR Clinical Drift Experiments Tutorial.\"\"\"\n",
     "\n",
     "\n",
+    "import random\n",
+    "\n",
     "import numpy as np\n",
-    "from monai.transforms import Compose, Lambdad, Resized\n",
+    "import torch\n",
+    "from torchvision.transforms import Compose\n",
     "from torchxrayvision.models import DenseNet\n",
     "\n",
     "from cyclops.data.loader import load_nihcxr\n",
     "from cyclops.data.slicer import SliceSpec\n",
+    "from cyclops.data.transforms import Lambdad, Resized\n",
     "from cyclops.monitor import ClinicalShiftApplicator, Detector, Reductor, TSTester\n",
     "from cyclops.monitor.plotter import plot_drift_experiment, plot_drift_timeseries\n",
     "\n",
     "\n",
-    "nih_ds = load_nihcxr(\"/mnt/data/clinical_datasets/NIHCXR\")[\"test\"]"
+    "nih_ds = load_nihcxr(\"/mnt/data/clinical_datasets/NIHCXR\")[\"test\"]\n",
+    "\n",
+    "random.seed(42)\n",
+    "np.random.seed(42)\n",
+    "torch.manual_seed(42)"
    ]
   },
   {
@@ -65,18 +73,19 @@
     "transforms = Compose(\n",
     "    [\n",
     "        Resized(\n",
-    "            keys=(\"image\",),\n",
     "            spatial_size=(224, 224),\n",
+    "            keys=(\"image\",),\n",
     "            allow_missing_keys=True,\n",
     "        ),\n",
     "        Lambdad(\n",
-    "            keys=(\"image\",),\n",
     "            func=lambda x: ((2 * (x / 255.0)) - 1.0) * 1024,\n",
+    "            keys=(\"image\",),\n",
     "            allow_missing_keys=True,\n",
     "        ),\n",
     "        Lambdad(\n",
-    "            (\"image\",),\n",
-    "            func=lambda x: np.mean(x, axis=0)[np.newaxis, :] if x.shape[0] != 1 else x,\n",
+    "            func=lambda x: x[0][np.newaxis, :] if x.shape[0] != 1 else x,\n",
+    "            keys=(\"image\",),\n",
+    "            allow_missing_keys=True,\n",
     "        ),\n",
     "    ],\n",
     ")"
diff --git a/poetry.lock b/poetry.lock
index 1888780cd..d6e051ef7 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
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+# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
 
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+    {file = "aiohttp-3.9.0.tar.gz", hash = "sha256:09f23292d29135025e19e8ff4f0a68df078fe4ee013bca0105b2e803989de92d"},
 ]
 
 [package.dependencies]
 aiosignal = ">=1.1.2"
-async-timeout = ">=4.0.0a3,<5.0"
+async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
-charset-normalizer = ">=2.0,<4.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
 yarl = ">=1.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns", "cchardet"]
+speedups = ["Brotli", "aiodns", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
@@ -641,13 +629,13 @@ files = [
 
 [[package]]
 name = "certifi"
-version = "2023.7.22"
+version = "2023.11.17"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
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+    {file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"},
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 ]
 
 [[package]]
@@ -897,22 +885,20 @@ files = [
 
 [[package]]
 name = "comm"
-version = "0.1.4"
+version = "0.2.0"
 description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
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+    {file = "comm-0.2.0.tar.gz", hash = "sha256:a517ea2ca28931c7007a7a99c562a0fa5883cfb48963140cf642c41c948498be"},
 ]
 
 [package.dependencies]
 traitlets = ">=4"
 
 [package.extras]
-lint = ["black (>=22.6.0)", "mdformat (>0.7)", "mdformat-gfm (>=0.3.5)", "ruff (>=0.0.156)"]
 test = ["pytest"]
-typing = ["mypy (>=0.990)"]
 
 [[package]]
 name = "confection"
@@ -1154,25 +1140,26 @@ files = [
 
 [[package]]
 name = "datasets"
-version = "2.14.6"
+version = "2.15.0"
 description = "HuggingFace community-driven open-source library of datasets"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
 aiohttp = "*"
 dill = ">=0.3.0,<0.3.8"
 fsspec = {version = ">=2023.1.0,<=2023.10.0", extras = ["http"]}
-huggingface-hub = ">=0.14.0,<1.0.0"
+huggingface-hub = ">=0.18.0"
 multiprocess = "*"
 numpy = ">=1.17"
 packaging = "*"
 pandas = "*"
 pyarrow = ">=8.0.0"
+pyarrow-hotfix = "*"
 pyyaml = ">=5.1"
 requests = ">=2.19.0"
 tqdm = ">=4.62.1"
@@ -1182,15 +1169,15 @@ xxhash = "*"
 apache-beam = ["apache-beam (>=2.26.0,<2.44.0)"]
 audio = ["librosa", "soundfile (>=0.12.1)"]
 benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
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+dev = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0,<2.44.0)", "black (>=23.1,<24.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "pyyaml (>=5.3.1)", "rarfile (>=4.0)", "ruff (>=0.0.241)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy (<2.0.0)", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
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-jax = ["jax (>=0.2.8,!=0.3.2,<=0.3.25)", "jaxlib (>=0.1.65,<=0.3.25)"]
+jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"]
 metrics-tests = ["Werkzeug (>=1.0.1)", "accelerate", "bert-score (>=0.3.6)", "jiwer", "langdetect", "mauve-text", "nltk", "requests-file (>=1.5.1)", "rouge-score", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "spacy (>=3.0.0)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "typer (<0.5.0)"]
 quality = ["black (>=23.1,<24.0)", "pyyaml (>=5.3.1)", "ruff (>=0.0.241)"]
 s3 = ["s3fs"]
 tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos"]
 tensorflow-gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
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+tests = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0,<2.44.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy (<2.0.0)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
 torch = ["torch"]
 vision = ["Pillow (>=6.2.1)"]
 
@@ -1279,26 +1266,15 @@ files = [
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 ]
 
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-name = "entrypoints"
-version = "0.4"
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-optional = false
-python-versions = ">=3.6"
-files = [
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-]
-
 [[package]]
 name = "exceptiongroup"
-version = "1.1.3"
+version = "1.2.0"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
@@ -1320,13 +1296,13 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
 
 [[package]]
 name = "fastjsonschema"
-version = "2.18.1"
+version = "2.19.0"
 description = "Fastest Python implementation of JSON schema"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.extras]
@@ -1450,53 +1426,53 @@ pyflakes = ">=3.1.0,<3.2.0"
 
 [[package]]
 name = "fonttools"
-version = "4.44.0"
+version = "4.45.0"
 description = "Tools to manipulate font files"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -1705,18 +1681,18 @@ test = ["objgraph", "psutil"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.17.3"
+version = "0.19.4"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
 filelock = "*"
-fsspec = "*"
+fsspec = ">=2023.5.0"
 packaging = ">=20.9"
 pyyaml = ">=5.1"
 requests = "*"
@@ -1724,17 +1700,17 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
-all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "black (==23.7)", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (<2.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "urllib3 (<2.0)"]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 cli = ["InquirerPy (==0.3.4)"]
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+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+docs = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "hf-doc-builder", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)", "watchdog"]
 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
-inference = ["aiohttp", "pydantic (<2.0)"]
-quality = ["black (==23.7)", "mypy (==1.5.1)", "ruff (>=0.0.241)"]
+inference = ["aiohttp", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)"]
+quality = ["mypy (==1.5.1)", "ruff (>=0.1.3)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
-testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic (<2.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+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-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
 torch = ["torch"]
-typing = ["pydantic (<2.0)", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
+typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
 [[package]]
 name = "hydra-core"
@@ -1754,13 +1730,13 @@ packaging = "*"
 
 [[package]]
 name = "identify"
-version = "2.5.31"
+version = "2.5.32"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "identify-2.5.31-py2.py3-none-any.whl", hash = "sha256:90199cb9e7bd3c5407a9b7e81b4abec4bb9d249991c79439ec8af740afc6293d"},
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+    {file = "identify-2.5.32-py2.py3-none-any.whl", hash = "sha256:0b7656ef6cba81664b783352c73f8c24b39cf82f926f78f4550eda928e5e0545"},
+    {file = "identify-2.5.32.tar.gz", hash = "sha256:5d9979348ec1a21c768ae07e0a652924538e8bce67313a73cb0f681cf08ba407"},
 ]
 
 [package.extras]
@@ -1779,31 +1755,32 @@ files = [
 
 [[package]]
 name = "imageio"
-version = "2.31.6"
+version = "2.33.0"
 description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "imageio-2.33.0-py3-none-any.whl", hash = "sha256:d580d6576d0ae39c459a444a23f6f61fe72123a3df2264f5fce8c87784a4be2e"},
+    {file = "imageio-2.33.0.tar.gz", hash = "sha256:39999d05eb500089e60be467dd7d618f56e142229b44c3961c2b420eeb538d7e"},
 ]
 
 [package.dependencies]
 numpy = "*"
-pillow = ">=8.3.2,<10.1.0"
+pillow = ">=8.3.2"
 
 [package.extras]
-all-plugins = ["astropy", "av", "imageio-ffmpeg", "psutil", "tifffile"]
-all-plugins-pypy = ["av", "imageio-ffmpeg", "psutil", "tifffile"]
+all-plugins = ["astropy", "av", "imageio-ffmpeg", "pillow-heif", "psutil", "tifffile"]
+all-plugins-pypy = ["av", "imageio-ffmpeg", "pillow-heif", "psutil", "tifffile"]
 build = ["wheel"]
 dev = ["black", "flake8", "fsspec[github]", "pytest", "pytest-cov"]
 docs = ["numpydoc", "pydata-sphinx-theme", "sphinx (<6)"]
 ffmpeg = ["imageio-ffmpeg", "psutil"]
 fits = ["astropy"]
-full = ["astropy", "av", "black", "flake8", "fsspec[github]", "gdal", "imageio-ffmpeg", "itk", "numpydoc", "psutil", "pydata-sphinx-theme", "pytest", "pytest-cov", "sphinx (<6)", "tifffile", "wheel"]
+full = ["astropy", "av", "black", "flake8", "fsspec[github]", "gdal", "imageio-ffmpeg", "itk", "numpydoc", "pillow-heif", "psutil", "pydata-sphinx-theme", "pytest", "pytest-cov", "sphinx (<6)", "tifffile", "wheel"]
 gdal = ["gdal"]
 itk = ["itk"]
 linting = ["black", "flake8"]
+pillow-heif = ["pillow-heif"]
 pyav = ["av"]
 test = ["fsspec[github]", "pytest", "pytest-cov"]
 tifffile = ["tifffile"]
@@ -1840,13 +1817,13 @@ testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs
 
 [[package]]
 name = "importlib-resources"
-version = "6.1.0"
+version = "6.1.1"
 description = "Read resources from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_resources-6.1.0-py3-none-any.whl", hash = "sha256:aa50258bbfa56d4e33fbd8aa3ef48ded10d1735f11532b8df95388cc6bdb7e83"},
-    {file = "importlib_resources-6.1.0.tar.gz", hash = "sha256:9d48dcccc213325e810fd723e7fbb45ccb39f6cf5c31f00cf2b965f5f10f3cb9"},
+    {file = "importlib_resources-6.1.1-py3-none-any.whl", hash = "sha256:e8bf90d8213b486f428c9c39714b920041cb02c184686a3dee24905aaa8105d6"},
+    {file = "importlib_resources-6.1.1.tar.gz", hash = "sha256:3893a00122eafde6894c59914446a512f728a0c1a45f9bb9b63721b6bacf0b4a"},
 ]
 
 [package.dependencies]
@@ -1869,13 +1846,13 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.26.0"
+version = "6.27.0"
 description = "IPython Kernel for Jupyter"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.26.0-py3-none-any.whl", hash = "sha256:3ba3dc97424b87b31bb46586b5167b3161b32d7820b9201a9e698c71e271602c"},
-    {file = "ipykernel-6.26.0.tar.gz", hash = "sha256:553856658eb8430bbe9653ea041a41bff63e9606fc4628873fc92a6cf3abd404"},
+    {file = "ipykernel-6.27.0-py3-none-any.whl", hash = "sha256:4388caa3c2cba0a381e20d289545e88a8aef1fe57a884d4c018718ec8c23c121"},
+    {file = "ipykernel-6.27.0.tar.gz", hash = "sha256:7f4986f606581be73bfb32dc7a1ac9fa0e804c9be50ddf1c7a119413e982693f"},
 ]
 
 [package.dependencies]
@@ -2393,17 +2370,18 @@ optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
 files = [
     {file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"},
+    {file = "jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"},
 ]
 
 [[package]]
 name = "jsonschema"
-version = "4.19.2"
+version = "4.20.0"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jsonschema-4.19.2-py3-none-any.whl", hash = "sha256:eee9e502c788e89cb166d4d37f43084e3b64ab405c795c03d343a4dbc2c810fc"},
-    {file = "jsonschema-4.19.2.tar.gz", hash = "sha256:c9ff4d7447eed9592c23a12ccee508baf0dd0d59650615e847feb6cdca74f392"},
+    {file = "jsonschema-4.20.0-py3-none-any.whl", hash = "sha256:ed6231f0429ecf966f5bc8dfef245998220549cbbcf140f913b7464c52c3b6b3"},
+    {file = "jsonschema-4.20.0.tar.gz", hash = "sha256:4f614fd46d8d61258610998997743ec5492a648b33cf478c1ddc23ed4598a5fa"},
 ]
 
 [package.dependencies]
@@ -2426,17 +2404,17 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-
 
 [[package]]
 name = "jsonschema-specifications"
-version = "2023.7.1"
+version = "2023.11.1"
 description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jsonschema_specifications-2023.7.1-py3-none-any.whl", hash = "sha256:05adf340b659828a004220a9613be00fa3f223f2b82002e273dee62fd50524b1"},
-    {file = "jsonschema_specifications-2023.7.1.tar.gz", hash = "sha256:c91a50404e88a1f6ba40636778e2ee08f6e24c5613fe4c53ac24578a5a7f72bb"},
+    {file = "jsonschema_specifications-2023.11.1-py3-none-any.whl", hash = "sha256:f596778ab612b3fd29f72ea0d990393d0540a5aab18bf0407a46632eab540779"},
+    {file = "jsonschema_specifications-2023.11.1.tar.gz", hash = "sha256:c9b234904ffe02f079bf91b14d79987faa685fd4b39c377a0996954c0090b9ca"},
 ]
 
 [package.dependencies]
-referencing = ">=0.28.0"
+referencing = ">=0.31.0"
 
 [[package]]
 name = "jupyter"
@@ -2460,27 +2438,26 @@ qtconsole = "*"
 
 [[package]]
 name = "jupyter-client"
-version = "7.4.9"
+version = "8.6.0"
 description = "Jupyter protocol implementation and client libraries"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "jupyter_client-7.4.9-py3-none-any.whl", hash = "sha256:214668aaea208195f4c13d28eb272ba79f945fc0cf3f11c7092c20b2ca1980e7"},
-    {file = "jupyter_client-7.4.9.tar.gz", hash = "sha256:52be28e04171f07aed8f20e1616a5a552ab9fee9cbbe6c1896ae170c3880d392"},
+    {file = "jupyter_client-8.6.0-py3-none-any.whl", hash = "sha256:909c474dbe62582ae62b758bca86d6518c85234bdee2d908c778db6d72f39d99"},
+    {file = "jupyter_client-8.6.0.tar.gz", hash = "sha256:0642244bb83b4764ae60d07e010e15f0e2d275ec4e918a8f7b80fbbef3ca60c7"},
 ]
 
 [package.dependencies]
-entrypoints = "*"
-jupyter-core = ">=4.9.2"
-nest-asyncio = ">=1.5.4"
+importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
 python-dateutil = ">=2.8.2"
 pyzmq = ">=23.0"
 tornado = ">=6.2"
-traitlets = "*"
+traitlets = ">=5.3"
 
 [package.extras]
-doc = ["ipykernel", "myst-parser", "sphinx (>=1.3.6)", "sphinx-rtd-theme", "sphinxcontrib-github-alt"]
-test = ["codecov", "coverage", "ipykernel (>=6.12)", "ipython", "mypy", "pre-commit", "pytest", "pytest-asyncio (>=0.18)", "pytest-cov", "pytest-timeout"]
+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"]
 
 [[package]]
 name = "jupyter-console"
@@ -2528,13 +2505,13 @@ test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-events"
-version = "0.8.0"
+version = "0.9.0"
 description = "Jupyter Event System library"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_events-0.8.0-py3-none-any.whl", hash = "sha256:81f07375c7673ff298bfb9302b4a981864ec64edaed75ca0fe6f850b9b045525"},
-    {file = "jupyter_events-0.8.0.tar.gz", hash = "sha256:fda08f0defce5e16930542ce60634ba48e010830d50073c3dfd235759cee77bf"},
+    {file = "jupyter_events-0.9.0-py3-none-any.whl", hash = "sha256:d853b3c10273ff9bc8bb8b30076d65e2c9685579db736873de6c2232dde148bf"},
+    {file = "jupyter_events-0.9.0.tar.gz", hash = "sha256:81ad2e4bc710881ec274d31c6c50669d71bbaa5dd9d01e600b56faa85700d399"},
 ]
 
 [package.dependencies]
@@ -2553,13 +2530,13 @@ test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "p
 
 [[package]]
 name = "jupyter-server"
-version = "2.9.1"
+version = "2.11.0"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_server-2.9.1-py3-none-any.whl", hash = "sha256:21ad1a3d455d5a79ce4bef5201925cd17510c17898cf9d54e3ccfb6b12734948"},
-    {file = "jupyter_server-2.9.1.tar.gz", hash = "sha256:9ba71be4b9c16e479e4c50c929f8ac4b1015baf90237a08681397a98c76c7e5e"},
+    {file = "jupyter_server-2.11.0-py3-none-any.whl", hash = "sha256:c9bd6e6d71dc5a2a25df167dc323422997f14682b008bfecb5d7920a55020ea7"},
+    {file = "jupyter_server-2.11.0.tar.gz", hash = "sha256:78c97ec8049f9062f0151725bc8a1364dfed716646a66819095e0e8a24793eba"},
 ]
 
 [package.dependencies]
@@ -2568,7 +2545,7 @@ argon2-cffi = "*"
 jinja2 = "*"
 jupyter-client = ">=7.4.4"
 jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
-jupyter-events = ">=0.6.0"
+jupyter-events = ">=0.9.0"
 jupyter-server-terminals = "*"
 nbconvert = ">=6.4.4"
 nbformat = ">=5.3.0"
@@ -2704,13 +2681,13 @@ files = [
 
 [[package]]
 name = "jupyterlab-server"
-version = "2.25.0"
+version = "2.25.2"
 description = "A set of server components for JupyterLab and JupyterLab like applications."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab_server-2.25.0-py3-none-any.whl", hash = "sha256:c9f67a98b295c5dee87f41551b0558374e45d449f3edca153dd722140630dcb2"},
-    {file = "jupyterlab_server-2.25.0.tar.gz", hash = "sha256:77c2f1f282d610f95e496e20d5bf1d2a7706826dfb7b18f3378ae2870d272fb7"},
+    {file = "jupyterlab_server-2.25.2-py3-none-any.whl", hash = "sha256:5b1798c9cc6a44f65c757de9f97fc06fc3d42535afbf47d2ace5e964ab447aaf"},
+    {file = "jupyterlab_server-2.25.2.tar.gz", hash = "sha256:bd0ec7a99ebcedc8bcff939ef86e52c378e44c2707e053fcd81d046ce979ee63"},
 ]
 
 [package.dependencies]
@@ -2726,7 +2703,7 @@ requests = ">=2.31"
 [package.extras]
 docs = ["autodoc-traits", "jinja2 (<3.2.0)", "mistune (<4)", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinxcontrib-openapi (>0.8)"]
 openapi = ["openapi-core (>=0.18.0,<0.19.0)", "ruamel-yaml"]
-test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.7.0)", "pytest (>=7.0)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
+test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.8.0)", "pytest (>=7.0)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
 
 [[package]]
 name = "jupyterlab-widgets"
@@ -2972,13 +2949,13 @@ testing = ["black", "isort", "pytest (>=6,!=7.0.0)", "pytest-xdist (>=2)", "twin
 
 [[package]]
 name = "lightning"
-version = "2.1.0"
+version = "2.1.2"
 description = "The Deep Learning framework to train, deploy, and ship AI products Lightning fast."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "lightning-2.1.0-py3-none-any.whl", hash = "sha256:c12bd10bd28b9e29a8e877be039350a585f248c10b76360faa2aa2497f980de6"},
-    {file = "lightning-2.1.0.tar.gz", hash = "sha256:1f78f5995ae7dcffa1edf34320db136902b73a0d1b304404c48ec8be165b3a93"},
+    {file = "lightning-2.1.2-py3-none-any.whl", hash = "sha256:f23358dedd8f5f1151475c9d95f33e4529591c992a99cb9ae89c84bca7289525"},
+    {file = "lightning-2.1.2.tar.gz", hash = "sha256:3b2599a8a719916cb03526e6570356809729680c6cda09391232e2aba0a4ed4b"},
 ]
 
 [package.dependencies]
@@ -2994,62 +2971,61 @@ tqdm = ">=4.57.0,<6.0"
 typing-extensions = ">=4.0.0,<6.0"
 
 [package.extras]
-all = ["Jinja2 (<4.0)", "Pillow (>=9.5.0)", "PyYAML (<7.0)", "aiohttp (>=3.8.0,<4.0)", "arrow (>=1.2.0,<2.0)", "backoff (>=2.2.1,<3.0)", "beautifulsoup4 (>=4.8.0,<5.0)", "click (<9.0)", "croniter (>=1.3.0,<1.5.0)", "dateutils (<1.0)", "deepdiff (>=5.7.0,<7.0)", "deepspeed (>=0.8.2,<=0.9.3)", "docker (>=5.0.0,<7.0)", "fastapi (>=0.92.0,<1.0)", "fsspec (>=2022.5.0,<2024.0)", "fsspec[http] (>2021.06.0,<2024.0)", "gym[classic-control] (>=0.17.0,<1.0)", "hydra-core (>=1.0.5,<2.0)", "inquirer (>=2.10.0,<4.0)", "ipython[all] (<9.0)", "jsonargparse[signatures] (>=4.18.0,<5.0)", "lightning-api-access (>=0.0.3)", "lightning-cloud (==0.5.39)", "lightning-fabric (>=1.9.0)", "lightning-utilities (>=0.8.0,<1.0)", "matplotlib (>3.1,<4.0)", "omegaconf (>=2.0.5,<3.0)", "packaging", "panel (>=1.0.0,<2.0)", "psutil (<6.0)", "pydantic (>=1.7.4)", "python-multipart (>=0.0.5,<1.0)", "pytorch-lightning (>=1.9.0)", "redis (>=4.0.1,<6.0)", "requests (<3.0)", "rich (>=12.3.0,<14.0)", "s3fs (>=2022.5.0,<2024.0)", "starlette", "starsessions (>=1.2.1,<2.0)", "streamlit (>=1.13.0,<2.0)", "tensorboardX (>=2.2,<3.0)", "torch (>0.14.0,<3.0)", "torchdata (>0.5.9,<1.0)", "torchmetrics (>=0.10.0,<2.0)", "torchvision (>=0.13.0,<1.0)", "torchvision (>=0.15.2,<1.0)", "traitlets (>=5.3.0,<6.0)", "typing-extensions (>=4.0.0,<5.0)", "urllib3 (<3.0)", "uvicorn (<1.0)", "websocket-client (<2.0)", "websockets (<12.0)"]
-app = ["Jinja2 (<4.0)", "PyYAML (<7.0)", "arrow (>=1.2.0,<2.0)", "backoff (>=2.2.1,<3.0)", "beautifulsoup4 (>=4.8.0,<5.0)", "click (<9.0)", "croniter (>=1.3.0,<1.5.0)", "dateutils (<1.0)", "deepdiff (>=5.7.0,<7.0)", "fastapi (>=0.92.0,<1.0)", "fsspec (>=2022.5.0,<2024.0)", "inquirer (>=2.10.0,<4.0)", "lightning-cloud (==0.5.39)", "lightning-utilities (>=0.8.0,<1.0)", "packaging", "psutil (<6.0)", "pydantic (>=1.7.4)", "python-multipart (>=0.0.5,<1.0)", "requests (<3.0)", "rich (>=12.3.0,<14.0)", "starlette", "starsessions (>=1.2.1,<2.0)", "traitlets (>=5.3.0,<6.0)", "typing-extensions (>=4.0.0,<5.0)", "urllib3 (<3.0)", "uvicorn (<1.0)", "websocket-client (<2.0)", "websockets (<12.0)"]
-app-all = ["Jinja2 (<4.0)", "PyYAML (<7.0)", "aiohttp (>=3.8.0,<4.0)", "arrow (>=1.2.0,<2.0)", "backoff (>=2.2.1,<3.0)", "beautifulsoup4 (>=4.8.0,<5.0)", "click (<9.0)", "croniter (>=1.3.0,<1.5.0)", "dateutils (<1.0)", "deepdiff (>=5.7.0,<7.0)", "docker (>=5.0.0,<7.0)", "fastapi (>=0.92.0,<1.0)", "fsspec (>=2022.5.0,<2024.0)", "inquirer (>=2.10.0,<4.0)", "lightning-api-access (>=0.0.3)", "lightning-cloud (==0.5.39)", "lightning-fabric (>=1.9.0)", "lightning-utilities (>=0.8.0,<1.0)", "packaging", "panel (>=1.0.0,<2.0)", "psutil (<6.0)", "pydantic (>=1.7.4)", "python-multipart (>=0.0.5,<1.0)", "pytorch-lightning (>=1.9.0)", "redis (>=4.0.1,<6.0)", "requests (<3.0)", "rich (>=12.3.0,<14.0)", "s3fs (>=2022.5.0,<2024.0)", "starlette", "starsessions (>=1.2.1,<2.0)", "streamlit (>=1.13.0,<2.0)", "traitlets (>=5.3.0,<6.0)", "typing-extensions (>=4.0.0,<5.0)", "urllib3 (<3.0)", "uvicorn (<1.0)", "websocket-client (<2.0)", "websockets (<12.0)"]
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 url = "https://github.com/mpi4py/mpi4py"
 reference = "HEAD"
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 [package.dependencies]
@@ -3548,6 +3534,7 @@ typing-extensions = ">=4.1.0"
 [package.extras]
 dmypy = ["psutil (>=4.0)"]
 install-types = ["pip"]
+mypyc = ["setuptools (>=50)"]
 reports = ["lxml"]
 
 [[package]]
@@ -3624,13 +3611,13 @@ test = ["coverage", "nbval", "pytest", "pytest-cov", "pytest-jupyter", "pytest-p
 
 [[package]]
 name = "nbclient"
-version = "0.8.0"
+version = "0.9.0"
 description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
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 python-versions = ">=3.8.0"
 files = [
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 [package.dependencies]
@@ -3646,13 +3633,13 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=
 
 [[package]]
 name = "nbconvert"
-version = "7.10.0"
+version = "7.11.0"
 description = "Converting Jupyter Notebooks"
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 serve = ["tornado (>=6.1)"]
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+test = ["flaky", "ipykernel", "ipywidgets (>=7)", "pytest"]
 webpdf = ["playwright"]
 
 [[package]]
@@ -3810,13 +3797,13 @@ setuptools = "*"
 
 [[package]]
 name = "notebook"
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 [package.dependencies]
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 ipykernel = "*"
 ipython-genutils = "*"
 jinja2 = "*"
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 nbconvert = ">=5"
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 nest-asyncio = ">=1.5"
 prometheus-client = "*"
-pyzmq = ">=17,<25"
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 terminado = ">=0.8.3"
 tornado = ">=6.1"
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 [package.dependencies]
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 [[package]]
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 [package.dependencies]
@@ -4118,7 +4107,7 @@ pytz = ">=2020.1"
 tzdata = ">=2022.1"
 
 [package.extras]
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 aws = ["s3fs (>=2022.05.0)"]
 clipboard = ["PyQt5 (>=5.15.6)", "qtpy (>=2.2.0)"]
 compression = ["zstandard (>=0.17.0)"]
@@ -4138,7 +4127,7 @@ plot = ["matplotlib (>=3.6.1)"]
 postgresql = ["SQLAlchemy (>=1.4.36)", "psycopg2 (>=2.9.3)"]
 spss = ["pyreadstat (>=1.1.5)"]
 sql-other = ["SQLAlchemy (>=1.4.36)"]
-test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)"]
+test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
 xml = ["lxml (>=4.8.0)"]
 
 [[package]]
@@ -4180,13 +4169,13 @@ files = [
 
 [[package]]
 name = "pbr"
-version = "5.11.1"
+version = "6.0.0"
 description = "Python Build Reasonableness"
 optional = false
 python-versions = ">=2.6"
 files = [
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 ]
 
 [[package]]
@@ -4404,13 +4393,13 @@ murmurhash = ">=0.28.0,<1.1.0"
 
 [[package]]
 name = "prometheus-client"
-version = "0.18.0"
+version = "0.19.0"
 description = "Python client for the Prometheus monitoring system."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "prometheus_client-0.19.0.tar.gz", hash = "sha256:4585b0d1223148c27a225b10dbec5ae9bc4c81a99a3fa80774fa6209935324e1"},
 ]
 
 [package.extras]
@@ -4418,13 +4407,13 @@ twisted = ["twisted"]
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.39"
+version = "3.0.41"
 description = "Library for building powerful interactive command lines in Python"
 optional = false
 python-versions = ">=3.7.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -4503,17 +4492,6 @@ files = [
 [package.extras]
 tests = ["pytest"]
 
-[[package]]
-name = "py"
-version = "1.11.0"
-description = "library with cross-python path, ini-parsing, io, code, log facilities"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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-
 [[package]]
 name = "pyarrow"
 version = "11.0.0"
@@ -4551,6 +4529,17 @@ files = [
 [package.dependencies]
 numpy = ">=1.16.6"
 
+[[package]]
+name = "pyarrow-hotfix"
+version = "0.5"
+description = ""
+optional = false
+python-versions = ">=3.5"
+files = [
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+]
+
 [[package]]
 name = "pybtex"
 version = "0.24.0"
@@ -4657,17 +4646,18 @@ files = [
 
 [[package]]
 name = "pygments"
-version = "2.16.1"
+version = "2.17.1"
 description = "Pygments is a syntax highlighting package written in Python."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
 plugins = ["importlib-metadata"]
+windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pylint"
@@ -4793,13 +4783,13 @@ unidecode = ["Unidecode (>=1.1.1)"]
 
 [[package]]
 name = "pytorch-lightning"
-version = "2.1.0"
+version = "2.1.2"
 description = "PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -4814,11 +4804,11 @@ tqdm = ">=4.57.0"
 typing-extensions = ">=4.0.0"
 
 [package.extras]
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@@ -4849,40 +4839,40 @@ tokenize-rt = ">=5.2.0"
 
 [[package]]
 name = "pywavelets"
-version = "1.4.1"
+version = "1.5.0"
 description = "PyWavelets, wavelet transform module"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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 [package.dependencies]
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@@ -6090,13 +6107,13 @@ go = ["sphinxcontrib-golangdomain"]
 
 [[package]]
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 [package.dependencies]
@@ -6312,7 +6329,7 @@ files = [
 ]
 
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 [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 = "text-unidecode"
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+    {file = "yarl-1.9.3-cp38-cp38-win32.whl", hash = "sha256:8a2538806be846ea25e90c28786136932ec385c7ff3bc1148e45125984783dc6"},
+    {file = "yarl-1.9.3-cp38-cp38-win_amd64.whl", hash = "sha256:6465d36381af057d0fab4e0f24ef0e80ba61f03fe43e6eeccbe0056e74aadc70"},
+    {file = "yarl-1.9.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:2f3c8822bc8fb4a347a192dd6a28a25d7f0ea3262e826d7d4ef9cc99cd06d07e"},
+    {file = "yarl-1.9.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:b7831566595fe88ba17ea80e4b61c0eb599f84c85acaa14bf04dd90319a45b90"},
+    {file = "yarl-1.9.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:ff34cb09a332832d1cf38acd0f604c068665192c6107a439a92abfd8acf90fe2"},
+    {file = "yarl-1.9.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fe8080b4f25dfc44a86bedd14bc4f9d469dfc6456e6f3c5d9077e81a5fedfba7"},
+    {file = "yarl-1.9.3-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8535e111a064f3bdd94c0ed443105934d6f005adad68dd13ce50a488a0ad1bf3"},
+    {file = "yarl-1.9.3-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:0d155a092bf0ebf4a9f6f3b7a650dc5d9a5bbb585ef83a52ed36ba46f55cc39d"},
+    {file = "yarl-1.9.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:778df71c8d0c8c9f1b378624b26431ca80041660d7be7c3f724b2c7a6e65d0d6"},
+    {file = "yarl-1.9.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b9f9cafaf031c34d95c1528c16b2fa07b710e6056b3c4e2e34e9317072da5d1a"},
+    {file = "yarl-1.9.3-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:ca6b66f69e30f6e180d52f14d91ac854b8119553b524e0e28d5291a724f0f423"},
+    {file = "yarl-1.9.3-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:e0e7e83f31e23c5d00ff618045ddc5e916f9e613d33c5a5823bc0b0a0feb522f"},
+    {file = "yarl-1.9.3-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:af52725c7c39b0ee655befbbab5b9a1b209e01bb39128dce0db226a10014aacc"},
+    {file = "yarl-1.9.3-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:0ab5baaea8450f4a3e241ef17e3d129b2143e38a685036b075976b9c415ea3eb"},
+    {file = "yarl-1.9.3-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:6d350388ba1129bc867c6af1cd17da2b197dff0d2801036d2d7d83c2d771a682"},
+    {file = "yarl-1.9.3-cp39-cp39-win32.whl", hash = "sha256:e2a16ef5fa2382af83bef4a18c1b3bcb4284c4732906aa69422cf09df9c59f1f"},
+    {file = "yarl-1.9.3-cp39-cp39-win_amd64.whl", hash = "sha256:d92d897cb4b4bf915fbeb5e604c7911021a8456f0964f3b8ebbe7f9188b9eabb"},
+    {file = "yarl-1.9.3-py3-none-any.whl", hash = "sha256:271d63396460b6607b588555ea27a1a02b717ca2e3f2cf53bdde4013d7790929"},
+    {file = "yarl-1.9.3.tar.gz", hash = "sha256:4a14907b597ec55740f63e52d7fee0e9ee09d5b9d57a4f399a7423268e457b57"},
 ]
 
 [package.dependencies]
@@ -7586,10 +7619,9 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.link
 testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
 
 [extras]
-models = ["alibi", "alibi-detect", "array-api-compat", "hydra-core", "llvmlite", "scikit-learn", "torch", "torchxrayvision", "xgboost"]
-report = ["kaleido", "pillow", "plotly", "pybtex", "pydantic", "scour", "spdx-tools"]
+models = ["alibi", "alibi-detect", "array-api-compat", "hydra-core", "llvmlite", "monai", "scikit-learn", "torch", "torchxrayvision", "xgboost"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9, <3.11"
-content-hash = "922e7f891d06f8f640c0a89e8d5eabd49cba3cd2bb49cb6fac4043358db1ef95"
+content-hash = "f43def1a1ef6c057c5443a157d59c0159f2c1c3b3b26d27ac0150f2da90f744f"
diff --git a/pyproject.toml b/pyproject.toml
index 0304dccce..04368e92f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,8 +18,16 @@ numpy = "^1.24.4"
 datasets = "^2.10.1"
 psutil = "^5.9.4"
 pyarrow = "^11.0.0"
-monai = { version = "^1.1.0", extras = ["itk"] }
+pydantic = "^1.10.11"
+Jinja2 = "^3.1.2"
+spdx-tools = "^0.8.1"
+pybtex = "^0.24.0"
+kaleido = "0.2.1"
+scour = "^0.38.2"
+plotly = "^5.7.0"
+pillow = "^9.5.0"
 
+monai = { version = "^1.1.0", extras = ["itk"], optional = true }
 hydra-core = { version = "^1.2.0", optional = true }
 scikit-learn = { version = "^1.2.2", optional = true }
 torch = { version = "^1.11.0", optional = true }
@@ -28,13 +36,6 @@ xgboost = { version = "^1.5.2", optional = true }
 alibi = { version = "^0.9.4", optional = true, extras = ["shap"] }
 alibi-detect = { version = "^0.11.0", optional = true, extras = ["torch"] }
 llvmlite = { version = "^0.40.0", optional = true }
-pydantic = { version = "^1.10.11", optional = true }
-spdx-tools = { version = "^0.8.1", optional = true }
-pybtex = { version = "^0.24.0", optional = true }
-kaleido = { version = "0.2.1", optional = true }
-scour = { version = "^0.38.2", optional = true }
-plotly = { version = "^5.7.0", optional = true }
-pillow = { version = "^9.5.0", optional = true }
 array-api-compat = {version = "^1.4", optional = true}
 
 [tool.poetry.group.models.dependencies]
@@ -47,15 +48,7 @@ alibi = { version = "^0.9.4", extras = ["shap"] }
 alibi-detect = { version = "^0.11.0", extras = ["torch"] }
 llvmlite = "^0.40.0"
 array-api-compat = {version = "^1.4"}
-
-[tool.poetry.group.report.dependencies]
-pydantic = "^1.10.11"
-spdx-tools = "^0.8.1"
-pybtex = "^0.24.0"
-kaleido = "0.2.1"
-scour = "^0.38.2"
-plotly = "^5.7.0"
-pillow = "^9.5.0"
+monai = { version = "^1.1.0", extras = ["itk"] }
 
 [tool.poetry.group.test]
 optional = true
@@ -107,8 +100,18 @@ mpi4py = {git = "https://github.com/mpi4py/mpi4py"}
 lightning = "^2.1.0"
 
 [tool.poetry.extras]
-models = ["array-api-compat", "hydra-core", "scikit-learn", "torch", "torchxrayvision", "xgboost", "alibi", "alibi-detect", "llvmlite"]
-report = ["pydantic", "spdx-tools", "pybtex", "kaleido", "scour", "plotly", "pillow"]
+models = [
+    "array-api-compat",
+    "hydra-core",
+    "scikit-learn",
+    "monai",
+    "torch",
+    "torchxrayvision",
+    "xgboost",
+    "alibi",
+    "alibi-detect",
+    "llvmlite",
+]
 
 [tool.mypy]
 ignore_missing_imports = true
diff --git a/tests/cyclops/evaluate/metrics/conftest.py b/tests/cyclops/evaluate/metrics/conftest.py
index f3241828e..1fc41753c 100644
--- a/tests/cyclops/evaluate/metrics/conftest.py
+++ b/tests/cyclops/evaluate/metrics/conftest.py
@@ -13,7 +13,7 @@
 from cyclops.utils.optional import import_optional_module
 
 
-MPI_pool = import_optional_module("mpi4py.util.pool", error="ignore")
+MPIPool = import_optional_module("mpi4py.util.pool", attribute="Pool", error="warn")
 
 with contextlib.suppress(RuntimeError):
     set_start_method("spawn")
@@ -61,8 +61,8 @@ def pytest_configure():
     )
     pytest.torch_pool = torch_pool  # type: ignore
 
-    if MPI_pool is not None:
-        mpi_pool = MPI_pool.Pool(processes=NUM_PROCESSES, path=sys.path)
+    if MPIPool is not None:
+        mpi_pool = MPIPool(processes=NUM_PROCESSES, path=sys.path)
         pytest.mpi_pool = mpi_pool  # type: ignore
 
 
@@ -71,6 +71,6 @@ def pytest_sessionfinish():
     pytest.torch_pool.close()  # type: ignore
     pytest.torch_pool.join()  # type: ignore
 
-    if MPI_pool is not None:
+    if MPIPool is not None:
         pytest.mpi_pool.close()  # type: ignore
         pytest.mpi_pool.join()  # type: ignore
diff --git a/tests/cyclops/evaluate/metrics/experimental/distributed_backends/test_mpi4py.py b/tests/cyclops/evaluate/metrics/experimental/distributed_backends/test_mpi4py.py
index d2909676d..672c4f5e8 100644
--- a/tests/cyclops/evaluate/metrics/experimental/distributed_backends/test_mpi4py.py
+++ b/tests/cyclops/evaluate/metrics/experimental/distributed_backends/test_mpi4py.py
@@ -15,10 +15,10 @@
 from ..testers import DummyListStateMetric, DummyMetric
 
 
-MPI = import_optional_module("mpi4py.MPI", error="ignore")
+MPI = import_optional_module("mpi4py.MPI", error="warn")
 
 
-def _test_mpi4py_class_init(rank: int, worldsize: int = 2):
+def _test_mpi4py_class_init(rank: int, worldsize: int = NUM_PROCESSES):
     """Run test."""
     if MPI is None:
         with pytest.raises(
@@ -38,10 +38,10 @@ def _test_mpi4py_class_init(rank: int, worldsize: int = 2):
 @pytest.mark.integration_test()
 def test_mpi4py_backend_class_init():
     """Test `TorchDistributed` class."""
-    pytest.mpi_pool.starmap(_test_mpi4py_class_init, [(rank, 2) for rank in range(2)])  # type: ignore
+    pytest.mpi_pool.starmap(_test_mpi4py_class_init, [(rank, NUM_PROCESSES) for rank in range(NUM_PROCESSES)])  # type: ignore
 
 
-def _test_all_gather_simple(rank: int, worldsize: int = 2):
+def _test_all_gather_simple(rank: int, worldsize: int = NUM_PROCESSES):
     """Run test."""
     backend = MPI4Py()
 
@@ -53,7 +53,7 @@ def _test_all_gather_simple(rank: int, worldsize: int = 2):
         assert anp.all(val == anp.ones_like(val))
 
 
-def _test_all_gather_uneven_arrays(rank: int, worldsize: int = 2):
+def _test_all_gather_uneven_arrays(rank: int, worldsize: int = NUM_PROCESSES):
     """Run test."""
     backend = MPI4Py()
 
@@ -65,7 +65,7 @@ def _test_all_gather_uneven_arrays(rank: int, worldsize: int = 2):
         assert anp.all(val == anp.ones_like(val))
 
 
-def _test_all_gather_uneven_multidim_arrays(rank: int, worldsize: int = 2):
+def _test_all_gather_uneven_multidim_arrays(rank: int, worldsize: int = NUM_PROCESSES):
     """Run test."""
     backend = MPI4Py()
 
@@ -89,7 +89,7 @@ def _test_all_gather_uneven_multidim_arrays(rank: int, worldsize: int = 2):
 )
 def test_mpi4py_all_gather(case_fn):
     """Test `all_gather` method."""
-    pytest.mpi_pool.starmap(case_fn, [(rank, 2) for rank in range(NUM_PROCESSES)])  # type: ignore
+    pytest.mpi_pool.starmap(case_fn, [(rank, NUM_PROCESSES) for rank in range(NUM_PROCESSES)])  # type: ignore
 
 
 def _test_dist_sum(rank: int, worldsize: int = NUM_PROCESSES) -> None:
diff --git a/tests/cyclops/evaluate/metrics/experimental/distributed_backends/test_torch_distributed.py b/tests/cyclops/evaluate/metrics/experimental/distributed_backends/test_torch_distributed.py
index 17077a205..aa57b6dfe 100644
--- a/tests/cyclops/evaluate/metrics/experimental/distributed_backends/test_torch_distributed.py
+++ b/tests/cyclops/evaluate/metrics/experimental/distributed_backends/test_torch_distributed.py
@@ -13,16 +13,16 @@
 from ..testers import DummyListStateMetric, DummyMetric
 
 
-torch = import_optional_module("torch", error="ignore")
+torch = import_optional_module("torch", error="warn")
 
 
 def _test_torch_distributed_class(rank: int, worldsize: int = NUM_PROCESSES):
     """Run test."""
-    torch_dist = import_optional_module("torch.distributed", error="ignore")
+    torch_dist = import_optional_module("torch.distributed", error="warn")
     if torch is None:
         with pytest.raises(
             ImportError,
-            match="For availability of TorchDistributed please install .*",
+            match="For availability of `TorchDistributed` please install .*",
         ):
             backend = TorchDistributed()
 
@@ -32,7 +32,7 @@ def _test_torch_distributed_class(rank: int, worldsize: int = NUM_PROCESSES):
 
     # skip if torch distributed is not available
     if torch_dist is None:
-        pytest.skip("torch.distributed is not available")
+        pytest.skip("`torch.distributed` is not available")
 
     backend = TorchDistributed()
 
diff --git a/tests/cyclops/evaluate/metrics/experimental/utils/test_ops.py b/tests/cyclops/evaluate/metrics/experimental/utils/test_ops.py
index cf2d2c0a7..3704674af 100644
--- a/tests/cyclops/evaluate/metrics/experimental/utils/test_ops.py
+++ b/tests/cyclops/evaluate/metrics/experimental/utils/test_ops.py
@@ -26,7 +26,7 @@
 from cyclops.utils.optional import import_optional_module
 
 
-cp = import_optional_module("cupy", error="ignore")
+cp = import_optional_module("cupy", error="warn")
 
 
 def multiply_by_two(x):
diff --git a/tests/cyclops/utils/test_optional.py b/tests/cyclops/utils/test_optional.py
index 2ccb3854d..28077531c 100644
--- a/tests/cyclops/utils/test_optional.py
+++ b/tests/cyclops/utils/test_optional.py
@@ -1,37 +1,73 @@
 """Test optional import utilities."""
+from math import sqrt
 
 import pytest
 
 from cyclops.utils.optional import import_optional_module
 
 
-class TestImportOptionalModule:
-    """Test importing optional modules."""
+def test_import_valid_module():
+    """Test importing a valid module."""
+    module = import_optional_module("math")
+    assert module is not None
+    import math
 
-    def test_import_valid_module(self):
-        """Test importing a valid module."""
-        module = import_optional_module("math")
-        assert module is not None
-        import math
+    assert module == math
 
-        assert module == math
 
-    def test_import_nonexistent_module_ignore(self):
-        """Test importing a non-existent module with `error='ignore'`."""
-        module = import_optional_module("nonexistent_module", error="ignore")
-        assert module is None
+def test_import_valid_module_attribute():
+    """Test importing a valid module attribute."""
+    module = import_optional_module("math", attribute="sqrt")
+    assert module is not None
+    assert module == sqrt
 
-    def test_import_nonexistent_module_warn(self):
-        """Test importing a non-existent module with `error='warn'`."""
-        with pytest.warns(ImportWarning):
-            import_optional_module("nonexistent_module", error="warn")
 
-    def test_import_nonexistent_module_raise(self):
-        """Test importing a non-existent module with `error='raise'`."""
-        with pytest.raises(ModuleNotFoundError):
-            import_optional_module("nonexistent_module", error="raise")
+def test_import_nonexistent_module_ignore():
+    """Test importing a non-existent module with `error='ignore'`."""
+    module = import_optional_module("nonexistent_module", error="ignore")
+    assert module is None
 
-    def test_invalid_error_option(self):
-        """Test importing a valid module with an invalid error option."""
-        with pytest.raises(ValueError):
-            import_optional_module("math", error="invalid_option")  # type: ignore
+    attr = import_optional_module(
+        "nonexistent_module",
+        attribute="nonexistent_attribute",
+        error="ignore",
+    )
+    assert attr is None
+
+
+def test_import_nonexistent_module_warn():
+    """Test importing a non-existent module with `error='warn'`."""
+    with pytest.warns(ImportWarning):
+        import_optional_module("nonexistent_module", error="warn")
+
+    with pytest.warns(ImportWarning):
+        import_optional_module(
+            "nonexistent_module",
+            attribute="nonexistent_attribute",
+            error="warn",
+        )
+
+
+def test_import_nonexistent_module_raise():
+    """Test importing a non-existent module with `error='raise'`."""
+    with pytest.raises(ModuleNotFoundError):
+        import_optional_module("nonexistent_module", error="raise")
+
+    with pytest.raises(ModuleNotFoundError):
+        import_optional_module(
+            "nonexistent_module",
+            attribute="nonexistent_attribute",
+            error="raise",
+        )
+
+
+def test_invalid_error_option():
+    """Test importing a valid module with an invalid error option."""
+    with pytest.raises(ValueError):
+        import_optional_module("math", error="invalid_option")  # type: ignore
+
+
+def test_import_nonexistent_attribute():
+    """Test importing a non-existent attribute."""
+    with pytest.raises(AttributeError):
+        import_optional_module("math", attribute="nonexistent_attribute")
diff --git a/tests/package/base.py b/tests/package/base.py
index dd9b940e5..42f56a569 100644
--- a/tests/package/base.py
+++ b/tests/package/base.py
@@ -1,5 +1,6 @@
 """Test import of subpackages with base cyclops install."""
 
+import numpy as np
 import pytest
 
 
@@ -19,5 +20,27 @@ def test_import_cyclops():
         import cyclops.models
     with pytest.raises(ImportError):
         import cyclops.monitor
-    with pytest.raises(ImportError):
-        import cyclops.report
+
+
+def test_medical_image_feature_without_monai():
+    """Test that the MedicalImage feature raises an error without MONAI installed."""
+    from cyclops.data.features.medical_image import MedicalImage
+
+    feat = MedicalImage()
+    # create a dummy image
+    img = np.random.rand(10, 10, 10)
+
+    # test encode_example
+    with pytest.raises(
+        RuntimeError,
+        match="The MONAI library is required to use the `MedicalImage` feature.*",
+    ):
+        feat.encode_example(img)
+
+    # test decode_example
+    dummy_val = {"path": "/dummy/local/path", "bytes": None}
+    with pytest.raises(
+        RuntimeError,
+        match="The MONAI library is required to use the `MedicalImage` feature.*",
+    ):
+        feat.decode_example(dummy_val)
diff --git a/tests/package/extras/models.py b/tests/package/extras/models.py
index 338594044..7d70f6048 100644
--- a/tests/package/extras/models.py
+++ b/tests/package/extras/models.py
@@ -1,7 +1,5 @@
 """Test import of subpackages with base cyclops + models extra install."""
 
-import pytest
-
 
 def test_import_cyclops():
     """Test import of cyclops."""
@@ -24,6 +22,3 @@ def test_import_cyclops():
     from cyclops import monitor
 
     assert monitor.__name__ == "cyclops.monitor"
-
-    with pytest.raises(ImportError):
-        import cyclops.report
diff --git a/tests/package/extras/report.py b/tests/package/extras/report.py
deleted file mode 100644
index 217970034..000000000
--- a/tests/package/extras/report.py
+++ /dev/null
@@ -1,25 +0,0 @@
-"""Test import of subpackages with base cyclops + report extra install."""
-
-import pytest
-
-
-def test_import_cyclops():
-    """Test import of cyclops."""
-    import cyclops
-
-    assert cyclops.__name__ == "cyclops"
-
-    from cyclops import data
-
-    assert data.__name__ == "cyclops.data"
-
-    from cyclops import report
-
-    assert report.__name__ == "cyclops.report"
-
-    with pytest.raises(ImportError):
-        import cyclops.evaluate
-    with pytest.raises(ImportError):
-        import cyclops.models
-    with pytest.raises(ImportError):
-        import cyclops.monitor