diff --git a/tests/unit_tests/test_charge.py b/tests/unit_tests/test_charge.py
index 4c5f629..e703c55 100644
--- a/tests/unit_tests/test_charge.py
+++ b/tests/unit_tests/test_charge.py
@@ -1,5 +1,4 @@
 import unittest
-from typing import List, Optional, Union
 
 import numpy as np
 
@@ -18,38 +17,6 @@ def test_indices_to_one_hot_with_classes(self):
 
     # Inside charge.py
 
-
-def indices_to_one_hot(labels: Union[int, List[int], np.ndarray], classes: Optional[int] = None) -> np.ndarray:
-    """
-    Convert a single or a list of labels to one-hot encoding.
-
-    :param labels: The labels to be one-hot encoding. Must be one-based.
-    :param classes: The number of classes, i.e. the length of the encoding. If omitted, set to the max label + 1.
-    :raises TypeError: If the type of labels is not understood
-    :raises ValueError: If the highest label in labels is larger or equal to the number of classes.
-    :return: np.ndarray with the one-hot encoded labels.
-    """
-    if isinstance(labels, int):
-        labels = np.array([labels])
-    elif isinstance(labels, (list, np.ndarray)):
-        labels = np.array(labels)
-    else:
-        raise TypeError(
-            f"Type of labels not understood. Only int, List[int] and np.ndarray are supported. Given: {type(labels)}."
-        )
-
-    if classes is None:
-        classes = np.max(labels) + 1
-    elif classes < np.max(labels) + 1:
-        raise ValueError(
-            f"Number of classes must be greater than or equal to the maximum label in labels. "
-            f"Given classes: {classes}, maximum label: {np.max(labels)}."
-        )
-
-    one_hot = np.zeros((labels.size, classes), dtype=int)
-    one_hot[np.arange(labels.size), labels - 1] = 1
-    return one_hot
-
     def test_indices_to_one_hot_with_int_and_class(self):
         """Test indices_to_one_hot with a single integer and given number of classes."""
         labels = 1