diff --git a/cebra_lens/__init__.py b/cebra_lens/__init__.py
index a198adb..e9dd10c 100644
--- a/cebra_lens/__init__.py
+++ b/cebra_lens/__init__.py
@@ -9,6 +9,7 @@
 from .utils_allen import *
 from .utils_hpc import *
 from .utils_plot import *
+from .utils_wrapper import *
 
 # selects what files can be imported when doing from CEBRA_Lens import * --> keep env clean
 # __all__ = ['get_layer_activations']
diff --git a/cebra_lens/activations.py b/cebra_lens/activations.py
index 0ad97d4..de95cef 100644
--- a/cebra_lens/activations.py
+++ b/cebra_lens/activations.py
@@ -9,7 +9,8 @@
 import torch
 import torch.nn as nn
 
-from .utils_plot import plot_activations
+from cebra_lens import utils_wrapper
+
 
 
 def _cut_array(array: npt.NDArray,
@@ -29,16 +30,16 @@ def _cut_array(array: npt.NDArray,
             The sliced array. If both start and end indices are 0, the whole array is returned.
     """
 
-    start = cut_indices[0]
-    end = cut_indices[1]
+    start, end = cut_indices
     if start == 0 and end == 0:
         # If both start and end are 0, take the whole array
-        sliced_array = array
-    else:
-        # Otherwise, slice the array
-        sliced_array = array[:, start:end if end != 0 else start:]
-    return sliced_array
+        return array
+    
+    end_idx = None if end == 0 else end
 
+    # Construct a slicing tuple like [:, :, ..., start:end_idx] to slice along the last axis
+    slicers = [slice(None)] * (array.ndim - 1) + [slice(start, end_idx)]
+    return array[tuple(slicers)]
 
 def get_cut_indices(
     model_: cebra.integrations.sklearn.cebra.CEBRA,
@@ -90,8 +91,10 @@ def get_cut_indices(
 def get_activations_model(
     model: cebra.integrations.sklearn.cebra.CEBRA,
     data: torch.Tensor,
-    session_id: int = -1,
-    name: str = "single",
+    labels: Optional[torch.Tensor] = None,
+    session_id: int = None,
+    pad_before_transform: bool = True,
+    activations_keys_prefix: str = "model",
     instance: int = 0,
     layer_type: Type[nn.Module] = nn.Conv1d,
 ) -> Dict[str, npt.NDArray]:
@@ -124,45 +127,59 @@ def get_activations_model(
 
     activations = {}
     transform_kwargs = {}
-    if model.solver_name_ in [
-            "multi-session",
-            "multi-session-aux",
-            "multiobjective-solver",
-    ]:
-
-        model_ = model.model_[session_id]
-        transform_kwargs.update({"session_id": session_id})
-
-    elif model.solver_name_ in [
-            "single-session",
-            "single-session-aux",
-            "single-session-hybrid",
-            "single-session-full",
-    ]:
-        model_ = model.model_
 
+    if isinstance(model, cebra.integrations.sklearn.cebra.CEBRA):
+        model_ = model.solver_._get_model(session_id=session_id)
+    elif isinstance(model, cebra.solver.Solver):
+        model_ = model._get_model(session_id=session_id)
     else:
-        raise NotImplementedError(
-            f"Solver {model.solver_name_} is not yet implemented.")
+        raise ValueError(
+            "Model must be an instance of cebra.integrations.sklearn.cebra.CEBRA "
+            f"or cebra.solver.Solver, got {type(model)} instead.", )
+
+    transform_kwargs.update({"session_id": session_id})
 
     activations, handles, conv_layer_info = _attach_hooks(
         activations=activations,
         model=model_,
-        name=name,
+        activations_keys_prefix=activations_keys_prefix,
         instance=instance,
         layer_type=layer_type,
     )
-    _ = model.transform(data, **transform_kwargs)
+
+    _ = utils_wrapper.transform(model=model,
+                                data=data,
+                                label=labels,
+                                **transform_kwargs)
 
     # remove all handles to avoid activation's problems
     for handle in handles:
         handle.remove()
-
-    if model.pad_before_transform:
-        # Padding logic: calculate the total reduction which happens based on the kernel size per layer, divide the reduction per layer into 2 parts
+    
+    if hasattr(model, "pad_before_transform"):
+        pad_before_transform = model.pad_before_transform    
+        
+    if pad_before_transform:
         cut_indices = get_cut_indices(model_, layer_type, conv_layer_info)
-        for i, (key, value) in enumerate(activations.items()):
-            activations[key] = _cut_array(value, cut_indices[i])
+    else:
+        cut_indices = [(0,0)] * len(handles)
+    # for any activation that was captures in time chunks:
+    # remove the padding from each chunk using cut indices,
+    # then, concatenate them along the time axis
+    for i, (key, batch_list) in enumerate(list(activations.items())):
+        if not isinstance(batch_list, list):
+            continue
+        sliced_chunks = [
+            _cut_array(chunk, cut_indices[i])
+            for chunk in batch_list
+        ]
+        # now every chunk.shape == (1, channels, time)
+        axis = sliced_chunks[0].ndim - 1
+        activations[key] = np.concatenate(sliced_chunks, axis=axis)
+    # squeeze (1, channels, time) to (channels, time), so downstream tools (e.g., k‑NN regression) receive the 2D array they require
+    for key, arr in list(activations.items()):
+        if arr.ndim == 3 and arr.shape[0] == 1:
+            activations[key] = arr[0]
 
     return activations
 
@@ -170,7 +187,9 @@ def get_activations_model(
 def process_activations(
     models: Dict[str, List[cebra.integrations.sklearn.cebra.CEBRA]],
     data: torch.Tensor,
-    session_id: int,
+    labels: Optional[torch.Tensor] = None,
+    session_id: int = None,
+    pad_before_transform: bool = True,
     activations: Dict[str, npt.NDArray] = {},
     layer_type: Type[nn.Module] = None,
 ) -> Dict[str, npt.NDArray]:
@@ -202,8 +221,10 @@ def process_activations(
                 get_activations_model(
                     model=model,
                     data=data,
+                    labels=labels,
                     session_id=session_id,
-                    name=model_name,
+                    pad_before_transform=pad_before_transform,
+                    activations_keys_prefix=model_name,
                     instance=i,
                     layer_type=layer_type,
                 ))
@@ -212,18 +233,37 @@ def process_activations(
 
 
 # Function to create a hook that stores the activations in the dictionary
-def _get_activation(name: str, activations: Dict):
-
+def _get_activation(activations_keys_prefix: str, activations: Dict):
+    """Creates a forward hook to capture activations from a model layer.
+    
+    This function returns a hook that captures the output of a model layer during the forward pass and stores it in a dictionary.
+    
+    Args:
+        activations_keys_prefix : str
+            The prefix to use for the activation key, corresponding to the type of model (eg. "single", "multi").
+        activations : Dict
+            A dictionary to store the activations. The key will be the name of the layer, and the value will be the activations.
+    
+    Returns:
+        hook : function
+            A forward hook function that captures the activations.
+        activations : Dict
+            The dictionary where the activations will be stored. The key is the name of the layer, and the value is the activations.
+    """
+    activations.setdefault(activations_keys_prefix, [])    
     def hook(model, input, output):
-        activations[name] = output.detach().squeeze().numpy()
+        arr = output.detach().cpu().numpy() 
+        activations[activations_keys_prefix].append(arr)
 
     return hook, activations
 
 
+#NOTE(celia): this function is not super flexible to handle different layer types,
+# but it is a good starting point.
 def _attach_hooks(
     activations: Dict[str, npt.NDArray],
     model: cebra.integrations.sklearn.cebra.CEBRA,
-    name: str,
+    activations_keys_prefix: str,
     instance: int,
     layer_type: Type[nn.Module] = None,
 ) -> Dict[str, npt.NDArray]:  # only attaches hooks on convolutional layers
@@ -237,8 +277,10 @@ def _attach_hooks(
             A dictionary to store the activations. Please refer to ``activations`` returned by ``get_activations_model``.
         model : cebra.integrations.sklearn.cebra.CEBRA
             The model to which hooks will be attached.
-        name : str
-            A base name for the activation keys (e.g., "single", "multi").
+        activations_keys_prefix : str
+            A base name for the activation keys (e.g., "single", "multi") so that the keys are 
+            unique for each model instance. The keys will be in the format 
+            '{activations_keys_prefix}_{instance}_layer_{num_layer}'.
         instance : int
             The instance number for the model, used to differentiate between models from the same model category.
         layer_type : Type[nn.Module]
@@ -258,8 +300,10 @@ def _attach_hooks(
             # attach hook to the layer_type and to the output layer
             if isinstance(model.net[i], layer_type) or i == len(model.net) - 1:
                 hook, activations = _get_activation(
-                    f"{name}_{instance}_layer_{num_layer}", activations)
-                if isinstance(model.net[i], layer_type):
+                    f"{activations_keys_prefix}_{instance}_layer_{num_layer}",
+                    activations)
+                if isinstance(model.net[i],
+                              layer_type) and layer_type == nn.Conv1d:
                     conv_layer_info.append(model.net[i].kernel_size[0])
                 handle = model.net[i].register_forward_hook(hook)
                 handles.append(handle)
@@ -269,7 +313,7 @@ def _attach_hooks(
                 for submodule in model.net[i].modules():
                     if isinstance(submodule, layer_type):
                         hook, activations = _get_activation(
-                            f"{name}_{instance}_layer_{num_layer}",
+                            f"{activations_keys_prefix}_{instance}_layer_{num_layer}",
                             activations,
                         )
                         conv_layer_info.append(submodule.kernel_size[0])
@@ -284,7 +328,7 @@ def _attach_hooks(
             if bool(model.net[i]._modules):
                 for submodule in model.net[i].modules():
                     hook, activations = _get_activation(
-                        f"{name}_{instance}_layer_{num_layer}",
+                        f"{activations_keys_prefix}_{instance}_layer_{num_layer}",
                         activations,
                     )
                     handle = submodule.register_forward_hook(hook)
@@ -293,7 +337,8 @@ def _attach_hooks(
 
             else:
                 hook, activations = _get_activation(
-                    f"{name}_{instance}_layer_{num_layer}", activations)
+                    f"{activations_keys_prefix}_{instance}_layer_{num_layer}",
+                    activations)
 
                 handle = model.net[i].register_forward_hook(hook)
                 handles.append(handle)
@@ -345,9 +390,11 @@ def aggregate_activations(
 def get_activations(
     models: Dict[str, List[cebra.integrations.sklearn.cebra.CEBRA]],
     data: torch.Tensor,
-    session_id: int,
+    labels: Optional[torch.Tensor] = None,
+    session_id: int = None,
+    pad_before_transform: bool = True,
     activations: Optional[Dict[str, npt.NDArray]] = None,
-    layer_type: Optional[Type[nn.Module]] = None,
+    layer_type: Optional[Type[nn.Module]] = nn.Conv1d,
 ) -> Dict[str, npt.NDArray]:
     """Extract and organize activations from models.
 
@@ -370,7 +417,15 @@ def get_activations(
     activations = activations or {}
 
     aggregated_activations = aggregate_activations(
-        process_activations(models, data, session_id, activations, layer_type))
+        process_activations(
+            models=models,
+            data=data,
+            labels=labels,
+            session_id=session_id,
+            pad_before_transform=pad_before_transform,
+            activations=activations,
+            layer_type=layer_type,
+        ))
 
     activations_dict = {}
     for key, value in aggregated_activations.items():
diff --git a/cebra_lens/quantification/base.py b/cebra_lens/quantification/base.py
index c267e0b..0ad047f 100644
--- a/cebra_lens/quantification/base.py
+++ b/cebra_lens/quantification/base.py
@@ -31,6 +31,7 @@ def iterate_over_layers(
         self,
         activations: List[Union[float, npt.NDArray]],
         metric_func: types.FunctionType,
+        **kwargs,
     ) -> List[Union[np.float64, npt.NDArray]]:
         """Iterate over each layer of activations and apply the metric function to compute the desired metric.
 
@@ -46,7 +47,7 @@ def iterate_over_layers(
         """
         layer_data = []
         for layer_activation in activations:
-            layer_data.append(metric_func(layer_activation))
+            layer_data.append(metric_func(layer_activation, **kwargs))
         return layer_data
 
     def save(self, filepath: str, data: Dict[str, npt.NDArray]) -> None:
@@ -88,3 +89,7 @@ def plot(self):
         The plot function is specific to a metric, e.g. intra-bin distance, RDM, CKA,...
         """
         raise NotImplementedError
+
+    def output_information(self):
+        """Output information about the metric class."""
+        print(f"Metric class: {self.__class__.__name__}")
\ No newline at end of file
diff --git a/cebra_lens/quantification/decoder.py b/cebra_lens/quantification/decoder.py
index 3172474..af4000d 100644
--- a/cebra_lens/quantification/decoder.py
+++ b/cebra_lens/quantification/decoder.py
@@ -1,94 +1,138 @@
 from typing import Dict, Optional, Tuple, Type
+from typing import NamedTuple, List, Any
+from typing import Union
 
 import cebra
 import matplotlib
 import numpy as np
 import numpy.typing as npt
 import sklearn.metrics
+from sklearn.model_selection import GridSearchCV
+from sklearn.metrics import make_scorer, accuracy_score, mean_absolute_error, r2_score
+from sklearn.utils.multiclass import type_of_target
 import torch
 import torch as pt
 import torch.nn as nn
 
-from cebra_lens import utils_plot
+from cebra_lens import utils_plot, utils_wrapper
 
 from ..activations import get_activations_model
 from ..utils_allen import decoding_frames
 from ..utils_hpc import decoding_pos_dir
 from .base import _BaseMetric
 
+#NOTE(eloise): resampling, subsampling and supervised model architecture is still not supported
+#              checked via '''supported_model_architectures()''' function.
 
+class DecodeResult(NamedTuple):
+    overall_score: float
+    per_label_error: List[float]
+    per_label_score: List[float]
+    label_types: List[str] # for each label either regression or classification 
+    
 def decoding(
     embedding_train: pt.tensor,
     embedding_test: pt.tensor,
-    label_train: npt.NDArray,
-    label_test: npt.NDArray,
-) -> Tuple[np.float64, list, list]:
-    """Function to decode the embeddings using KNNDecoder from CEBRA. 
+    train_label: npt.NDArray,
+    test_label: npt.NDArray,
+) -> DecodeResult:
+    """ Decode embeddings via per-label KNNDecoder hyperparameter search.  
+      
+    For each label, determines whether it's a regression (continuous) or classification (discrete)
+    task, then runs a grid search over KNN neighbors using R² for regression targets, accuracy for classification targets.
     
-    The decoding scores are returned in the form of average $R^2$ score 
-    across all labels, $R^2$ scores per label and error per label.
-
     Args:
         embedding_train : pt.tensor
             The part of the output embedding to use as training for the decoding.
         embedding_test : pt.tensor
             The part of the output embedding to use as testing for the decoding.
-        label_train : npt.NDArray
+        train_label : npt.NDArray
             The true labels corresponding to the training data.
-        label_test : npt.NDArray
+        test_label : npt.NDArray
             The true labels corresponding to the validation data.
 
     Returns:
-        Tuple[np.float64, list, list]
-            A tuple containing the overall test score (R^2), a list of median errors for each label, and a list of R^2 scores for each label.
+        DecodeResult(
+            overall_score: float     # R² (if all regression), accuracy (if all classification), else None
+            per_label_error: List[float]  # MAE (regression) or 1–accuracy (classification)
+            per_label_score: List[float]  # R² or accuracy per label
+            label_types: str             # "regression" or "classification" per label 
+        )
+
     """
-    try:
-        num_labels = label_train.shape[1]
-    except:
+    if train_label.shape[1] > 1:
+        num_labels = train_label.shape[1]
+    else:
         num_labels = 1
-        label_train = label_train.reshape(-1, 1)
-        label_test = label_test.reshape(-1, 1)
-
-    # resampling, subsampling and supervised model architecture is still not supported
-    # checked via '''supported_model_architectures()''' function
-
+        train_label = train_label.reshape(-1, 1)
+        test_label = test_label.reshape(-1, 1)
+    # type_of_target check if each label is continuous (float) or discrete (int) and may return:
+    #  - "continuous"             → 1D float array (single‐output regression)
+    #  - "continuous‑multioutput" → 2D float array (multi‑output regression)
+    #  - "binary"/"multiclass"    → 1D int array (classification)
+    # Since we immediately split into 1D columns, we only ever see "continuous" vs classification types 
+    
+    raw_types  = [type_of_target(train_label[:, i]) for i in range(num_labels)]
+    label_types = [
+        "regression"     if t == "continuous" else
+        "classification" # covers "binary"/"multiclass"/etc.
+        for t in raw_types
+    ]
+    is_reg      = [mode == "regression" for mode in label_types]
+    
     # for each label find another K
-    predictions, labels_test_err, labels_test_score = [], [], []
+    param_grid = {"n_neighbors": np.arange(1, 11) ** 2}
+    
+    predictions, test_labels_err, test_labels_score = [], [], []
     for i in range(num_labels):
-        params = np.power(np.linspace(1, 10, 10, dtype=int), 2)
-        errs = []
-        for n in params:
-            train_decoder = cebra.KNNDecoder(n_neighbors=n, metric="cosine")
-            train_valid_idx = int(len(embedding_train) / 9 * 8)
-            train_decoder.fit(embedding_train[:train_valid_idx],
-                              label_train[:train_valid_idx, i])
-            pred = train_decoder.predict(embedding_train[train_valid_idx:])
-            err = label_train[train_valid_idx:, i] - pred
-            errs.append(abs(err).sum())
-
-        test_decoder = cebra.KNNDecoder(n_neighbors=params[np.argmin(errs)],
-                                        metric="cosine")
-
-        test_decoder.fit(embedding_train, label_train[:, i])
-        label_pred = test_decoder.predict(embedding_test)
-
-        predictions.append(label_pred)
-        label_test_err = np.median(abs(label_pred - label_test[:, i]))
-        labels_test_err.append(label_test_err)
-        label_test_score = sklearn.metrics.r2_score(label_test[:, i],
-                                                    label_pred)
-        labels_test_score.append(label_test_score)
-
-    # transform it into an appropriate shape
-    predictions = np.stack(np.array(predictions), axis=1)
-    # difference between classification error and regression error -> here we are only taking into account regression style labels
-
-    test_score = sklearn.metrics.r2_score(label_test, predictions)
+        train_label_i = train_label[:, i]
+        test_label_i  = test_label[:, i]
+
+        if not is_reg[i]:
+            # Cast discrete (binary/multiclass) labels to int64 so KNNDecoder runs classification
+            train_label_i = train_label_i.astype(np.int64)
+            test_label_i  = test_label_i.astype(np.int64)
+
+        # Choose scorer based on regression vs. classification
+        scorer = make_scorer(r2_score) if is_reg[i] else make_scorer(accuracy_score)
+
+        gs = GridSearchCV(
+            cebra.KNNDecoder(metric="cosine"),
+            param_grid=param_grid,
+            scoring=scorer,
+            cv=2,
+        )
 
-    # always plot the test_score in R2 for overall labels, if wanted you can choose a label and plot its error, but I need to add a parameter
+        gs.fit(embedding_train, train_label_i)
+        pred_label = gs.best_estimator_.predict(embedding_test)  
 
-    return test_score, labels_test_err, labels_test_score
+        predictions.append(pred_label)
+        if is_reg[i]:
+            test_labels_err.append(mean_absolute_error(test_label_i, pred_label))
+            test_labels_score.append(r2_score(test_label_i, pred_label))
+        else:
+            # Class labels are categorical, so “prediction – true_label” has no meaningful numeric distance.
+            # Instead, use misclassification rate (1 – accuracy) as the error for classification tasks.
+            acc = accuracy_score(test_label_i, pred_label)
+            test_labels_err.append(1.0 - acc)
+            test_labels_score.append(acc)  
 
+    predictions = np.stack(np.array(predictions), axis=1)
+    # only compute a global score if all labels use the same mode;
+    # if regression + classification labels are mixed, overall score will be None
+    if all(is_reg):
+        test_score = r2_score(test_label, predictions)
+    elif not any(is_reg):
+        test_score = accuracy_score(test_label.ravel(), predictions.ravel())
+    else:
+        test_score = None
+
+    return DecodeResult(
+        overall_score=test_score,
+        per_label_error=test_labels_err,
+        per_label_score=test_labels_score,
+        label_types=label_types,
+    )
 
 class Decoding(_BaseMetric):
     """
@@ -122,7 +166,6 @@ def __init__(
         session_id: int = 0,
         dataset_label: str = None,
         layer_type: Optional[Type[nn.Module]] = nn.Conv1d,
-        output_only: bool = True,
     ):
 
         self.train_label = train_label
@@ -132,7 +175,6 @@ def __init__(
         self.session_id = session_id
         self.dataset_label = dataset_label
         self.layer_type = layer_type
-        self.output_only = output_only
 
     def output_information(self):
         print(
@@ -140,27 +182,28 @@ def output_information(self):
         print(f"Session ID: {self.session_id}")
         print(f"Dataset label: {self.dataset_label}")
         print(f"Layer type: {self.layer_type}")
-        print(f"Output only: {self.output_only}")
-        if self.output_only:
-            print(
-                "The decoding analysis will only compute the decoding scores for the output embeddings of the model and plot them."
-            )
-        else:
-            print(
-                "The decoding analysis will compute the decoding scores for the activations of the model and plot them across layers."
-            )
+        # print(f"Output only: {self.output_only}")
+        # if self.output_only:
+        #    print(
+        #        "The decoding analysis will only compute the decoding scores for the output embeddings of the model and plot them."
+        #    )
+        # else:
+        #     print(
+        #         "The decoding analysis will compute the decoding scores for the activations of the model and plot them across layers."
+        #     )
         print(
-            "If you want to change the parameters, please re-initialize the class with the new parameters or if you want to change the output_only parameter, call the compute_metric function with the output_only parameter set to True or False."
-        )
+            "If you want to change the parameters, please re-initialize the class with the new parameters ",
+            "or if you want to change the output_only parameter, call the compute_metric function with the ",
+            "output_only parameter set to True or False.")
 
     def _decode(
         self,
         embedding_train: npt.NDArray,
-        label_train: npt.NDArray,
+        train_label: npt.NDArray,
         embedding_test: npt.NDArray,
-        label_test: npt.NDArray,
+        test_label: npt.NDArray,
         dataset_label: str = None,
-    ) -> npt.NDArray:
+    ) -> Union[DecodeResult, np.ndarray]:
         """Decode a model by choosing the appropriate function base on the dataset.
         
         Note: 
@@ -191,76 +234,71 @@ def _decode(
             embedding_test = embedding_test.T
 
         if dataset_label == "visual":
-
             results = decoding_frames(
                 embedding_train=embedding_train,
-                label_train=label_train,
+                train_label=train_label,
                 embedding_test=embedding_test,
-                label_test=label_test,
+                test_label=test_label,
             )
         elif dataset_label == "HPC":
             results = decoding_pos_dir(
                 embedding_train=embedding_train,
-                label_train=label_train,
                 embedding_test=embedding_test,
-                label_test=label_test,
+                train_label=train_label,
+                test_label=test_label,
             )
         else:
-            results = decoding(
+            results: DecodeResult = decoding(
                 embedding_train=embedding_train,
-                label_train=label_train,
                 embedding_test=embedding_test,
-                label_test=label_test,
+                train_label=train_label,
+                test_label=test_label,
             )
         return results
 
     def compute(
         self,
         model: cebra.integrations.sklearn.cebra.CEBRA,
-    ) -> npt.NDArray:
+        output_only: bool = True,
+    ) -> Dict[int, DecodeResult]:
         """Decode neural data by layer using a given CEBRA model.
 
         Args:
-            model : cebra.integrations.sklearn.cebra.CEBRA
+            model : cebra.intfegrations.sklearn.cebra.CEBRA
                 The CEBRA model that will be used to transform the data (either multi-session or single-session model for now).
+            output_only: bool
+                A bool which defines whether to calculation decoding scores for the activations layers of a model, or for the
+                embeddings of the model. Default: True.
 
         Returns:
-            npt.NDArray
-                A numpy array containing the decoding results for each layer and the neural input baseline.
+            Dict[int, DecodeResult]
+                Mapping from layer index (0=input/baseline, 1…N=layers) to its decoding result.
         """
         transform_kwargs = {}
-        if self.output_only:
-
+        if output_only:
             num_layers = 0
+            transform_kwargs.update({"session_id": self.session_id})
 
-            if model.solver_name_ not in [
-                    "single-session",
-                    "single-session-aux",
-                    "single-session-hybrid",
-                    "single-session-full",
-                    "multi-session",
-                    "multi-session-aux",
-                    "multiobjective-solver",
-            ]:
-                raise NotImplementedError(
-                    f"Solver {model.solver_name_} is not yet implemented.")
-            elif model.solver_name_ in [
-                    "multi-session",
-                    "multi-session-aux",
-                    "multiobjective-solver",
-            ]:
-                transform_kwargs.update({"session_id": self.session_id})
-
-            train_embedding = model.transform(self.train_data,
-                                              **transform_kwargs)
-            test_embedding = model.transform(self.test_data,
-                                             **transform_kwargs)
-        else:
+            train_embedding = utils_wrapper.transform(
+                model=model,
+                data=self.train_data,
+                label=self.train_label,
+                **transform_kwargs,
+            )
 
+            test_embedding = utils_wrapper.transform(
+                model=model,
+                data=self.test_data,
+                label=self.test_label,
+                **transform_kwargs,
+            )
+        else:
             activations_train = get_activations_model(
                 model=model,
                 data=self.train_data,
-                name=model.solver_name_,
+                labels=self.train_label,
+                activations_keys_prefix=model.solver_.__class__.__name__
+                if hasattr(model, 'solver_') else model.__class__.__name__,
                 session_id=self.session_id,
                 layer_type=self.layer_type,
             )
@@ -268,45 +306,66 @@ def compute(
             activations_test = get_activations_model(
                 model=model,
                 data=self.test_data,
-                name=model.solver_name_,
+                labels=self.test_label,
+                activations_keys_prefix=model.solver_.__class__.__name__
+                if hasattr(model, 'solver_') else model.__class__.__name__,
                 session_id=self.session_id,
                 layer_type=self.layer_type,
             )
             num_layers = len(activations_train)
             keys = list(activations_train.keys())
 
+        # Get the decoding labels
+        if isinstance(model, cebra.solver.UnifiedSolver):
+            train_decoding_labels = self.train_label[self.session_id]
+            test_decoding_labels = self.test_label[self.session_id]
+        else:
+            train_decoding_labels = self.train_label
+            test_decoding_labels = self.test_label
+            
         results = {}
         for i in range(num_layers + 1):
 
-            # if output_only == True, then it will only do this loop and for train_data it will take in the embeddings
-            if i == 0:
-                if not self.output_only:
-                    train_embedding = self.train_data
-                    test_embedding = self.test_data
-
+            #NOTE(eloise): if output_only is True, then it will only do this iteration
+            # of the for loop and for train_data it will take in the embeddings.
+            #NOTE(celia): for now we skip the first layer if the model is a UnifiedSolver
+            if output_only:
                 results.update({
                     i:
                     self._decode(
                         train_embedding,
-                        self.train_label,
+                        train_decoding_labels,
                         test_embedding,
-                        self.test_label,
+                        test_decoding_labels,
                         self.dataset_label,
                     )
                 })
 
             else:
-
-                results.update({
-                    i:
-                    self._decode(
-                        activations_train[keys[i - 1]],
-                        self.train_label,
-                        activations_test[keys[i - 1]],
-                        self.test_label,
-                        self.dataset_label,
-                    )
-                })
+                if i == 0:  
+                    if isinstance(model,cebra.solver.UnifiedSolver):
+                        continue
+                    results.update({
+                        i:
+                        self._decode(
+                            self.train_data,
+                            train_decoding_labels,
+                            self.test_data,
+                            test_decoding_labels,
+                            self.dataset_label,
+                        )
+                    })
+                else:
+                    results.update({
+                        i:
+                        self._decode(
+                            activations_train[keys[i - 1]],
+                            train_decoding_labels,
+                            activations_test[keys[i - 1]],
+                            test_decoding_labels,
+                            self.dataset_label,
+                        )
+                    })
 
         return results
 
@@ -314,20 +373,21 @@ def compute(
     def __name__(self):
         return "decode_by_layer"
 
-    def set_output_only(self, output_only: bool) -> None:
-        """Set the output_only parameter to True or False. 
-        
-        If True, it will compute the decoding scores for the output embeddings of the model, otherwise it will compute the decoding scores for the activations of the model.
+    # TODO(celia): check that doesn't break anything to remove it
+    # def set_output_only(self, output_only: bool) -> None:
+    #     """
+    #     Set the output_only parameter to True or False. If True, it will compute the decoding scores for the output embeddings of the model, otherwise it will compute the decoding scores for the activations of the model.
 
-        Args:
-            output_only : bool
-                If True, it will compute the decoding scores for the output embeddings of the model, otherwise it will compute the decoding scores for the activations of the model.
-        """
-        self.output_only = output_only
+    #     Parameters:
+    #     ----------
+    #     output_only : bool
+    #         If True, it will compute the decoding scores for the output embeddings of the model, otherwise it will compute the decoding scores for the activations of the model.
+    #     """
+    #     self.output_only = output_only
 
     def plot(
         self,
-        results_dict: Dict[str, Dict[int, Tuple[np.float64, list, list]]],
+        results_dict: Dict[str, Dict[int, DecodeResult]],
         title: str = "Decoding by layer",
         label: int = None,
         figsize: tuple = (15, 5),
@@ -361,14 +421,36 @@ def plot(
         if self.dataset_label is None:
             if label is None:
                 raise ValueError(
-                    "If dataset_label is not specified, label must be provided to plot the decoding scores for specified label."
+                    "If dataset_label is not specified, label must be provided to plot ",
+                    "the decoding scores for specified label.",
                 )
-
-        if self.output_only:
-            return utils_plot.plot_decoding(results_dict, palette,
-                                            self.dataset_label, label,
-                                            plot_error, ax)
+                
+        num_models = len(results_dict)
+        
+        # pick the first model's results and count its layers
+        #first_model_results = next(iter(results_dict.values())).tolist()
+        first_model_results = next(iter(results_dict.values()))
+        first_run: Dict[int, DecodeResult] = first_model_results[0]
+        num_layers = len(first_run)
+        
+        if num_models == 1 and num_layers == 1:
+            # single-model & single-layer
+            return utils_plot.plot_decoding(
+                results_dict,
+                palette,
+                self.dataset_label,
+                label,
+                plot_error,
+                ax,
+            )    
+           
         else:
-            return utils_plot.plot_layer_decoding(results_dict, title,
-                                                  self.dataset_label, label,
-                                                  plot_error, figsize)
+            # multiple layers or multiple models
+            return utils_plot.plot_layer_decoding(
+                results_dict,
+                title,
+                self.dataset_label,
+                label,
+                plot_error=plot_error,
+                figsize=figsize,
+            )
\ No newline at end of file
diff --git a/cebra_lens/quantification/distance.py b/cebra_lens/quantification/distance.py
index a2a7731..9c77cfb 100644
--- a/cebra_lens/quantification/distance.py
+++ b/cebra_lens/quantification/distance.py
@@ -7,7 +7,6 @@
 from scipy.spatial.distance import cdist, pdist
 from sklearn.preprocessing import StandardScaler
 
-from ..utils import extract_label
 from ..utils_plot import *
 from .base import _BaseMetric
 from .misc import continuous_binning, discrete_binning, repetition_binning
@@ -320,8 +319,6 @@ class Distance(_BaseMetric):
             The data array of shape (num_samples, num_features).
         label : torch.Tensor
             The array of labels corresponding to the data.
-        label_ind : int, optional
-            The index of the label to extract from the array (default is 0). This is relevant when dataset_label is None.
         is_discrete_labels : bool, optional
             Specifies whether the given label is discrete or continuous. This is relevant when dataset_label is None.
         dataset_label : str, optional
@@ -336,7 +333,6 @@ def __init__(
         self,
         data,
         label,
-        label_ind: int = 0,
         is_discrete_labels: bool = False,
         dataset_label: str = None,
         metric: str = "cosine",
@@ -348,15 +344,13 @@ def __init__(
         self.label = label
         self.dataset_label = dataset_label
         if self.dataset_label is None:
-            if label_ind is None:
-                raise ValueError(
-                    "If dataset_label is None, label_ind must be provided to indicate which label will be used for the distance calculation."
-                )
             if is_discrete_labels is None:
                 raise ValueError(
                     "If dataset_label is None, is_discrete_labels must be specified to indicate whether the label is is_discrete_labels or continuous."
                 )
-            self.label = extract_label(label, label_ind)
+            if len(label.shape) > 1:
+                raise ValueError("The label must be a 1D array.")
+            self.label = label
         self.metric = metric
         self.distance_label = distance_label
 
@@ -369,7 +363,7 @@ def _define_indices(
     ) -> Tuple[npt.NDArray, Optional[npt.NDArray]]:
         """
         Defines the indices for each bin.
-        
+
         Depending on if the labels are continuous or is_discrete_labels, the labels are calculated accordingly using `continuous_binning()` or `is_discrete_labels_binning()`.
 
         Args:
diff --git a/cebra_lens/quantification/rdm_metric.py b/cebra_lens/quantification/rdm_metric.py
index ad6d2bd..8897a50 100644
--- a/cebra_lens/quantification/rdm_metric.py
+++ b/cebra_lens/quantification/rdm_metric.py
@@ -29,10 +29,6 @@ class RDM(_BaseMetric):
             The dataset type, either 'visual' or 'HPC'. Default is 'visual'.
         metric : str, optional
             The distance metric to use for computing the RDMs. 'correlation' or 'euclidean' are supported.
-        bool_oracle : bool, optional
-            Whether to compute and compare with the Oracle RDM. Default is True.
-        label_ind : int, optional
-            The index of the label to use for the RDM calculation if there are multiple labels. If None, it will raise an error if the dataset is not HPC or visual.
     """
 
     def __init__(
@@ -42,45 +38,38 @@ def __init__(
         is_discrete_labels: bool = False,
         dataset_label: str = None,
         metric: str = "correlation",
-        bool_oracle: bool = True,
-        label_ind: int = None,
     ):
         super().__init__()
         self.data = data
         self.label = label
-        self.label_ind = label_ind
         self.dataset_label = dataset_label
-        # check that label is 1D if dataset_label is not HPC/visual, and the label_ind is not provided
-        if (isinstance(self.label, np.ndarray) and self.label.ndim != 1
-                and self.dataset_label not in ["HPC", "visual"]):
-            # if the dataset contains multiple labels check that if it is not HPC dataset the label_ind was given
-            if self.label_ind != None:
-                self.label = label[:, label_ind]
-
-            else:
-                raise KeyError(
-                    "If dataset not HPC or visual and there are multiple possible labels, parameter label_ind must be provided to indicate which label will be used for the RDM calculation"
-                )
+        # check that label is 1D if dataset_label is not HPC/visual
+        if isinstance(self.label, np.ndarray) and self.dataset_label not in [
+                "HPC",
+                "visual",
+        ]:
+            if len(label.shape) > 1:
+                raise ValueError("The label must be a 1D array.")
+            self.label = label
 
         self.metric = metric
-        self.bool_oracle = bool_oracle
         self.discrete = is_discrete_labels
         self.idxs, self.num_bins = self._define_indices()
 
     def output_information(self):
         """Outputs information about the RDM class initialization parameters."""
         print("RDM class initialized with the following parameters:")
-        if self.bool_oracle:
-            print(
-                "The chosen analysis will plot the correlation of the RDMs with the Oracle RDM."
-            )
-        else:
-            print(
-                "The chosen analysis will plot the RDMs, no Oracle RDM comparison."
-            )
+        # if self.bool_oracle:
+        #     print(
+        #         "The chosen analysis will plot the correlation of the RDMs with the Oracle RDM."
+        #     )
+        # else:
+        #     print(
+        #         "The chosen analysis will plot the RDMs, no Oracle RDM comparison."
+        #     )
         if self.dataset_label is None:
             print(
-                f"The dataset label is not specified, the RDMs will be computed based on the label index {self.label_ind},\n this label has been noted DISCRETE = {self.discrete}."
+                f"The dataset label is not specified,\n this label has been noted DISCRETE = {self.discrete}."
             )
         else:
             print(f"The dataset label is specified as: {self.dataset_label}")
@@ -164,7 +153,9 @@ def _create_oracle_rdm(self):
         return oracle_rdm
 
     def _compute_per_layer(
-            self, layer_activation: npt.NDArray) -> Tuple[npt.NDArray, float]:
+            self,
+            layer_activation: npt.NDArray,
+            bool_oracle: bool = False) -> Tuple[npt.NDArray, float]:
         """Computes the RDM for a given layer's activation.
 
         Args:
@@ -181,7 +172,7 @@ def _compute_per_layer(
 
         rdm = pdist(layer_activation[self.idxs.flatten(), :],
                     metric=self.metric)
-        if self.bool_oracle:
+        if bool_oracle:
             oracle_rdm = self._create_oracle_rdm()
             comparison = 1 - correlation(oracle_rdm, rdm)
         else:
@@ -192,6 +183,7 @@ def _compute_per_layer(
     def compute(
         self,
         activations: List[Union[float, npt.NDArray]],
+        bool_oracle: bool = False,
     ) -> List[Tuple[npt.NDArray, float]]:
         """Computes the RDMs (Representational Dissimilarity Matrices) for each layer's activations.
 
@@ -209,7 +201,8 @@ def compute(
             activations = [activations]
 
         return super().iterate_over_layers(activations,
-                                           self._compute_per_layer)
+                                           self._compute_per_layer,
+                                           bool_oracle=bool_oracle)
 
     @property
     def __name__(self):
@@ -218,6 +211,7 @@ def __name__(self):
     def plot(
         self,
         rdms: Dict[str, List[npt.NDArray]],
+        bool_oracle: bool = False,
         titles: List[str] = None,
         cmap: str = "viridis",
         figsize: tuple = None,
@@ -241,7 +235,7 @@ def plot(
             matplotlib.figure.Figure
                 The figure containing the plotted RDMs.
         """
-        if self.bool_oracle:
+        if bool_oracle:
             return utils_plot.plot_rdm_correlation(rdms)
         else:
             return utils_plot.plot_rdm_all(
diff --git a/cebra_lens/quantification/tsne.py b/cebra_lens/quantification/tsne.py
index b4d1396..90a71e2 100644
--- a/cebra_lens/quantification/tsne.py
+++ b/cebra_lens/quantification/tsne.py
@@ -83,8 +83,8 @@ def plot(
         sample_plot: int = 200,
         dataset_label: str = None,
         group_name: str = "t-SNE",
-        label_ind: int = None,
         ax: Optional[matplotlib.axes.Axes] = None,
+        **kwargs: dict,
     ) -> matplotlib.figure.Figure:
         """Plots the t-SNE embeddings for the first 3 dimensions.
 
@@ -99,8 +99,6 @@ def plot(
                 The label of the dataset, used for determining the number of bins in the plot. Default is "HPC".
             group_name : str, optional
                 The name of the group for which the embeddings are plotted. Default is "t-SNE".
-            label_ind : int, optional
-                In case dataset in non-defined and we are plotting embeddings, we need to define the label index for the coloring of the embeddings.
             ax : Optional[matplotlib.axes.Axes], optional
                 The axes on which to plot the embeddings. If None, a new figure and axes will be created. Default is None.
 
@@ -113,7 +111,7 @@ def plot(
             labels=labels,
             group_name=group_name,
             dataset_label=dataset_label,
-            label_ind=label_ind,
             sample_plot=sample_plot,
             ax=ax,
+            **kwargs,
         )
diff --git a/cebra_lens/utils.py b/cebra_lens/utils.py
index 80ec77d..1a3734d 100644
--- a/cebra_lens/utils.py
+++ b/cebra_lens/utils.py
@@ -30,7 +30,7 @@ def get_data(
 
     Returns:
         list[npt.NDArray, npt.NDArray, npt.NDArray, npt.NDArray]
-            A list containing the datasets: train_data, test_data, label_train, label_test.
+            A list containing the datasets: train_data, test_data, train_label, test_label.
     """
 
     if dataset_label == "visual":
@@ -43,38 +43,11 @@ def get_data(
         )
 
 
-def extract_label(labels: npt.NDArray, label_ind: int) -> List:
-    """Extracts unique labels from a NumPy array of labels.
-    
-    Args:
-        labels : npt.NDArray
-            A NumPy array containing labels, which can be of any numeric type.
-        label_ind : int
-            The index of the label to extract from the array.
-
-    Returns:
-        List
-            A list of unique labels extracted from the input array.
-    """
-    try:
-        num_labels = labels.shape[1]
-    except:
-        num_labels = 1
-        labels = labels.reshape(-1, 1)
-    if label_ind > num_labels - 1:
-        raise ValueError(
-            f"label_ind {label_ind} is out of range for labels with shape {labels.shape}"
-        )
-    labels = labels[:, label_ind]
-
-    return labels
-
-
+#NOTE(celia): this is messy, but it is a temporary solution, could be improved in the future.
 def compute_metric(
     model_data: Dict[str, List[npt.NDArray[Any]]],
     metric_class: Any,
-    output_only: bool = False,
-    bool_oracle: bool = False,
+    **kwargs,
 ) -> Dict[str, npt.NDArray[Any]]:
     """Computes metrics for each model using a provided metric class.
 
@@ -95,7 +68,7 @@ def compute_metric(
     """
     result_dict = {}
 
-    if not isinstance(model_data, Dict):
+    if isinstance(metric_class, Decoding) and not isinstance(model_data, Dict):
         raise ValueError(
             "model_data should be a dictionary mapping model grouns to lists of model data."
         )
@@ -106,20 +79,10 @@ def compute_metric(
             result_dict[f"{comparison[0]}_v_{comparison[1]}"] = cka_matrix
 
     else:
-        if isinstance(metric_class, Decoding):
-            metric_class.output_only = output_only
-            metric_class.output_information()
-            print("\n")
-
-        if isinstance(metric_class, RDM):
-            metric_class.bool_oracle = bool_oracle
-            metric_class.output_information()
-            print("\n")
-
         for group_name, samples in model_data.items():
 
             computed_values = [
-                metric_class.compute(sample)
+                metric_class.compute(sample, **kwargs)
                 for sample in tqdm(samples, desc=f"Processing {group_name}")
             ]
             if not isinstance(metric_class, RDM):
@@ -127,6 +90,8 @@ def compute_metric(
 
             result_dict[group_name] = computed_values
 
+        metric_class.output_information()
+
     return result_dict
 
 
@@ -158,7 +123,9 @@ def plot_metric(
 
 def model_loader(
     model_dir: str,
-    groups: Dict[str, str] = {}
+    groups: Dict[str, str] = {},
+    backend: str = "torch",
+    device: Union[str, torch.device] = "cpu",
 ) -> Dict[str, List[cebra.integrations.sklearn.cebra.CEBRA]]:
     """Loads and categorizes CEBRA models from a given directory.
 
@@ -198,8 +165,8 @@ def model_loader(
     for file in models_folder_path.iterdir():
         if str(file).endswith((".pt", ".pth")):
             loaded_model = cebra.CEBRA.load(
-                file, backend="torch",
-                map_location=torch.device("cpu")).to("cpu")
+                file, backend=backend,
+                map_location=torch.device(device)).to(device)
             key = groups.get(file.stem, file.stem)
             models.setdefault(key, []).append(loaded_model)
             print(f"Model {file.stem} loaded successfully.")
diff --git a/cebra_lens/utils_allen.py b/cebra_lens/utils_allen.py
index 1e45397..97dd1fc 100644
--- a/cebra_lens/utils_allen.py
+++ b/cebra_lens/utils_allen.py
@@ -9,11 +9,6 @@
 import torch
 
 
-########################################################################################################################
-########################################################################################################################
-######################################## TAKEN FROM utils_allen.py of Célia ############################################
-########################################################################################################################
-########################################################################################################################
 def get_datasets(
     test_session=9,
     corrupted=False,
@@ -124,16 +119,6 @@ def obtain_pseudomice(mice, num_neurons_per_mouse=80):
     return pseudomouse
 
 
-def _add_gaussian_noise(neural_data, sigma: float = 2):
-    gaussian_noise = torch.normal(mean=0.0, std=sigma, size=neural_data.size())
-    return neural_data + gaussian_noise
-
-
-def _add_shot_noise(neural_data, scale_factor: float = 1.0):
-    # Neural data * scale_factor = Poisson lambda
-    return torch.poisson(neural_data * scale_factor) / scale_factor
-
-
 def _quantize_acc(frame_diff, time_window=1):
 
     true = (abs(frame_diff) < (time_window * 30)).sum()
@@ -154,8 +139,8 @@ def create_sequences(embedding, labels, seq_len=10):
 
 def decoding_frames(embedding_train,
                     embedding_test,
-                    label_train,
-                    label_test,
+                    train_label,
+                    test_label,
                     time_window=1,
                     seq_len=1):
     """1-frame decoding.
@@ -163,10 +148,10 @@ def decoding_frames(embedding_train,
     TODO(celia): Implement n-frames decoding. Started but not functional yet.
     """
     if seq_len > 1:
-        embedding_train, label_train = create_sequences(
-            embedding_train, label_train, seq_len)
-        embedding_test, label_test = create_sequences(embedding_test,
-                                                      label_test, seq_len)
+        embedding_train, train_label = create_sequences(
+            embedding_train, train_label, seq_len)
+        embedding_test, test_label = create_sequences(embedding_test,
+                                                      test_label, seq_len)
 
     params = np.power(np.linspace(1, 10, 10, dtype=int), 2)
     errs = []
@@ -177,16 +162,16 @@ def decoding_frames(embedding_train,
             train_decoder.fit(
                 embedding_train[:train_valid_idx].reshape(
                     -1, seq_len * embedding_train.shape[2]),
-                label_train[:train_valid_idx],
+                train_label[:train_valid_idx],
             )
             pred = train_decoder.predict(
                 embedding_train[train_valid_idx:].reshape(
                     -1, seq_len * embedding_train.shape[2]))
         else:
             train_decoder.fit(embedding_train[:train_valid_idx],
-                              label_train[:train_valid_idx])
+                              train_label[:train_valid_idx])
             pred = train_decoder.predict(embedding_train[train_valid_idx:])
-        err = label_train[train_valid_idx:] - pred
+        err = train_label[train_valid_idx:] - pred
         errs.append(abs(err).sum())
 
     test_decoder = cebra.KNNDecoder(n_neighbors=params[np.argmin(errs)],
@@ -194,15 +179,15 @@ def decoding_frames(embedding_train,
     if seq_len > 1:
         test_decoder.fit(
             embedding_train.reshape(-1, seq_len * embedding_train.shape[2]),
-            label_train)
+            train_label)
         frame_pred = test_decoder.predict(
             embedding_test.reshape(-1, seq_len * embedding_test.shape[2]))
     else:
-        test_decoder.fit(embedding_train, label_train)
+        test_decoder.fit(embedding_train, train_label)
         frame_pred = test_decoder.predict(embedding_test)
 
-    frame_errors = frame_pred - label_test
-    test_score = sklearn.metrics.r2_score(label_test, frame_pred)
+    frame_errors = frame_pred - test_label
+    test_score = sklearn.metrics.r2_score(test_label, frame_pred)
     test_err = np.median(abs(frame_errors))
     test_acc = _quantize_acc(frame_errors, time_window=1)
 
diff --git a/cebra_lens/utils_hpc.py b/cebra_lens/utils_hpc.py
index 1eb3fd2..f348884 100644
--- a/cebra_lens/utils_hpc.py
+++ b/cebra_lens/utils_hpc.py
@@ -28,11 +28,11 @@ def get_datasets(
 
     for i in rats:
         data = cebra.datasets.init(f"rat-hippocampus-single-{i}")
-        neural_train, neural_valid, label_train, label_valid = split_data_HPC(
+        neural_train, neural_valid, train_label, label_valid = split_data_HPC(
             data)
         train_datas.append(neural_train)
         valid_datas.append(neural_valid)
-        continuous_labels_train.append(label_train)
+        continuous_labels_train.append(train_label)
         continuous_labels_val.append(label_valid)
 
     if session_id is not None:
@@ -47,17 +47,17 @@ def get_datasets(
 def decoding_pos_dir(
     embedding_train: npt.NDArray,
     embedding_test: npt.NDArray,
-    label_train: npt.NDArray,
-    label_test: npt.NDArray,
+    train_label: npt.NDArray,
+    test_label: npt.NDArray,
 ):
     """Decodes position and direction from embeddings using K-Nearest Neighbors (KNN) and evaluates the performance.
     
     Args:
         embedding_train : npt.NDArray
         embedding_test : npt.NDArray
-        label_train : npt.NDArray
+        train_label : npt.NDArray
             Training labels, where the first column represents position and the second column represents direction.
-        label_test : npt.NDArray
+        test_label : npt.NDArray
             Testing labels, where the first column represents position and the second column represents direction.
     
     Returns:
@@ -72,17 +72,17 @@ def decoding_pos_dir(
     pos_decoder = cebra.KNNDecoder(n_neighbors=36, metric="cosine")
     dir_decoder = cebra.KNNDecoder(n_neighbors=36, metric="cosine")
 
-    pos_decoder.fit(embedding_train, label_train[:, 0])
-    dir_decoder.fit(embedding_train, label_train[:, 1])
+    pos_decoder.fit(embedding_train, train_label[:, 0])
+    dir_decoder.fit(embedding_train, train_label[:, 1])
 
     pos_pred = pos_decoder.predict(embedding_test)
     dir_pred = dir_decoder.predict(embedding_test)
 
     prediction = np.stack([pos_pred, dir_pred], axis=1)
 
-    test_score = sklearn.metrics.r2_score(label_test[:, :2], prediction)
-    pos_test_err = np.median(abs(prediction[:, 0] - label_test[:, 0]))
-    pos_test_score = sklearn.metrics.r2_score(label_test[:, 0], prediction[:,
+    test_score = sklearn.metrics.r2_score(test_label[:, :2], prediction)
+    pos_test_err = np.median(abs(prediction[:, 0] - test_label[:, 0]))
+    pos_test_score = sklearn.metrics.r2_score(test_label[:, 0], prediction[:,
                                                                            0])
 
     return test_score, pos_test_err, pos_test_score
@@ -99,19 +99,19 @@ def split_data_HPC(data: object, test_ratio: float = 0.2):
         tuple: A tuple containing four elements:
             - neural_train (numpy.ndarray): The training set for neural data.
             - neural_test (numpy.ndarray): The testing set for neural data.
-            - label_train (numpy.ndarray): The training set for continuous index labels.
-            - label_test (numpy.ndarray): The testing set for continuous index labels.
+            - train_label (numpy.ndarray): The training set for continuous index labels.
+            - test_label (numpy.ndarray): The testing set for continuous index labels.
     """
 
     split_idx = int(len(data) * (1 - test_ratio))
     neural_train = data.neural[:split_idx]
     neural_test = data.neural[split_idx:]
-    label_train = data.continuous_index[:split_idx]
-    label_test = data.continuous_index[split_idx:]
+    train_label = data.continuous_index[:split_idx]
+    test_label = data.continuous_index[split_idx:]
 
     return (
         neural_train.numpy(),
         neural_test.numpy(),
-        label_train.numpy(),
-        label_test.numpy(),
+        train_label.numpy(),
+        test_label.numpy(),
     )
diff --git a/cebra_lens/utils_plot.py b/cebra_lens/utils_plot.py
index da73910..33e8923 100644
--- a/cebra_lens/utils_plot.py
+++ b/cebra_lens/utils_plot.py
@@ -3,7 +3,9 @@
 import random
 from abc import *
 from typing import Dict, List, Optional, Tuple, Union
+from .quantification.decoder import DecodeResult
 
+import cebra
 import matplotlib.axes
 import matplotlib.pyplot as plt
 import numpy as np
@@ -12,6 +14,33 @@
 import torch
 
 
+def style():
+    """
+    Set the style of the plots.
+
+    This function sets the font color, size and weight, and axis label properties
+    for a matplotlib plot using the Google style guidelines.
+    """
+    font_color = "black"
+    font_size = 18
+    plt.rcParams.update({
+        "text.color": font_color,
+        "axes.labelcolor": font_color,
+        "axes.labelsize": font_size,
+        # "axes.titleweight": "bold",
+        "axes.titlesize": font_size,
+        "xtick.labelcolor": font_color,
+        "xtick.labelsize": font_size,
+        "ytick.labelcolor": font_color,
+        "ytick.labelsize": font_size,
+        "font.weight": "regular",
+        "svg.fonttype": "none",
+    })
+
+    plt.rc("axes.spines", top=False, bottom=True, left=True, right=False)
+    plt.rc("axes", edgecolor=font_color)
+
+
 class _BasePlot:
     """Base plotting class.
 
@@ -27,6 +56,7 @@ def __init__(
         axis: Optional[matplotlib.axes.Axes],
         figsize: Tuple[np.float64, np.float64],
     ):
+        style()
         if axis is None:
             self.fig, self.ax = plt.subplots(figsize=figsize)
         else:
@@ -138,11 +168,15 @@ def __init__(
         self.colors = sns.color_palette("husl", len(self.unique_keys))
 
     def _transform(self):
-        """Transforms ``results_dict`` into a dictionary where the key stays the same, but the values are now corresponding to the correlation between RDM and Oracle data across layers for model label.
+        """Transforms ``results_dict`` into a dictionary.
+        
+        Key stays the same but the values are now corresponding to the correlation 
+        between RDM and Oracle data across layers for model label.
 
         Returns:
             Dict[str,List[List[np.float64]]]
-                Dictionary where the keys correspond to the model labels, and the value to the correlation between RDM and Oracle data for each layer for each model inside a model label category.
+                Dictionary where the keys correspond to the model labels, and the value to the 
+                correlation between RDM and Oracle data for each layer for each model inside a model label category.
         """
         data = {}
         for key, data_list in self.results_dict.items():
@@ -189,7 +223,10 @@ def __init__(
         self.colors = sns.color_palette("husl", len(self.unique_keys))
 
     def _transform(self) -> Dict[str, List[List[np.float64]]]:
-        """Transforms ``results_dict`` into a dictionary where the key stays the same, but the values are now corresponding to the distance metric across layers for model label.
+        """Transforms ``results_dict`` into a dictionary.
+        
+        The returned dictionary is such that the keys stay the same, but the values are 
+        now corresponding to the distance metric across layers for model label.
 
         Returns:
             Dict[str,List[List[np.float64]]]
@@ -233,85 +270,103 @@ class DecodingPlot(_GenericPlot):
 
     def __init__(
         self,
-        results_dict: Dict[str, Dict[int, Tuple[np.float64, list, list]]],
+        results_dict: Dict[str, Dict[int, DecodeResult]],
         dataset_label: str = None,
         title: str = None,
         label: int = None,
+        label_types: List[str] = None,
         plot_error: bool = False,
         figsize: Tuple[np.float64, np.float64] = (15, 5),
-        axis: Optional[matplotlib.axes.Axes] = None,
+        axis: Optional[matplotlib.axes.Axes] = None, 
     ):
+        
+        # Use the first DecodeResult to infer the label type : regression or classification 
+        first_group_results = next(iter(results_dict.values())).tolist()
+        first_layer_result = next(iter(first_group_results[0].values()))
+       
+        this_type = first_layer_result.label_types[label]
+        
+        if not plot_error:
+            base_title = "Layer-wise decoding performance"
+        else:
+            base_title = "Layer-wise decoding error"
+
+        # if no dataset_label but a per‑label plot, add label index
+        if dataset_label is None and label is not None:
+            title = f"{base_title} (Label {label})"
+        else:
+            title = base_title
 
-        if title is not None:
-            if dataset_label == "visual":
-                title = "Decoding accuracies across layers (%)"
-            elif dataset_label == "HPC":
-                title = "Decoding position errors across layers (cm)"
-            else:
-                title = "Decoding average R^2 scores across layers"
-                if plot_error:
-                    title = "Decoding error scores across layers"
 
         super().__init__(axis, figsize, title)
         if dataset_label is None and label is None:
             raise ValueError(
-                "Please define the label score you want to plot. This is the index value corresponding to the label you want to plot in the decoding results."
-            )
+                "Please define the label score you want to plot. This is the index value corresponding "
+                "to the label you want to plot in the decoding results.")
         self.label = label
         self.plot_error = plot_error
         self.dataset_label = dataset_label
+        self.this_type = this_type
         self.results_dict = results_dict
         self.plot_data = self._transform()
         self.unique_keys = list(
             self.results_dict.keys())  # Define unique keys here
         self.colors = sns.color_palette("husl", len(self.unique_keys))
+    
+    def _y_axis_label(self) -> str:
+        """Pick the correct y-axis label from self.this_type and self.plot_error."""
+        if self.this_type == "classification":
+            return (
+                "Error (1 - accuracy)"
+                if self.plot_error else
+                "Accuracy (%)"
+            )
+        else:  # regression
+            return (
+                "Mean absolute error"
+                if self.plot_error else
+                "R² score"
+            )
 
-    def _transform(self) -> Dict[str, List[List[np.float64]]]:
-        """Transforms ``results_dict`` into a dictionary where the key stays the same, but the values are now corresponding to the decoding scores across layers for model label.
+    def _transform(self) -> Dict[str, List[List[float]]]:
+        """Transforms ``results_dict`` into a dictionary where the key stays the same, but the values 
+        are now corresponding to the decoding scores across layers for model label.
 
         Returns:
             Dict[str,List[List[np.float64]]]
                 Dictionary where the keys correspond to the model labels, and the value to the decoding scores for each layer for each model inside a model label category.
         """
-        data = {}
-        for idx, (group_name, models) in enumerate(self.results_dict.items()):
-            layer_values = []
-
-            for i, model in enumerate(models):
-
-                if self.dataset_label == "visual":
-                    ind = 2
-                elif self.dataset_label == "HPC":
-                    ind = 2
-                else:
-                    ind = self.label
-                layer_scores = []
-                for layer, scores in model.items():
+        out: Dict[str, List[List[float]]] = {}
+        for group_label, layer_map in self.results_dict.items():
+            runs: List[List[float]] = []
+            for run_layers in layer_map:
+                values = []
+                for layer_idx in sorted(run_layers.keys()):
+                    res = run_layers[layer_idx]
+                    # choose per-label vector or overall score
                     if self.dataset_label is None:
-                        if self.plot_error:
-                            layer_scores.append(scores[1][ind])
-                        else:
-                            layer_scores.append(scores[2][ind])
+                        vector = (
+                            res.per_label_error if self.plot_error else res.per_label_score
+                        )
+                        # index into chosen label (default 0 if None)
+                        values.append(vector[self.label or 0])
                     else:
-                        layer_scores.append(scores[ind])
-                layer_values.append(layer_scores)
-            data[group_name] = layer_values
-        return data
+                        # dataset-specific: overall_score for single-value metrics
+                        if res.overall_score is not None:
+                            values.append(res.overall_score)
+                        else:
+                            values.append(res.per_label_score[0])
+                runs.append(values)
+            out[group_label] = runs
+        return out
 
     def plot(self):
-        """Plots decoding accuracy across layers"""
-        if self.dataset_label == "visual":
-            y_axis = "Decoding accuracy (%)"
-        elif self.dataset_label == "HPC":
-            y_axis = "Decoding position error (cm)"
-        else:
-            if self.plot_error:
-                y_axis = "Decoding error score"
-            else:
-                y_axis = "Decoding $R^2$ score"
+        """Plots decoding accuracy (or error) across layers."""
+        y_axis = self._y_axis_label()
         return super().plot(self.plot_data, y_axis)
 
 
+
 def plot_rdm_correlation(
     rdm_dict: Dict[str, List[npt.NDArray]],
     title: str = "RDM comparison to Oracle",
@@ -375,10 +430,11 @@ def plot_distance(
 
 
 def plot_layer_decoding(
-    results_dict: Dict[str, npt.NDArray],
+    results_dict: Dict[str, Dict[int, DecodeResult]],
     title: str = "Decoding by layer",
     dataset_label: str = None,
     label: int = None,
+    label_types: List[str] = None,
     plot_error: bool = False,
     figsize: Tuple[np.float64, np.float64] = (15, 5),
     **kwargs,
@@ -404,12 +460,13 @@ def plot_layer_decoding(
         fig : matplotlib.figure.Figure
             The generated figure containing the decoding scored per layer per model.
     """
-
+    
     return DecodingPlot(
         results_dict=results_dict,
         title=title,
         dataset_label=dataset_label,
         label=label,
+        label_types=label_types, 
         plot_error=plot_error,
         figsize=figsize,
     ).plot(**kwargs)
@@ -472,7 +529,7 @@ def plot(self, **kwargs) -> None:
                                  1))  # X positions for scatter points
 
         for i, (key, results) in enumerate(self.results_dict.items()):
-            if self.dataset_label == "visual":
+            if (self.dataset_label == "visual"):
                 # for visual dataset get accuracy
                 score = [dict_el[0][2] for dict_el in results]
                 self.plot_label = "Accuracy"
@@ -582,36 +639,48 @@ class _EmbeddingPlot:
     def __init__(
         self,
         embeddings: List[npt.NDArray],
-        labels: npt.NDArray,
-        dataset_label: str,
-        axis: Optional[matplotlib.axes.Axes],
+        labels: Union[npt.NDArray, str] = "grey",
+        dataset_label: str = None,
+        axis: Optional[matplotlib.axes.Axes] = None,
         sample_plot: int = None,
         comparison_groups: Tuple = None,
     ):
-        self.figsize = (15, 10)
-        self.embeddings_list = embeddings
         self.labels = labels
         self.dataset_label = dataset_label
-        self.axs = self._define_ax(axis)
+        if axis is None:
+            self.figsize = (15, 10)
+        self.axs = self._define_ax(axis, embeddings)
+
+        self.embeddings = embeddings
+
+        if comparison_groups is None and len(embeddings) == 2:
+            raise ValueError(
+                f"Please provide a comparison_groups tuple if you want to plot two sets of embeddings."
+            )
+        elif comparison_groups is not None and len(embeddings) != 2:
+            raise ValueError(
+                f"Please provide two sets of embeddings if you want to plot a comparison_groups tuple, got {len(embeddings)} sets instead."
+            )
+
         if len(embeddings) == 1:
             self.embeddings = embeddings[0]
-            if sample_plot is None:
-                self.sample_plot = self.embeddings[0].shape[1]
+            if sample_plot is None or sample_plot > embeddings[0].shape[1]:
+                self.sample_plot = embeddings[0].shape[1]
             else:
                 self.sample_plot = sample_plot
 
         else:
-            self.embeddings_1 = embeddings[0]
-            self.embeddings_2 = embeddings[1]
-            if sample_plot is None:
-                self.sample_plot = self.embeddings_1[0].shape[1]
+            #self.embeddings_1 = embeddings[0]
+            #self.embeddings_2 = embeddings[1]
+            if sample_plot is None or sample_plot > embeddings[0][0].shape[1]:
+                self.sample_plot = embeddings[0][0].shape[1]
             else:
                 self.sample_plot = sample_plot
 
             self.comparison_groups = comparison_groups
 
-            self.axs_1 = self.ax[0, :]
-            self.axs_2 = self.ax[1, :]
+        #NOTE(celia): the sampling so that embeddings and labels have the same number of samples
+        # is a bit weird for now.
 
     def _multi_padding_check(self, embeddings_1, embeddings_2):
         """Check if the two embeddings have the same number of layer. 
@@ -637,9 +706,8 @@ def _multi_padding_check(self, embeddings_1, embeddings_2):
             embeddings_1 += [np.empty_like(embeddings_1[0])
                              ] * (self.num_layers_2 - self.num_layers_1)
 
-    def _define_ax(
-            self,
-            axis: Optional[matplotlib.axes.Axes]) -> matplotlib.axes.Axes:
+    def _define_ax(self, axis: Optional[matplotlib.axes.Axes],
+                   embeddings: List[npt.NDArray]) -> matplotlib.axes.Axes:
         """Define the ax on which to generate the plot.
 
         Args:
@@ -650,9 +718,8 @@ def _define_ax(
             A ``matplotlib.axes.Axes`` on which to generate the plot.
         """
         if axis is None:
-            if len(self.embeddings_list) == 2:
-                self._multi_padding_check(self.embeddings_list[0],
-                                          self.embeddings_list[1])
+            if len(embeddings) == 2:
+                self._multi_padding_check(embeddings[0], embeddings[1])
                 self.fig, self.ax = plt.subplots(
                     2,
                     max(self.num_layers_1, self.num_layers_2),
@@ -662,7 +729,7 @@ def _define_ax(
             else:
                 self.fig, self.ax = plt.subplots(
                     1,
-                    len(self.embeddings_list[0]),
+                    len(embeddings[0]),
                     figsize=(15, 10),
                     subplot_kw={"projection": "3d"},
                 )
@@ -670,88 +737,11 @@ def _define_ax(
             self.ax = axis
         return self.ax
 
-    def _plot_dataset(
-            self,
-            ax: matplotlib.axes.Axes,
-            embedding: npt.NDArray,
-            label: str,
-            label_ind: int = None,
-            gray: bool = False,
-            idx_order: Tuple[int, int, int] = (0, 1, 2),
-    ) -> matplotlib.axes.Axes:
-        """Plot the dataset embedding, for generic dataset.
-        
-        Note: 
-            By default, it plots all labels.
-
-        Args: 
-            ax : matplotlib.axes.Axes
-                The axis on which to plot the embedding.
-            embedding : npt.NDArray
-                The embedding data to be plotted, shape Samples X num Neurons.
-            label : str
-                The label data corresponding to the embedding, shape Samples X num Labels.
-            label_ind : int, optional
-                The index of the label to be used for coloring the points in the embedding plot.
-            gray : bool, optional
-                If True, will plot the embedding in gray scale (default is False).
-            idx_order : Tuple[int, int, int], optional
-                The order of indices to use for the x, y, and z axes in the 3D plot (default is (0, 1, 2)).
-
-        Returns:
-            ax : matplotlib.axes.Axes
-                The axis with the plotted embedding.
-        """
-        idx1, idx2, idx3 = idx_order
-        label = np.atleast_2d(label)
-        if label.shape[0] == 1 and label.shape[1] != 1:
-            label = label.T
-
-        if (0 in np.unique(label[:, label_ind])
-                and 1 in np.unique(label[:, label_ind])
-                and len(np.unique(label[:, label_ind])) == 2):
-            l_ind = label[:, label_ind] == 1
-            l_c = label[l_ind, label_ind]
-            l = ax.scatter(
-                embedding[l_ind, idx1],
-                embedding[l_ind, idx2],
-                embedding[l_ind, idx3],
-                c=l_c,
-                cmap="cool",
-                s=0.05,
-                alpha=0.75,
-            )
-
-        else:
-            c = label[:, label_ind]
-
-            idx1, idx2, idx3 = idx_order
-            ax.scatter(
-                embedding[:, idx1],
-                embedding[:, idx2],
-                embedding[:, idx3],
-                c=c,
-                cmap="magma",
-                s=0.05,
-                alpha=0.75,
-            )
-
-        ax.grid(False)
-        ax.xaxis.pane.fill = False
-        ax.yaxis.pane.fill = False
-        ax.zaxis.pane.fill = False
-        ax.xaxis.pane.set_edgecolor("w")
-        ax.yaxis.pane.set_edgecolor("w")
-        ax.zaxis.pane.set_edgecolor("w")
-
-        return ax
-
     def _plot_hippocampus(
             self,
             ax: matplotlib.axes.Axes,
             embedding: npt.NDArray,
-            label: str,
-            gray: bool = False,
+            label: npt.NDArray,
             idx_order: Tuple[int, int, int] = (0, 1, 2),
     ) -> matplotlib.axes.Axes:
         """Plot the hippocampus embedding.
@@ -763,8 +753,6 @@ def _plot_hippocampus(
                 The embedding data to be plotted, shape Samples X num Neurons.
             label : str
                 The label data corresponding to the embedding, shape Samples X num Labels.
-            gray : bool, optional
-                If True, will plot the embedding in gray scale (default is False).
             idx_order : Tuple[int, int, int], optional
                 The order of indices to use for the x, y, and z axes in the 3D plot (default is (0, 1, 2)).
 
@@ -772,19 +760,13 @@ def _plot_hippocampus(
             ax : matplotlib.axes.Axes
                 The axis with the plotted embedding.
         """
+
         r_ind = label[:, 1] == 1
         l_ind = label[:, 2] == 1
-
-        if not gray:
-            r_cmap = "cool"
-            l_cmap = "magma"
-            r_c = label[r_ind, 0]
-            l_c = label[l_ind, 0]
-        else:
-            r_cmap = None
-            l_cmap = None
-            r_c = "gray"
-            l_c = "gray"
+        r_cmap = "cool"
+        l_cmap = "magma"
+        r_c = label[r_ind, 0]
+        l_c = label[l_ind, 0]
 
         idx1, idx2, idx3 = idx_order
         r = ax.scatter(
@@ -806,63 +788,6 @@ def _plot_hippocampus(
             alpha=0.75,
         )
 
-        ax.grid(False)
-        ax.xaxis.pane.fill = False
-        ax.yaxis.pane.fill = False
-        ax.zaxis.pane.fill = False
-        ax.xaxis.pane.set_edgecolor("w")
-        ax.yaxis.pane.set_edgecolor("w")
-        ax.zaxis.pane.set_edgecolor("w")
-
-        return ax
-
-    def _plot_allen(
-            self,
-            ax: matplotlib.axes.Axes,
-            embedding: npt.NDArray,
-            label: str,
-            gray: bool = False,
-            idx_order: Tuple[int, int, int] = (0, 1, 2),
-    ) -> matplotlib.axes.Axes:
-        """Plot the Allen embedding.
-
-        Args:
-            ax : matplotlib.axes.Axes
-                The axis on which to plot the embedding.
-            embedding : npt.NDArray
-                The embedding data to be plotted, shape Samples X num Neurons.
-            label : str
-                The label data corresponding to the embedding, shape Samples X num Labels.
-            gray : bool, optional
-                If True, will plot the embedding in gray scale (default is False).
-            idx_order : Tuple[int, int, int], optional
-                The order of indices to use for the x, y, and z axes in the 3D plot (default is (0, 1, 2)).
-
-        Returns:
-            ax : matplotlib.axes.Axes
-                The axis with the plotted embedding.
-        """
-        c = label
-
-        idx1, idx2, idx3 = idx_order
-        ax.scatter(
-            embedding[:, idx1],
-            embedding[:, idx2],
-            embedding[:, idx3],
-            c=c,
-            cmap="magma",
-            s=0.05,
-            alpha=0.75,
-        )
-
-        ax.grid(False)
-        ax.xaxis.pane.fill = False
-        ax.yaxis.pane.fill = False
-        ax.zaxis.pane.fill = False
-        ax.xaxis.pane.set_edgecolor("w")
-        ax.yaxis.pane.set_edgecolor("w")
-        ax.zaxis.pane.set_edgecolor("w")
-
         return ax
 
     def plot_embedding_layers(
@@ -870,7 +795,7 @@ def plot_embedding_layers(
         axs: List[matplotlib.axes.Axes],
         embeddings: List[npt.NDArray],
         group_name: str,
-        label_ind: int = None,
+        **kwargs,
     ):
         """
         Plots the embedding layers on the provided axes. Used in tSNE and in normal CEBRA.
@@ -882,19 +807,21 @@ def plot_embedding_layers(
                 List of numpy arrays containing the embeddings for each layer. Each array is shape Samples X num Neurons.
             group_name : str
                 Title of the plot (e.g., 'single' or 'multi').
-            label_ind : int, optional
-                The index of the label to be used for coloring the points in the embedding plot. If None, dataset_label needs to be defined as not None.
         """
         num_layers = len(embeddings)
         self.fig.suptitle(
             f"{group_name}",
             fontsize=20,
         )
-        labels_list = [self.labels[:self.sample_plot]] * num_layers
+        if not isinstance(self.labels, str):
+            labels = [self.labels[:self.sample_plot]] * num_layers
+        else:
+            labels = [self.labels] * num_layers
+
         titles = [f"Layer {layer}" for layer in range(1, num_layers)]
         titles.append("Output layer")
 
-        for i, (label, ax) in enumerate(zip(labels_list, axs)):
+        for i, (label, ax) in enumerate(zip(labels, axs)):
             if (embeddings[i].shape[0] < embeddings[i].shape[1]
                 ):  # should be num Samples X num Neurons
                 embedding = embeddings[i].T
@@ -902,61 +829,43 @@ def plot_embedding_layers(
                 embedding = embeddings[i]
 
             embedding = embedding[:self.sample_plot, :]
+
             if self.dataset_label == "HPC":
                 ax = self._plot_hippocampus(ax, embedding, label)
-            elif self.dataset_label == "visual":
-                ax = self._plot_allen(ax, embedding, label)
             else:
-                ax = self._plot_dataset(ax,
-                                        embedding,
-                                        label,
-                                        label_ind=label_ind)
+                ax = cebra.plot_embedding(
+                    embedding=embedding,
+                    embedding_labels=label,
+                    ax=ax,
+                    title="",
+                    **kwargs,
+                )
+
+            ax.grid(False)
+            ax.xaxis.pane.fill = False
+            ax.yaxis.pane.fill = False
+            ax.zaxis.pane.fill = False
+            ax.xaxis.pane.set_edgecolor("w")
+            ax.yaxis.pane.set_edgecolor("w")
+            ax.zaxis.pane.set_edgecolor("w")
 
             ax.set_title(titles[i], y=1)
             ax.axis("off")
             plt.subplots_adjust(wspace=0, hspace=0)
             plt.tight_layout()
 
-    def plot_embedding(self, group_name: str, label_ind: int = None):
+    def plot_embedding(self, group_name: str, **kwargs):
         """Plots embedding layers for a single model.
 
         Args:
             group_name : str
                 The name of the group to be used for the plot title.
-            label_ind : int
-                The index of the label to be used for coloring the points in the embedding plot.
         """
 
-        return self.plot_embedding_layers(self.axs,
-                                          self.embeddings,
-                                          group_name,
-                                          label_ind=label_ind)
-
-    def plot_compare(self, label_ind: int = None):
-        """Plots embedding layers for models being compared
-
-        Args:
-            label_ind : int
-                The index of the label to be used for coloring the points in the embedding plot.
-        """
-        self.plot_embedding_layers(
-            self.axs_1,
-            self.embeddings_1,
-            self.comparison_groups[1][0],
-            label_ind=label_ind,
-        )
-        self.plot_embedding_layers(
-            self.axs_2,
-            self.embeddings_2,
-            self.comparison_groups[1][1],
-            label_ind=label_ind,
-        )
-        self.fig.suptitle(
-            f"CEBRA across layers comparison",
-            fontsize=20,
-        )
-        plt.subplots_adjust(wspace=0, hspace=0)
-        plt.tight_layout()
+        return self.plot_embedding_layers(axs=self.axs,
+                                          embeddings=self.embeddings,
+                                          group_name=group_name,
+                                          **kwargs)
 
 
 def compare_embeddings_layers(
@@ -966,7 +875,6 @@ def compare_embeddings_layers(
     comparison_groups: Tuple = ("tSNE", ["Untrained", "Trained"]),
     dataset_label: str = None,
     sample_plot: int = None,
-    label_ind: int = None,
     ax: Optional[matplotlib.axes.Axes] = None,
     **kwargs,
 ) -> plt.Figure:
@@ -986,8 +894,6 @@ def compare_embeddings_layers(
             Dataset identifier.
         sample_plot : int, optional
             Number of samples to plot from the embeddings (default is None, which means all samples will be plotted).
-        label_ind : int, optional
-            The index of the label to be used for coloring the points in the embedding plot (default is None).
         ax : matplotlib.axes.Axes, optional
             Matplotlib axes object (default is None).
 
@@ -995,22 +901,42 @@ def compare_embeddings_layers(
         fig : matplotlib.figure.Figure
             The generated figure containing the embedding comparison plots.
     """
-    return _EmbeddingPlot(
+
+    embeddings = _EmbeddingPlot(
         embeddings=[embeddings_1, embeddings_2],
         labels=labels,
         comparison_groups=comparison_groups,
         dataset_label=dataset_label,
         sample_plot=sample_plot,
         axis=ax,
-    ).plot_compare(label_ind, **kwargs)
+    )
+
+    embeddings.plot_embedding_layers(
+        axs=embeddings.ax[0, :],
+        embeddings=embeddings.embeddings[0],
+        group_name=embeddings.comparison_groups[1][0],
+        **kwargs,
+    )
+    embeddings.plot_embedding_layers(
+        axs=embeddings.ax[1, :],
+        embeddings=embeddings.embeddings[1],
+        group_name=embeddings.comparison_groups[1][1],
+        **kwargs,
+    )
+
+    embeddings.fig.suptitle(
+        f"CEBRA across layers comparison",
+        fontsize=20,
+    )
+    plt.subplots_adjust(wspace=0, hspace=0)
+    plt.tight_layout()
 
 
 def plot_embeddings(
     data: Union[Dict[str, List[npt.NDArray]], List[npt.NDArray]],
-    labels: npt.NDArray,
+    labels: Union[npt.NDArray, str] = "grey",
     group_name: str = None,
     dataset_label: str = None,
-    label_ind: int = None,
     sample_plot: int = None,
     ax: Optional[matplotlib.axes.Axes] = None,
     **kwargs,
@@ -1038,6 +964,10 @@ def plot_embeddings(
         fig : matplotlib.figure.Figure
             The generated figure containing the embedding plots.
     """
+    if not isinstance(labels, str) and len(labels.shape) > 1:
+        raise ValueError(
+            "Labels should be a 1D array. If you have multiple labels,"
+            "please pass the one you want to plot.")
 
     data_dict = data
     if not isinstance(data, Dict):
@@ -1054,9 +984,7 @@ def plot_embeddings(
                 dataset_label=dataset_label,
                 sample_plot=sample_plot,
                 axis=ax,
-            ).plot_embedding(group_name=f"{group_name} instance {i}",
-                             label_ind=label_ind,
-                             **kwargs)
+            ).plot_embedding(group_name=f"{group_name} instance {i}", **kwargs)
 
 
 class _ActivationPlot:
@@ -1081,7 +1009,7 @@ class _ActivationPlot:
 
     def __init__(
         self,
-        input_data: torch.Tensor,
+        # input_data: torch.Tensor,
         embeddings: List[npt.NDArray],
         figsize: Tuple[np.float64, np.float64],
         axis: Optional[matplotlib.axes.Axes],
@@ -1090,7 +1018,7 @@ def __init__(
         title: str = "Trained activations",
     ):
         self.figsize = figsize
-        self.input_data = input_data
+        # self.input_data = input_data
         self.embeddings = embeddings
         self.sample_plot = sample_plot
         self.cmap = cmap
@@ -1112,27 +1040,27 @@ def _define_ax(
             A ``matplotlib.axes.Axes`` on which to generate the plot.
         """
         if axis is None:
-            self.fig, self.axes = plt.subplots(self.num_layers + 1,
+            self.fig, self.axes = plt.subplots(self.num_layers,
                                                1,
                                                figsize=self.figsize)
         else:
             self.axes = [axis] + [
-                axis.figure.add_subplot(self.num_layers + 1, 1, i + 2)
+                axis.figure.add_subplot(self.num_layers, 1, i + 2)
                 for i in range(self.num_layers)
             ]
 
     def plot(self):
         """Handles plotting logic."""
-        self.axes[0].imshow(self.input_data.T[:, 0:self.sample_plot],
-                            aspect="auto")
-        self.axes[0].set_title("Input Data")
-        self.axes[0].set_ylabel("Channel #")
-        self.axes[0].set_xlabel("Time")
-        self.axes[0].grid(False)
+        # self.axes[0].imshow(self.input_data.T[:, 0:self.sample_plot],
+        #                     aspect="auto")
+        # self.axes[0].set_title("Input Data")
+        # self.axes[0].set_ylabel("Channel #")
+        # self.axes[0].set_xlabel("Time")
+        # self.axes[0].grid(False)
 
         # Plot the embeddings for each layer
         for i in range(self.num_layers):
-            self.axes[i + 1].imshow(
+            self.axes[i].imshow(
                 self.embeddings[i][:, 0:self.sample_plot],
                 cmap=self.cmap,
                 aspect="auto",
@@ -1141,17 +1069,17 @@ def plot(self):
                 layer_title = "Output Layer"
             else:
                 layer_title = f"Layer {i + 1}"
-            self.axes[i + 1].set_title(layer_title)
-            self.axes[i + 1].set_ylabel("Unit #")
-            self.axes[i + 1].set_xlabel("Time")
-            self.axes[i + 1].grid(False)
+            self.axes[i].set_title(layer_title)
+            self.axes[i].set_ylabel("Unit #")
+            self.axes[i].set_xlabel("Time")
+            self.axes[i].grid(False)
 
         # Adjust layout for better spacing
         plt.tight_layout()
 
 
 def plot_activations(
-    input_data: torch.Tensor,
+    # input_data: torch.Tensor,
     data: Union[Dict[str, List[npt.NDArray]], List[npt.NDArray]],
     sample_plot: int = 100,
     cmap: str = "magma",
@@ -1191,7 +1119,7 @@ def plot_activations(
     for group_name, models in data_dict.items():
         for i, embs in enumerate(models):
             fig = _ActivationPlot(
-                input_data=input_data,
+                # input_data=input_data,
                 embeddings=embs,
                 sample_plot=sample_plot,
                 cmap=cmap,
@@ -1423,7 +1351,6 @@ def __init__(
         # Generate tick labels specific to the dataset
         if dataset_label == "visual":
             self.tick_labels = [str(i) for i in range(0, 930, 30)]
-
         elif self.dataset_label == "HPC":
             self.tick_positions = (np.arange(0, 34, 2) / 10
                                    )  # Ticks at 0, 0.2, 0.4,..., 1.6
diff --git a/cebra_lens/utils_wrapper.py b/cebra_lens/utils_wrapper.py
new file mode 100644
index 0000000..0055434
--- /dev/null
+++ b/cebra_lens/utils_wrapper.py
@@ -0,0 +1,22 @@
+import inspect
+
+from cebra.integrations.sklearn.cebra import CEBRA
+from cebra.solver.base import Solver
+
+
+def transform(model, data, label, **transform_kwargs):
+    if isinstance(model, Solver):
+        transform_signature = inspect.signature(model.transform)
+        if "labels" in transform_signature.parameters:
+            embedding = model.transform(inputs=data, labels=label, **transform_kwargs)
+        else:
+            embedding = model.transform(inputs=data, **transform_kwargs)
+    elif isinstance(model, CEBRA):
+        embedding = model.transform(inputs=data, **transform_kwargs)
+    else:
+        raise TypeError(
+            "Model must be an instance of cebra.solver.base.Solver "
+            "or cebra.integrations.sklearn.cebra.CEBRA, "
+            f"got {type(model)} instead.",
+        )
+    return embedding
diff --git a/demos/UsageDemoGENERAL.ipynb b/demos/UsageDemoGENERAL.ipynb
index 4397392..31bb003 100644
--- a/demos/UsageDemoGENERAL.ipynb
+++ b/demos/UsageDemoGENERAL.ipynb
@@ -142,7 +142,7 @@
    "source": [
     "### Model decoding\n",
     "\n",
-    "To verify the model performance the decode_models function can be called. This will give a dictionnary with each key being the name of the model \"single_UT\" \"multi_TR\" ... and plot the performance accordingly. This is flexible and can thus take many different models.\n",
+    "To verify the model performance the decode_models function can be called. This will give a dictionary with each key being the name of the model \"single_UT\" \"multi_TR\" ... and plot the performance accordingly. This is flexible and can thus take many different models.\n",
     "\n",
     "If the data comes from either the Hippocampus or the Allen visual dataset the decoding functions are defined specifically, but if the data is from another dataset, the decoding scores are calculated with a generic decoding function where there are 3 outputs:\n",
     "- average R^2 score over all labels\n",
diff --git a/demos/UsageDemoVISUAL.ipynb b/demos/UsageDemoVISUAL.ipynb
index 201f859..b4b825f 100644
--- a/demos/UsageDemoVISUAL.ipynb
+++ b/demos/UsageDemoVISUAL.ipynb
@@ -149,7 +149,7 @@
    "source": [
     "## Model decoding\n",
     "\n",
-    "To verify the model performance the decode_models function can be called. This will give a dictionnary with each key being the name of the model \"single_UT\" \"multi_TR\" ... and plot the performance accordingly. This is flexible and can thus take many different models.\n",
+    "To verify the model performance the decode_models function can be called. This will give a dictionary with each key being the name of the model \"single_UT\" \"multi_TR\" ... and plot the performance accordingly. This is flexible and can thus take many different models.\n",
     "\n",
     "If the data comes from either the Hippocampus or the Allen visual dataset the decoding functions are defined specifically, but if the data is from another dataset, the decoding scores are calculated with a generic decoding function where there are 3 outputs:\n",
     "- average R^2 score over all labels\n",
diff --git a/demos/metric_template.py b/demos/metric_template.py
index afa477f..80cf985 100644
--- a/demos/metric_template.py
+++ b/demos/metric_template.py
@@ -36,7 +36,7 @@ def _compute_per_layer(self, layer_data: npt.NDArray) -> npt.NDArray:
         --------
         newmetric_for_layer : npt.NDArray
         """
-        #New Metric computation for layer
+        # New Metric computation for layer
 
         return NewMetric_for_layer
 
@@ -57,12 +57,12 @@ def compute(
             The 2D embedding produced by t-SNE for each layer of a model.
         """
 
-        #Computation logic insert
+        # Computation logic insert
 
-        #If the computation is done by layer then
+        # If the computation is done by layer then
         return super().iterate_over_layers(data, self._compute_per_layer)
 
-        #If the computation is done on the data directly
+        # If the computation is done on the data directly
         return result
 
     @property
@@ -88,7 +88,7 @@ def plot(
             The figure containing the NewMetric plot.
         """
 
-        #Need to define the plot_newMetric function in the utils_plot.py
+        # Need to define the plot_newMetric function in the utils_plot.py
         return plot_newMetric(
             embeddings,
             labels,
diff --git a/docs/docs/examples/UsageDemo.ipynb b/docs/docs/examples/UsageDemo.ipynb
index b205408..96ef776 100644
--- a/docs/docs/examples/UsageDemo.ipynb
+++ b/docs/docs/examples/UsageDemo.ipynb
@@ -154,7 +154,7 @@
    "source": [
     "## Model decoding\n",
     "\n",
-    "To verify the model performance the decode_models function can be called. This will give a dictionnary with each key being the name of the model \"single_UT\" \"multi_TR\" ... and plot the performance accordingly. This is flexible and can thus take many different models."
+    "To verify the model performance the decode_models function can be called. This will give a dictionary with each key being the name of the model \"single_UT\" \"multi_TR\" ... and plot the performance accordingly. This is flexible and can thus take many different models."
    ]
   },
   {
diff --git a/test_unified.ipynb b/test_unified.ipynb
new file mode 100644
index 0000000..1563eb1
--- /dev/null
+++ b/test_unified.ipynb
@@ -0,0 +1,840 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e453f8ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e7fae20a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading cached model. If you want to re-run, set USE_CACHED_MODEL to False.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/cebra/__init__.py:118: UserWarning: Your code triggered a lazy import of cebra.datasets. While this will (likely) work, it is recommended to add an explicit import statement to you code instead. To disable this warning, you can run ``cebra.allow_lazy_imports()``.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "USE_CACHED_MODEL = True\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from matplotlib.colors import ListedColormap, BoundaryNorm\n",
+    "import os\n",
+    "import seaborn as sns\n",
+    "import sklearn.metrics\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "\n",
+    "import cebra\n",
+    "from cebra.integrations.plotly import plot_embedding_interactive\n",
+    "from plotly.subplots import make_subplots\n",
+    "\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "\n",
+    "# Load the datasets\n",
+    "hippocampus_a = cebra.datasets.init('rat-hippocampus-single-achilles')\n",
+    "hippocampus_b = cebra.datasets.init('rat-hippocampus-single-buddy')\n",
+    "hippocampus_c = cebra.datasets.init('rat-hippocampus-single-cicero')\n",
+    "hippocampus_g = cebra.datasets.init('rat-hippocampus-single-gatsby')\n",
+    "\n",
+    "datasets = [hippocampus_a, hippocampus_b, hippocampus_c, hippocampus_g]\n",
+    "\n",
+    "# Download the cached model\n",
+    "if USE_CACHED_MODEL:\n",
+    "    print(\"Downloading cached model. If you want to re-run, set USE_CACHED_MODEL to False.\")\n",
+    "    import os, requests, zipfile, shutil\n",
+    "    model_path = os.path.join(\"model\", \"250609-unified-reference-run.pt\")\n",
+    "    if not os.path.exists(model_path):\n",
+    "        url = \"https://figshare.com/ndownloader/articles/29275358?private_link=ae8099185f5e2f5e8185\"\n",
+    "        with open(\"data.tgz\", \"wb\") as f: f.write(requests.get(url).content)\n",
+    "        with zipfile.ZipFile(\"data.tgz\", \"r\") as zip_ref: zip_ref.extractall()\n",
+    "        os.makedirs(\"model\", exist_ok=True)\n",
+    "        shutil.move(\"250609-unified-reference-run.pt\", \"model\")\n",
+    "        if os.path.exists(\"data.tgz\"): os.remove(\"data.tgz\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b0b925bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split data and labels (labels we use later!)\n",
+    "train_data, valid_data = [], []\n",
+    "train_continuous_label, valid_continuous_label = [], []\n",
+    "for i, dataset in enumerate(datasets):\n",
+    "    split_idx = int(0.8 * len(dataset.neural)) #suggest: 5%-20% depending on your dataset size\n",
+    "\n",
+    "    train_data.append(dataset.neural[:split_idx])\n",
+    "    valid_data.append(dataset.neural[split_idx:])\n",
+    "\n",
+    "    train_continuous_label.append(dataset.continuous_index.numpy()[:split_idx])\n",
+    "    valid_continuous_label.append(dataset.continuous_index.numpy()[split_idx:])\n",
+    "\n",
+    "train_datasets = [\n",
+    "    cebra.data.TensorDataset(\n",
+    "        neural=train_data[i], continuous=train_continuous_label[i], device=device\n",
+    "    )\n",
+    "    for i in range(len(train_data))\n",
+    "]\n",
+    "\n",
+    "valid_datasets = [\n",
+    "    cebra.data.TensorDataset(valid_data[i], continuous=valid_continuous_label[i], device=device)\n",
+    "    for i in range(len(valid_data))\n",
+    "]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c49b159c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Hyperparameters:\n",
+    "config = {\"model_architecture\": \"offset10-model\",\n",
+    "    \"batch_size\": 2048,\n",
+    "    \"output_dimension\": 32,\n",
+    "    \"time_offsets\": 10,\n",
+    "    \"learning_rate\": 0.0003,\n",
+    "    \"num_hidden_units\": 32,\n",
+    "    \"temperature\": 1,\n",
+    "    \"max_iterations\": 5000,\n",
+    "    \"conditional\": \"time_delta\",\n",
+    "    \"verbose\": True,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5b9bf340",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Number of neurons = sum of neurons in all datasets\n",
+    "num_neurons = 0\n",
+    "for dataset in train_datasets:\n",
+    "    num_neurons += dataset.neural.shape[1]\n",
+    "\n",
+    "# Define the dataset\n",
+    "dataset = cebra.data.UnifiedDataset(dataset for dataset in train_datasets)\n",
+    "\n",
+    "# Set the masks for the dataset\n",
+    "# 'RandomNeuronMask' and 'RandomTimestepMask' are used to randomly mask neurons and timesteps\n",
+    "# during training, which helps the model to learn better representations.\n",
+    "dataset.set_masks({\"RandomNeuronMask\": (0.3, 0.9, 0.05),\n",
+    "                   \"RandomTimestepMask\": (0.3, 0.9, 0.05)})\n",
+    "\n",
+    "dataset.to(device)\n",
+    "\n",
+    "# Define the dataset loader\n",
+    "loader = cebra.data.UnifiedLoader(\n",
+    "    dataset,\n",
+    "    conditional=config[\"conditional\"],\n",
+    "    num_steps=config[\"max_iterations\"],\n",
+    "    batch_size=config[\"batch_size\"],\n",
+    "    time_offset=config[\"time_offsets\"],\n",
+    ")\n",
+    "\n",
+    "dataset.to(device)\n",
+    "\n",
+    "# Define the model\n",
+    "model = cebra.models.init(\n",
+    "    config[\"model_architecture\"],\n",
+    "    num_neurons=num_neurons,\n",
+    "    num_units=config[\"num_hidden_units\"],\n",
+    "    num_output=config[\"output_dimension\"],\n",
+    ")\n",
+    "model.to(device)\n",
+    "dataset.configure_for(model)\n",
+    "\n",
+    "criterion = cebra.models.FixedCosineInfoNCE(temperature=config[\"temperature\"])\n",
+    "criterion.to(device)\n",
+    "optimizer = torch.optim.Adam(list(model.parameters()), lr=config[\"learning_rate\"])\n",
+    "\n",
+    "# Define the solver\n",
+    "solver = cebra.solver.UnifiedSolver(\n",
+    "    model=model, criterion=criterion, optimizer=optimizer, tqdm_on=config[\"verbose\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0a34f4f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if os.path.exists(\"model/250609-unified-reference-run.pt\"):\n",
+    "    checkpoint = torch.load('model/250609-unified-reference-run.pt', map_location=device, weights_only = False)\n",
+    "    solver.load_state_dict(checkpoint, strict=True)\n",
+    "else:\n",
+    "    solver.fit(loader)\n",
+    "    solver.save(logdir = \"model\", filename = \"250609-unified-reference-run.pt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ef0cd22c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cebra_lens as lens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3cccf0ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_path  = \"model\"\n",
+    "\n",
+    "groups = {'250609-unified-reference-run.pt':'unified_model',}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6a1f922b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model 250609-unified-reference-run loaded successfully.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pathlib\n",
+    "models_folder_path = pathlib.Path(\"model\")\n",
+    "\n",
+    "models = {}\n",
+    "for file in models_folder_path.iterdir():\n",
+    "    if str(file).endswith((\".pt\", \".pth\")):\n",
+    "        # loaded_model = cebra.CEBRA.load(\n",
+    "        #     file, backend=backend, map_location=torch.device(\"cpu\")\n",
+    "        # ).to(\"cpu\")\n",
+    "        checkpoint = torch.load(file, map_location=device, weights_only = False)\n",
+    "        solver.load_state_dict(checkpoint, strict=True)\n",
+    "        key = groups.get(file.stem, file.stem)\n",
+    "        models.setdefault(key, []).append(solver)\n",
+    "        print(f\"Model {file.stem} loaded successfully.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d16f5238",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm\n",
+    "import torch.nn as nn\n",
+    "\n",
+    "dataset_label = None\n",
+    "layer_type = nn.Conv1d\n",
+    "session_id = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0f21fe2d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decoding_models = lens.Decoding(train_data, train_continuous_label, \n",
+    "                                valid_data, valid_continuous_label, \n",
+    "                                session_id=session_id, \n",
+    "                                layer_type=layer_type)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "b067d381",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2036, 120])\n",
+      "(2036, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "activations_train = lens.get_activations_model(\n",
+    "                model=solver,\n",
+    "                data=valid_data,\n",
+    "                labels=valid_continuous_label,\n",
+    "                activations_keys_prefix=model.__class__.__name__,\n",
+    "                session_id=0,\n",
+    "                layer_type=nn.Conv1d,\n",
+    "            )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "681e86fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processing 250609-unified-reference-run:   0%|          | 0/1 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([8142, 120])\n",
+      "(8142, 3)\n",
+      "torch.Size([2036, 120])\n",
+      "(2036, 3)\n",
+      "coucou (2036,) (2036,) (2036, 32)\n",
+      "coucou (2036,) (2036,) (2036, 32)\n",
+      "coucou (2036,) (2036,) (2036, 32)\n",
+      "coucou (2036,) (2036,) (2036, 32)\n",
+      "coucou (2036,) (2036,) (2036, 32)\n",
+      "coucou (2036,) (2036,) (2036, 32)\n",
+      "coucou (2036,) (2036,) (2036, 32)\n",
+      "coucou (2036,) (2036,) (2036, 32)\n",
+      "coucou (2036,) (2036,) (2036, 32)\n",
+      "coucou (2036,) (2036,) (2036, 32)\n",
+      "coucou (2036,) (2036,) (2036, 32)\n",
+      "coucou (2036,) (2036,) (2036, 32)\n",
+      "coucou (2036,) (2036,) (2036, 32)\n",
+      "coucou (2036,) (2036,) (2036, 32)\n",
+      "coucou (2036,) (2036,) (2036, 32)\n",
+      "coucou (2036,) (2036,) (2036, 32)\n",
+      "coucou (2036,) (2036,) (2036, 32)\n",
+      "coucou (2036,) (2036,) (2036, 32)\n",
+      "coucou (2036,) (2036,) (2036, 32)\n",
+      "coucou (2036,) (2036,) (2036, 32)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processing 250609-unified-reference-run: 100%|██████████| 1/1 [02:16<00:00, 136.69s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "coucou (2036,) (2036,) (2036, 32)\n",
+      "The decoding analysis initialized with the following parameters:\n",
+      "Session ID: 0\n",
+      "Dataset label: None\n",
+      "Layer type: <class 'torch.nn.modules.conv.Conv1d'>\n",
+      "If you want to change the parameters, please re-initialize the class with the new parameters  or if you want to change the output_only parameter, call the compute_metric function with the  output_only parameter set to True or False.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "results_dict = lens.compute_metric(models, decoding_models, output_only=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6e0130f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lens.utils_plot.style()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "024f7ca7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = lens.plot_metric(results_dict, decoding_models, label = 2, label_is_binary = True, plot_error = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6b95ba8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processing 250609-unified-reference-run:   0%|          | 0/1 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([8142, 120])\n",
+      "(8142, 3)\n",
+      "torch.Size([2036, 120])\n",
+      "(2036, 3)\n",
+      "coucou (2036,) (2036,) torch.Size([2036, 32])\n",
+      "coucou (2036,) (2036,) torch.Size([2036, 32])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processing 250609-unified-reference-run: 100%|██████████| 1/1 [00:18<00:00, 18.54s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "coucou (2036,) (2036,) torch.Size([2036, 32])\n",
+      "The decoding analysis initialized with the following parameters:\n",
+      "Session ID: 0\n",
+      "Dataset label: None\n",
+      "Layer type: <class 'torch.nn.modules.conv.Conv1d'>\n",
+      "If you want to change the parameters, please re-initialize the class with the new parameters  or if you want to change the output_only parameter, call the compute_metric function with the  output_only parameter set to True or False.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "results_dict = lens.compute_metric(models, decoding_models, output_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "00f17a52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = lens.plot_metric(results_dict, decoding_models, label = 2, label_is_binary = True, plot_error = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c82bde56",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([8142, 120])\n",
+      "(8142, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "activations_dict = lens.get_activations(\n",
+    "    models=models,\n",
+    "    data=train_data,\n",
+    "    labels=train_continuous_label,\n",
+    "    session_id=0,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "f548d3c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x1400 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = lens.plot_activations(\n",
+    "    activations_dict,\n",
+    "    plot_title=\"Multi Trained\",\n",
+    "    figsize=(7, 14),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "edae03b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'list' object has no attribute 'shape'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[28], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m fig \u001b[38;5;241m=\u001b[39m \u001b[43mlens\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_embeddings\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[43mactivations_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_continuous_label\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdataset_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdataset_label\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmarkersize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43midx_order\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m12\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m31\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m8\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/workspace/CEBRA-lens/cebra_lens/utils_plot.py:961\u001b[0m, in \u001b[0;36mplot_embeddings\u001b[0;34m(data, labels, group_name, dataset_label, sample_plot, ax, **kwargs)\u001b[0m\n\u001b[1;32m    959\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m group_name, models \u001b[38;5;129;01min\u001b[39;00m data_dict\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m    960\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, embs \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(models):\n\u001b[0;32m--> 961\u001b[0m         fig \u001b[38;5;241m=\u001b[39m \u001b[43m_EmbeddingPlot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    962\u001b[0m \u001b[43m            \u001b[49m\u001b[43membeddings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43membs\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    963\u001b[0m \u001b[43m            \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    964\u001b[0m \u001b[43m            \u001b[49m\u001b[43mdataset_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdataset_label\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    965\u001b[0m \u001b[43m            \u001b[49m\u001b[43msample_plot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_plot\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    966\u001b[0m \u001b[43m            \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    967\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mplot_embedding(group_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mgroup_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m instance \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[0;32m/workspace/CEBRA-lens/cebra_lens/utils_plot.py:648\u001b[0m, in \u001b[0;36m_EmbeddingPlot.__init__\u001b[0;34m(self, embeddings, labels, dataset_label, axis, sample_plot, comparison_groups)\u001b[0m\n\u001b[1;32m    646\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membeddings \u001b[38;5;241m=\u001b[39m embeddings[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    647\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sample_plot \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m sample_plot \u001b[38;5;241m>\u001b[39m embeddings[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]:\n\u001b[0;32m--> 648\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msample_plot \u001b[38;5;241m=\u001b[39m \u001b[43membeddings\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m    649\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    650\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msample_plot \u001b[38;5;241m=\u001b[39m sample_plot\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'shape'"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = lens.plot_embeddings(\n",
+    "    activations_dict,\n",
+    "    labels=train_continuous_label[0][:,0],\n",
+    "    dataset_label=dataset_label,\n",
+    "    markersize=5,\n",
+    "    idx_order=(12, 31, 8))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "171397d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model1 = activations_dict[\"250609-unified-reference-run\"][0]\n",
+    "model2 = activations_dict[\"250609-unified-reference-run\"][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "9a6788a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = lens.compare_embeddings_layers(\n",
+    "    embeddings_1=model1, \n",
+    "    embeddings_2=model2,\n",
+    "    labels= train_continuous_label[0][:,0],\n",
+    "    comparison_groups=(\"CEBRA embeddings\", [\"Single\", \"Multi\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "bd3f0269",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_samples_tSNE = 2000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "114d1407",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tsne_model = lens.Tsne(num_samples_tSNE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "82555ab0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processing 250609-unified-reference-run:   0%|          | 0/1 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processing 250609-unified-reference-run: 100%|██████████| 1/1 [00:30<00:00, 30.31s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Metric class: Tsne\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "tSNE_dict = lens.compute_metric(activations_dict, tsne_model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0140a60",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = lens.plot_metric(tSNE_dict, tsne_model, \n",
+    "                       labels=train_continuous_label[0][:,0], alpha=1, markersize=2, sample_plot = 900)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8b6b74cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comparisons = [\n",
+    "        (\"250609-unified-reference-run\", \"250609-unified-reference-run\"),\n",
+    "    ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "397a9fd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/1 [00:00<?, ?it/s]\n",
+      "  0%|          | 0/1 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1/1 [00:13<00:00, 13.73s/it]\n",
+      "100%|██████████| 1/1 [00:13<00:00, 13.73s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "cka_multi = lens.CKA(comparisons)\n",
+    "cka_matrices = lens.compute_metric(activations_dict, cka_multi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0291d64",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = lens.plot_metric(cka_matrices, cka_multi, annot=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2822f339",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rdm_single = lens.RDM(\n",
+    "    data=train_data,\n",
+    "    label=train_continuous_label[0][:, 0],\n",
+    "    is_discrete_labels = True,\n",
+    "    metric=\"cosine\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd834912",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processing 250609-unified-reference-run:   0%|          | 0/1 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processing 250609-unified-reference-run: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RDM class initialized with the following parameters:\n",
+      "The dataset label is not specified,\n",
+      " this label has been noted DISCRETE = True.\n",
+      "If this is not the desired behavior, please check the parameters passed to the RDM class.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "multi_rdm_corr = lens.compute_metric(activations_dict, rdm_single, bool_oracle=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a125ce3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cebra",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tests/test_activations.py b/tests/test_activations.py
index 7f3ff15..8e1e712 100644
--- a/tests/test_activations.py
+++ b/tests/test_activations.py
@@ -1,10 +1,12 @@
 from collections import namedtuple
 from unittest.mock import MagicMock
 
+import cebra
 import numpy as np
 import pytest
 import torch
 
+from cebra_lens import activations
 from cebra_lens.activations import (_cut_array, get_activations_model,
                                     get_cut_indices)
 
@@ -38,13 +40,9 @@ def test_get_cut_indices():
 
 
 def make_mock_cebra_model():
-    model = MagicMock()
-    model.net = [torch.nn.Conv1d(1, 1, 3), torch.nn.ReLU()]
-    model.net[0].kernel_size = [3]
-    model.solver_name_ = "single-session"
-    model.model_ = model
+    model = cebra.CEBRA(max_iterations=1, device="cpu")
+    model.fit(torch.rand((5, 10)), torch.rand((5, 1)))
     model.pad_before_transform = False
-    model.transform.return_value = None
     return model
 
 
@@ -52,9 +50,6 @@ def test_get_activations_model_basic(monkeypatch):
     model = make_mock_cebra_model()
     data = torch.rand((5, 10))
 
-    # Patch _attach_hooks to avoid touching real layers
-    from cebra_lens import activations
-
     monkeypatch.setattr(
         activations,
         "_attach_hooks",
@@ -62,7 +57,10 @@ def test_get_activations_model_basic(monkeypatch):
             "test_layer": np.ones((5, 3))
         }, [], [3]),
     )
-    result = get_activations_model(model, data, layer_type=torch.nn.Conv1d)
+    print(type(model))
+    result = get_activations_model(model=model,
+                                   data=data,
+                                   layer_type=torch.nn.Conv1d)
     assert isinstance(result, dict)
     assert "test_layer" in result
     assert result["test_layer"].shape == (5, 3)
diff --git a/tests/test_decoding.py b/tests/test_decoding.py
index f47b067..12dc942 100644
--- a/tests/test_decoding.py
+++ b/tests/test_decoding.py
@@ -1,118 +1,139 @@
-from unittest.mock import MagicMock, patch
+from unittest.mock import patch
+from cebra_lens.quantification.decoder import decoding, Decoding, DecodeResult
 
+import cebra
 import numpy as np
 import pytest
 import torch
 
 import cebra_lens
+    
+class DummyKNNDecoder:
+    def fit(self, X, y):
+        return self
+
+    def predict(self, X):
+        return np.random.rand(len(X))
+
+    def get_params(self, deep=True):
+        return {}
+
+    def set_params(self, **params):
+        return self
+
+
+
+def make_mock_cebra_model(input_dim=10, label_dim=1):
+    model = cebra.CEBRA(max_iterations=1, device="cpu")
+    model.fit(torch.rand((5, input_dim)), torch.rand((5, label_dim)))
+    return model
 
 
 @pytest.fixture
 def embeddings_labels():
     emb_train = torch.randn((1000, 32))
     emb_test = torch.randn((200, 32))
-    label_train = np.random.rand(1000, 2)
-    label_test = np.random.rand(200, 2)
-    return emb_train, emb_test, label_train, label_test
+    train_label = np.random.rand(1000, 2)
+    test_label = np.random.rand(200, 2)
+    return emb_train, emb_test, train_label, test_label
 
 
 def test_decoding_function(embeddings_labels):
-    emb_train, emb_test, label_train, label_test = embeddings_labels
-
-    with patch("cebra.KNNDecoder") as mock_knn:
-        mock_model = MagicMock()
-        # Return prediction with correct shape each time
-        mock_model.predict.side_effect = lambda x: np.random.rand(len(x))
-        mock_knn.return_value = mock_model
-
-        score, medians, r2s = cebra_lens.quantification.decoder.decoding(
-            emb_train, emb_test, label_train, label_test)
-
+    emb_train, emb_test, train_label, test_label = embeddings_labels
+
+    with patch("cebra.KNNDecoder", return_value=DummyKNNDecoder()):
+        # decoding() now returns a DecodeResult namedtuple:
+        dr = decoding(
+            emb_train, emb_test, train_label, test_label)
+        # unpack fields rather than expecting three separate return values
+        score   = dr.overall_score
+        medians = dr.per_label_error
+        r2s     = dr.per_label_score
+        
         assert isinstance(score, float)
-        assert len(medians) == label_train.shape[1]
-        assert len(r2s) == label_train.shape[1]
-
-
-def make_mock_cebra_model():
-    model = MagicMock()
-    model.solver_name_ = "single-session"
-    model.transform.side_effect = lambda x, **kwargs: x.detach().numpy()
-    return model
+        assert len(medians) == train_label.shape[1]
+        assert len(r2s) == train_label.shape[1]
 
 
 def test_decoding_class_output_only_true():
-    model = make_mock_cebra_model()
-    decoding_class = cebra_lens.quantification.decoder.Decoding(
+    model = make_mock_cebra_model(100, 1)
+    decoding_class = Decoding(
         train_data=torch.rand((300, 100)),
         train_label=np.random.rand(300, 1),
         test_data=torch.rand((100, 100)),
         test_label=np.random.rand(100, 1),
         dataset_label=None,
-        output_only=True,
     )
-    results = decoding_class.compute(model)
+    results = decoding_class.compute(model, output_only=True)
     assert isinstance(results, dict)
     assert 0 in results
 
 
 @patch("cebra_lens.activations.get_activations_model")
 def test_decoding_class_output_only_false(mock_get_act):
-    model = make_mock_cebra_model()
+    model = make_mock_cebra_model(1000, 1)
     mock_get_act.side_effect = lambda **kwargs: {
         "layer1": np.random.rand(1000, 1000),
         "layer2": np.random.rand(1000, 1000),
     }
 
-    decoding_class = cebra_lens.quantification.decoder.Decoding(
+    decoding_class = Decoding(
         train_data=torch.rand((1000, 1000)),
         train_label=np.random.rand(1000, 1),
         test_data=torch.rand((1000, 1000)),
         test_label=np.random.rand(1000, 1),
         dataset_label=None,
-        output_only=False,
     )
 
-    results = decoding_class.compute(model)
+    results = decoding_class.compute(model, output_only=False)
     assert isinstance(results, dict)
-    assert len(results) == 1  # only one Conv1d layer in the mock model
+    assert len(results) == 2  # only 2 Conv1d layer in the mock model
 
     decoding_class.layer_type = None
     with pytest.raises(NotImplementedError,
                        match="Padding handling not implemented*"):
-        decoding_class.compute(model)
+        decoding_class.compute(model, output_only=False)
 
     decoding_class.layer_type = torch.nn.Linear
     with pytest.raises(NotImplementedError,
                        match="Padding handling not implemented*"):
-        decoding_class.compute(model)
-
-
-def test_set_output_only():
-    decoding_instance = cebra_lens.quantification.decoder.Decoding(
-        train_data=torch.rand((10, 5)),
-        train_label=np.random.rand(10, 1),
-        test_data=torch.rand((10, 5)),
-        test_label=np.random.rand(10, 1),
-    )
-    decoding_instance.set_output_only(False)
-    assert decoding_instance.output_only is False
+        decoding_class.compute(model, output_only=False)
 
 
 @patch("cebra_lens.utils_plot.plot_decoding")
 @patch("cebra_lens.utils_plot.plot_layer_decoding")
 def test_decoder_plot(mock_layer_plot, mock_decoding_plot):
-    dummy_result = {"modelA": {0: (0.9, [0.1], [0.8])}}
-    dec = cebra_lens.quantification.decoder.Decoding(
+    dec = Decoding(
         train_data=torch.rand((10, 10)),
         train_label=np.random.rand(10, 1),
         test_data=torch.rand((10, 10)),
         test_label=np.random.rand(10, 1),
     )
 
-    dec.output_only = True
+    # single-model, single-layer → should call plot_decoding
+    dummy_result = {
+        "modelA": [
+            {
+                0: DecodeResult(
+                    overall_score=0.9,
+                    per_label_error=[0.1],
+                    per_label_score=[0.8],
+                    label_types=["regression"],
+                )
+            }
+        ]
+    }
     dec.plot(dummy_result, label=0)
     assert mock_decoding_plot.called
 
-    dec.output_only = False
+    # multiple models → plot_layer_decoding
+    dummy_result = {
+        "modelA": [{
+            0: DecodeResult(0.9, [0.1], [0.8], ["regression"])
+        }],
+        "modelB": [{
+            0: DecodeResult(0.85, [0.15], [0.75], ["regression"])
+        }],
+    }
     dec.plot(dummy_result, label=0)
-    assert mock_layer_plot.called
+    assert mock_layer_plot.called
\ No newline at end of file
diff --git a/tests/test_rdm.py b/tests/test_rdm.py
index 58c6be3..a734fa7 100644
--- a/tests/test_rdm.py
+++ b/tests/test_rdm.py
@@ -44,12 +44,17 @@ def test_init_with_label_ind():
     labels = np.array([[1, 2], [3, 4]])
     data = torch.tensor(np.random.rand(2, 5), dtype=torch.float32)
     rdm = cebra_lens.quantification.rdm_metric.RDM(data=data,
-                                                   label=labels,
-                                                   label_ind=0,
+                                                   label=labels[:, 0],
                                                    dataset_label=None,
                                                    is_discrete_labels=True)
     assert rdm.label.tolist() == [1, 3]
 
+    with pytest.raises(ValueError):
+        cebra_lens.quantification.rdm_metric.RDM(data=data,
+                                                 label=labels,
+                                                 dataset_label=None,
+                                                 is_discrete_labels=True)
+
 
 def test_create_oracle_rdm_custom():
     rdm = cebra_lens.quantification.rdm_metric.RDM(
@@ -71,8 +76,7 @@ def test_compute_per_layer_and_bool_oracle():
     )
     rdm.idxs = np.array([[i] for i in range(10000)])
     dummy_layer = np.random.rand(10000, 5)
-    rdm.bool_oracle = True
-    _, corr = rdm._compute_per_layer(dummy_layer)
+    _, corr = rdm._compute_per_layer(dummy_layer, bool_oracle=True)
     assert isinstance(corr, float)
 
 
@@ -96,35 +100,20 @@ def test_rdm_plot(mock_all, mock_corr):
     dummy_rdm = cebra_lens.quantification.rdm_metric.RDM(
         data=torch.rand((10000, 5)),
         label=torch.randint(0, 5, (10000, )),
-        is_discrete_labels=False)
-    dummy_rdm.bool_oracle = True
-    dummy_rdm.plot({"group": [np.random.rand(5, 5)]})
+        is_discrete_labels=False,
+    )
+    dummy_rdm.plot({"group": [np.random.rand(5, 5)]}, bool_oracle=True)
     assert mock_corr.called
 
-    dummy_rdm.bool_oracle = False
     dummy_rdm.num_bins = 2
     dummy_rdm.is_discrete_labels = True
-    dummy_rdm.plot({"group": [np.random.rand(5, 5)]})
+    dummy_rdm.plot({"group": [np.random.rand(5, 5)]}, bool_oracle=False)
     assert mock_all.called
 
     dummy_rdm.plot({"group": [np.random.rand(5, 5)]}, titles=None)
     dummy_rdm.plot({})
 
 
-def test_init_raises_keyerror_for_multilabel_without_label_ind():
-    # label is 2D, dataset_label is not HPC/visual, label_ind not provided
-    labels = np.array([[1, 2], [3, 4]])
-    data = torch.tensor(np.random.rand(2, 5), dtype=torch.float32)
-    with pytest.raises(KeyError):
-        cebra_lens.quantification.rdm_metric.RDM(
-            data=data,
-            label=labels,
-            is_discrete_labels=True,
-            dataset_label=None,
-            label_ind=None,
-        )
-
-
 def test_define_indices_raises_valueerror_for_invalid_dataset_label():
     data = torch.tensor(np.random.rand(10, 5), dtype=torch.float32)
     labels = torch.tensor(np.random.randint(0, 5, size=10), dtype=torch.int64)
@@ -146,5 +135,4 @@ def test_compute_per_layer_transposes_if_needed():
     rdm.idxs = np.array([[i] for i in range(10)])
     # Provide activation with shape (features, samples)
     dummy_layer = np.random.rand(5, 10)
-    rdm.bool_oracle = False
     rdm._compute_per_layer(dummy_layer)
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 7054517..2fd32c4 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,36 +1,28 @@
 from unittest.mock import MagicMock, patch
 
+import cebra
 import numpy as np
 import pytest
+import torch
 
-from cebra_lens import compute_metric, extract_label, model_loader, plot_metric
+from cebra_lens import compute_metric, model_loader, plot_metric
+from cebra_lens.quantification.base import _BaseMetric
 from cebra_lens.quantification.cka_metric import CKA
 from cebra_lens.quantification.decoder import Decoding
 from cebra_lens.quantification.rdm_metric import RDM
 
 
-def test_extract_label_single_dim():
-    labels = np.array([1, 2, 3, 2])
-    result = extract_label(labels, 0)
-    assert result.tolist() == [1, 2, 3, 2]
-
-
-def test_extract_label_multi_dim():
-    labels = np.array([[1, 0], [2, 1], [3, 2]])
-    result = extract_label(labels, 1)
-    assert result.tolist() == [0, 1, 2]
-
-
-def test_extract_label_out_of_range():
-    labels = np.array([[1, 0], [2, 1], [3, 2]])
-    with pytest.raises(ValueError):
-        extract_label(labels, 3)
+def make_mock_cebra_model(input_dim=10, label_dim=1):
+    model = cebra.CEBRA(max_iterations=1, device="cpu")
+    model.fit(torch.rand((5, input_dim)), torch.rand((5, label_dim)))
+    #model.pad_before_transform = False
+    return model
 
 
 def test_compute_metric_with_mock_metric_class():
     dummy_data = {"group1": [np.array([1, 2]), np.array([3, 4])]}
 
-    class DummyMetric:
+    class DummyMetric(_BaseMetric):
 
         def compute(self, sample):
             return sample.sum()
@@ -40,12 +32,18 @@ def compute(self, sample):
 
 
 def test_compute_metric_with_decoding():
-    dummy_data = {"group": [np.ones((5, )), np.ones((5, ))]}
-
-    mock_metric = MagicMock(spec=Decoding)
-    mock_metric.compute.side_effect = lambda x: x.sum()
-    result = compute_metric(dummy_data, mock_metric, output_only=True)
-    assert result["group"].tolist() == [5.0, 5.0]
+    dummy_model = {"group1": [make_mock_cebra_model(100, 1)]}
+
+    dec_metric = Decoding(
+        train_data=torch.rand((300, 100)),
+        train_label=np.random.rand(300, 1),
+        test_data=torch.rand((100, 100)),
+        test_label=np.random.rand(100, 1),
+        dataset_label=None,
+    )
+    result = compute_metric(model_data=dummy_model,
+                            metric_class=dec_metric,
+                            output_only=False)
 
 
 def test_plot_metric_single_model(monkeypatch):