Minor formatting

cebra/integrations/sklearn/decoder.py

@@ -23,32 +23,32 @@
 
 
 def _is_integer(y: Union[npt.NDArray, torch.Tensor]) -> bool:
-    """Check if the values in `y` are :py:class:`int`.
+    """Check if the values in ``y`` are :py:class:`int`.
 
     Args:
         y: An array, either as a :py:func:`numpy.array` or a :py:class:`torch.Tensor`.
     
     Returns:
-        `True` if `y` contains :py:class:`int`.
+        ``True`` if ``y`` contains :py:class:`int`.
     """
     return (isinstance(y, np.ndarray) and np.issubdtype(y.dtype, np.integer)
            ) or (isinstance(y, torch.Tensor) and
                  (not torch.is_floating_point(y) and not torch.is_complex(y)))
 
 
 def _is_floating(y: Union[npt.NDArray, torch.Tensor]) -> bool:
-    """Check if the values in `y` are :py:class:`int`.
+    """Check if the values in ``y`` are :py:class:`int`.
     
     Note: 
-        There is no `torch` method to check that the `dtype` of a :py:class:`torch.Tensor`
+        There is no ``torch`` method to check that the ``dtype`` of a :py:class:`torch.Tensor`
         is a :py:class:`float`, consequently, we check that it is not :py:class:`int` nor
         :py:class:`complex`.
 
     Args:
         y: An array, either as a :py:func:`numpy.array` or a :py:class:`torch.Tensor`.
     
     Returns:
-        `True` if `y` contains :py:class:`float`.
+        ``True`` if ``y`` contains :py:class:`float`.
     """
 
     return (isinstance(y, np.ndarray) and

tests/test_sklearn_decoder.py

@@ -107,4 +107,4 @@ def test_dtype_checker():
     assert cebra_sklearn_decoder._is_floating(torch.Tensor([4.5]))
     assert cebra_sklearn_decoder._is_integer(torch.LongTensor([4]))
     assert cebra_sklearn_decoder._is_floating(np.array([4.5]))
-    assert cebra_sklearn_decoder._is_integer(np.array([4]))
+    assert cebra_sklearn_decoder._is_integer(np.array([4]))