📖 Fix CalibrationError docstring

torch_uncertainty/metrics/classification/calibration_error.py

@@ -237,39 +237,39 @@ def __new__(  # type: ignore[misc]
 
         .. math::
 
-        \text{ECE} = \sum_{i=1}^N b_i \lvert p_i - c_i \rvert
+            \text{ECE} = \sum_{i=1}^N b_i \lvert p_i - c_i \rvert
 
         **Maximum Calibration Error (MCE):**
 
         .. math::
 
-        \text{MCE} = \max_{i} \lvert p_i - c_i \rvert
+            \text{MCE} = \max_{i} \lvert p_i - c_i \rvert
 
         **Root Mean Square Calibration Error (RMSCE):**
 
         .. math::
 
-        \text{RMSCE} = \sqrt{\sum_{i=1}^N b_i (p_i - c_i)^2}
+            \text{RMSCE} = \sqrt{\sum_{i=1}^N b_i (p_i - c_i)^2}
 
         Here:
-        - :math:`p_i` is the accuracy of bin :math:`i` (fraction of correct predictions).
-        - :math:`c_i` is the mean predicted confidence in bin :math:`i`.
-        - :math:`b_i` is the fraction of total samples falling into bin :math:`i`.
+            - :math:`p_i` is the accuracy of bin :math:`i` (fraction of correct predictions).
+            - :math:`c_i` is the mean predicted confidence in bin :math:`i`.
+            - :math:`b_i` is the fraction of total samples falling into bin :math:`i`.
 
         Bins are constructed either uniformly in the range :math:`[0, 1]` or adaptively
         (if `adaptive=True`).
 
         Args:
-        task (str): Specifies the task type, either ``"binary"`` or ``"multiclass"``.
-        adaptive (bool, optional): Whether to use adaptive binning. Defaults to ``False``.
-        num_bins (int, optional): Number of bins to divide the probability space. Defaults to ``10``.
-        norm (str, optional): Specifies the type of norm to use: ``"l1"``, ``"l2"``, or ``"max"``.
-            Defaults to ``"l1"``.
-        num_classes (int, optional): Number of classes for ``"multiclass"`` tasks. Required when task
-            is ``"multiclass"``.
-        ignore_index (int, optional): Index to ignore during calculations. Defaults to ``None``.
-        validate_args (bool, optional): Whether to validate input arguments. Defaults to ``True``.
-        **kwargs: Additional keyword arguments for the metric.
+            task (str): Specifies the task type, either ``"binary"`` or ``"multiclass"``.
+            adaptive (bool, optional): Whether to use adaptive binning. Defaults to ``False``.
+            num_bins (int, optional): Number of bins to divide the probability space. Defaults to ``10``.
+            norm (str, optional): Specifies the type of norm to use: ``"l1"``, ``"l2"``, or ``"max"``.
+                Defaults to ``"l1"``.
+            num_classes (int, optional): Number of classes for ``"multiclass"`` tasks. Required when task
+                is ``"multiclass"``.
+            ignore_index (int, optional): Index to ignore during calculations. Defaults to ``None``.
+            validate_args (bool, optional): Whether to validate input arguments. Defaults to ``True``.
+            **kwargs: Additional keyword arguments for the metric.
 
         Example:
 
@@ -292,20 +292,20 @@ def __new__(  # type: ignore[misc]
             # Output: Calibration Error: 0.199
 
         Note:
-        - Bins are either uniformly distributed in :math:`[0, 1]` or adaptively sized
-          (if `adaptive=True`).
+            Bins are either uniformly distributed in :math:`[0, 1]` or adaptively sized
+            (if `adaptive=True`).
 
         Warning:
-        If `task="multiclass"`, `num_classes` must be an integer; otherwise, a :class:`TypeError`
-        is raised.
+            If `task="multiclass"`, `num_classes` must be an integer; otherwise, a :class:`TypeError`
+            is raised.
 
         References:
-        [1] `Naeini et al. Obtaining well calibrated probabilities using Bayesian binning. In AAAI, 2015
-        <https://ojs.aaai.org/index.php/AAAI/article/view/9602>`_.
+            [1] `Naeini et al. Obtaining well calibrated probabilities using Bayesian binning. In AAAI, 2015
+            <https://ojs.aaai.org/index.php/AAAI/article/view/9602>`_.
 
         .. seealso::
-        - See `CalibrationError <https://torchmetrics.readthedocs.io/en/stable/classification/calibration_error.html>`_
-          for details. Our version of the metric is a wrapper around the original metric providing a plotting functionality.
+            See `CalibrationError <https://torchmetrics.readthedocs.io/en/stable/classification/calibration_error.html>`_
+            for details. Our version of the metric is a wrapper around the original metric providing a plotting functionality.
         """
         if kwargs.get("n_bins") is not None:
             raise ValueError("`n_bins` does not exist, use `num_bins`.")