From 36733df8bde194e440a44455951fc1c1e436788a Mon Sep 17 00:00:00 2001
From: Jirka B <j.borovec+github@gmail.com>
Date: Thu, 31 Oct 2024 18:48:06 +0100
Subject: [PATCH] docs: specify directives

---
 .github/CONTRIBUTING.md                        |  2 +-
 docs/source/pages/implement.rst                | 10 +++++-----
 docs/source/pages/lightning.rst                |  8 ++++----
 docs/source/pages/overview.rst                 |  8 ++++----
 src/torchmetrics/audio/dnsmos.py               |  6 ++++--
 src/torchmetrics/audio/nisqa.py                |  6 ++++--
 src/torchmetrics/audio/pesq.py                 |  6 ++++--
 src/torchmetrics/audio/srmr.py                 |  5 +++--
 src/torchmetrics/audio/stoi.py                 |  3 ++-
 .../classification/calibration_error.py        |  2 +-
 src/torchmetrics/classification/cohen_kappa.py |  4 ++--
 src/torchmetrics/classification/dice.py        |  2 +-
 src/torchmetrics/classification/hinge.py       |  4 ++--
 src/torchmetrics/classification/jaccard.py     |  6 +++---
 .../classification/matthews_corrcoef.py        |  6 +++---
 .../classification/precision_fixed_recall.py   |  6 +++---
 .../classification/precision_recall_curve.py   |  6 +++---
 src/torchmetrics/classification/ranking.py     |  6 +++---
 .../classification/recall_fixed_precision.py   |  6 +++---
 src/torchmetrics/classification/roc.py         | 18 +++++++++---------
 src/torchmetrics/collections.py                |  4 ++--
 src/torchmetrics/detection/_mean_ap.py         |  8 ++++----
 src/torchmetrics/detection/mean_ap.py          |  6 +++---
 src/torchmetrics/functional/audio/dnsmos.py    |  3 ++-
 src/torchmetrics/functional/audio/nisqa.py     |  3 ++-
 src/torchmetrics/functional/audio/pesq.py      |  3 ++-
 src/torchmetrics/functional/audio/srmr.py      |  5 +++--
 src/torchmetrics/functional/audio/stoi.py      |  3 ++-
 .../functional/classification/dice.py          |  6 ++++--
 src/torchmetrics/functional/image/gradients.py |  3 ++-
 src/torchmetrics/functional/image/psnr.py      |  4 ++--
 .../functional/multimodal/clip_iqa.py          |  3 ++-
 .../functional/multimodal/clip_score.py        |  3 ++-
 .../functional/regression/log_mse.py           |  4 ++--
 src/torchmetrics/image/fid.py                  |  5 ++---
 src/torchmetrics/image/inception.py            |  3 ++-
 src/torchmetrics/image/kid.py                  |  3 ++-
 src/torchmetrics/image/lpip.py                 |  6 ++----
 src/torchmetrics/image/mifid.py                |  6 ++++--
 .../image/perceptual_path_length.py            |  3 ++-
 src/torchmetrics/image/psnrb.py                |  2 +-
 src/torchmetrics/metric.py                     |  4 ++--
 src/torchmetrics/multimodal/clip_iqa.py        |  3 ++-
 src/torchmetrics/multimodal/clip_score.py      |  3 ++-
 src/torchmetrics/regression/kl_divergence.py   |  4 ++--
 src/torchmetrics/regression/log_mse.py         |  4 ++--
 src/torchmetrics/retrieval/base.py             |  7 ++++---
 .../retrieval/precision_recall_curve.py        |  5 +++--
 src/torchmetrics/wrappers/multitask.py         |  4 ++--
 49 files changed, 132 insertions(+), 108 deletions(-)

diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md
index 7b7211b9d61..b923d6ea262 100644
--- a/.github/CONTRIBUTING.md
+++ b/.github/CONTRIBUTING.md
@@ -103,7 +103,7 @@ def my_func(param_a: int, param_b: Optional[float] = None) -> str:
         >>> my_func(1, 2)
         3
 
-    .. note:: If you want to add something.
+    .. hint:: If you want to add something.
     """
     p = param_b if param_b else 0
     return str(param_a + p)
diff --git a/docs/source/pages/implement.rst b/docs/source/pages/implement.rst
index 1620ce29cd9..5ab044e1be1 100644
--- a/docs/source/pages/implement.rst
+++ b/docs/source/pages/implement.rst
@@ -257,7 +257,7 @@ and tests gets formatted in the following way:
   3. ``new_metric(...)``: essentially wraps the ``_update`` and ``_compute`` private functions into one public function that
      makes up the functional interface for the metric.
 
-  .. note::
+  .. hint::
      The `functional mean squared error <https://github.com/Lightning-AI/torchmetrics/blob/master/src/torchmetrics/functional/regression/mse.py>`_
      metric is a is a great example of how to divide the logic.
 
@@ -270,9 +270,9 @@ and tests gets formatted in the following way:
      ``_new_metric_compute(...)`` function in its ``compute``. No logic should really be implemented in the module interface.
      We do this to not have duplicate code to maintain.
 
-   .. note::
-     The module `MeanSquaredError <https://github.com/Lightning-AI/torchmetrics/blob/master/src/torchmetrics/regression/mse.py>`_
-     metric that corresponds to the above functional example showcases these steps.
+  .. note::
+    The module `MeanSquaredError <https://github.com/Lightning-AI/torchmetrics/blob/master/src/torchmetrics/regression/mse.py>`_
+    metric that corresponds to the above functional example showcases these steps.
 
 4. Remember to add binding to the different relevant ``__init__`` files.
 
@@ -291,7 +291,7 @@ and tests gets formatted in the following way:
      so that different combinations of inputs and parameters get tested.
   5. (optional) If your metric raises any exception, please add tests that showcase this.
 
-  .. note::
+  .. hint::
     The `test file for MSE <https://github.com/Lightning-AI/torchmetrics/blob/master/tests/unittests/regression/test_mean_error.py>`_
     metric shows how to implement such tests.
 
diff --git a/docs/source/pages/lightning.rst b/docs/source/pages/lightning.rst
index a96196396b8..0fd27a4d474 100644
--- a/docs/source/pages/lightning.rst
+++ b/docs/source/pages/lightning.rst
@@ -13,7 +13,7 @@ TorchMetrics in PyTorch Lightning
 TorchMetrics was originally created as part of `PyTorch Lightning <https://github.com/Lightning-AI/pytorch-lightning>`_, a powerful deep learning research
 framework designed for scaling models without boilerplate.
 
-.. note::
+.. caution::
 
     TorchMetrics always offers compatibility with the last 2 major PyTorch Lightning versions, but we recommend always
     keeping both frameworks up-to-date for the best experience.
@@ -69,9 +69,9 @@ LightningModule `self.log <https://lightning.ai/docs/pytorch/stable/extensions/l
 method, Lightning will log the metric based on ``on_step`` and ``on_epoch`` flags present in ``self.log(...)``. If
 ``on_epoch`` is True, the logger automatically logs the end of epoch metric value by calling ``.compute()``.
 
-.. note::
+.. caution::
 
-    ``sync_dist``, ``sync_dist_group`` and ``reduce_fx`` flags from ``self.log(...)`` don't affect the metric logging
+    The ``sync_dist``, ``sync_dist_group`` and ``reduce_fx`` flags from ``self.log(...)`` don't affect the metric logging
     in any manner. The metric class contains its own distributed synchronization logic.
 
     This, however is only true for metrics that inherit the base class ``Metric``,
@@ -136,7 +136,7 @@ Note that logging metrics this way will require you to manually reset the metric
 In general, we recommend logging the metric object to make sure that metrics are correctly computed and reset.
 Additionally, we highly recommend that the two ways of logging are not mixed as it can lead to wrong results.
 
-.. note::
+.. hint::
 
     When using any Modular metric, calling ``self.metric(...)`` or ``self.metric.forward(...)`` serves the dual purpose
     of calling ``self.metric.update()`` on its input and simultaneously returning the metric value over the provided
diff --git a/docs/source/pages/overview.rst b/docs/source/pages/overview.rst
index 34d0dcbd6fc..933d4254cfe 100644
--- a/docs/source/pages/overview.rst
+++ b/docs/source/pages/overview.rst
@@ -61,13 +61,13 @@ This metrics API is independent of PyTorch Lightning. Metrics can directly be us
     It is highly recommended to re-initialize the metric per mode as
     shown in the examples above.
 
-.. note::
+.. caution::
 
     Metric states are **not** added to the models ``state_dict`` by default.
     To change this, after initializing the metric, the method ``.persistent(mode)`` can
     be used to enable (``mode=True``) or disable (``mode=False``) this behaviour.
 
-.. note::
+.. important::
 
     Due to specialized logic around metric states, we in general do **not** recommend
     that metrics are initialized inside other metrics (nested metrics), as this can lead
@@ -306,7 +306,7 @@ This pattern is implemented for the following operators (with ``a`` being metric
 * Positive Value (``pos(a)``)
 * Indexing (``a[0]``)
 
-.. note::
+.. caution::
 
     Some of these operations are only fully supported from Pytorch v1.4 and onwards, explicitly we found:
     ``add``, ``mul``, ``rmatmul``, ``rsub``, ``rmod``
@@ -381,7 +381,7 @@ inside your LightningModule. In most cases we just have to replace ``self.log``
             # remember to reset metrics at the end of the epoch
             self.valid_metrics.reset()
 
-.. note::
+.. important::
 
     `MetricCollection` as default assumes that all the metrics in the collection
     have the same call signature. If this is not the case, input that should be
diff --git a/src/torchmetrics/audio/dnsmos.py b/src/torchmetrics/audio/dnsmos.py
index 74d035a7fd4..406b817eb05 100644
--- a/src/torchmetrics/audio/dnsmos.py
+++ b/src/torchmetrics/audio/dnsmos.py
@@ -54,11 +54,13 @@ class DeepNoiseSuppressionMeanOpinionScore(Metric):
     - ``dnsmos`` (:class:`~torch.Tensor`): float tensor of DNSMOS values reduced across the batch
         with shape ``(...,4)`` indicating [p808_mos, mos_sig, mos_bak, mos_ovr] in the last dim.
 
-    .. note:: using this metric requires you to have ``librosa``, ``onnxruntime`` and ``requests`` installed.
+    .. hint::
+        Using this metric requires you to have ``librosa``, ``onnxruntime`` and ``requests`` installed.
         Install as ``pip install torchmetrics['audio']`` or alternatively `pip install librosa onnxruntime-gpu requests`
         (if you do not have GPU enabled machine install `onnxruntime` instead of `onnxruntime-gpu`)
 
-    .. note:: the ``forward`` and ``compute`` methods in this class return a reduced DNSMOS value
+    .. caution::
+        The ``forward`` and ``compute`` methods in this class return a reduced DNSMOS value
         for a batch. To obtain the DNSMOS value for each sample, you may use the functional counterpart in
         :func:`~torchmetrics.functional.audio.dnsmos.deep_noise_suppression_mean_opinion_score`.
 
diff --git a/src/torchmetrics/audio/nisqa.py b/src/torchmetrics/audio/nisqa.py
index d722b192bb5..c079d903101 100644
--- a/src/torchmetrics/audio/nisqa.py
+++ b/src/torchmetrics/audio/nisqa.py
@@ -43,10 +43,12 @@ class NonIntrusiveSpeechQualityAssessment(Metric):
     - ``nisqa`` (:class:`~torch.Tensor`): float tensor reduced across the batch with shape ``(5,)`` corresponding to
       overall MOS, noisiness, discontinuity, coloration and loudness in that order
 
-    .. note:: Using this metric requires you to have ``librosa`` and ``requests`` installed. Install as
+    .. hint::
+        Using this metric requires you to have ``librosa`` and ``requests`` installed. Install as
         ``pip install librosa requests``.
 
-    .. note:: The ``forward`` and ``compute`` methods in this class return values reduced across the batch. To obtain
+    .. caution::
+        The ``forward`` and ``compute`` methods in this class return values reduced across the batch. To obtain
         values for each sample, you may use the functional counterpart
         :func:`~torchmetrics.functional.audio.nisqa.non_intrusive_speech_quality_assessment`.
 
diff --git a/src/torchmetrics/audio/pesq.py b/src/torchmetrics/audio/pesq.py
index dfcf623aa25..ee6a1751359 100644
--- a/src/torchmetrics/audio/pesq.py
+++ b/src/torchmetrics/audio/pesq.py
@@ -45,12 +45,14 @@ class PerceptualEvaluationSpeechQuality(Metric):
 
     - ``pesq`` (:class:`~torch.Tensor`): float tensor of PESQ value reduced across the batch
 
-    .. note:: using this metrics requires you to have ``pesq`` install. Either install as ``pip install
+    .. hint::
+        Using this metrics requires you to have ``pesq`` install. Either install as ``pip install
         torchmetrics[audio]`` or ``pip install pesq``. ``pesq`` will compile with your currently
         installed version of numpy, meaning that if you upgrade numpy at some point in the future you will
         most likely have to reinstall ``pesq``.
 
-    .. note:: the ``forward`` and ``compute`` methods in this class return a single (reduced) PESQ value
+    .. caution::
+        The ``forward`` and ``compute`` methods in this class return a single (reduced) PESQ value
         for a batch. To obtain a PESQ value for each sample, you may use the functional counterpart in
         :func:`~torchmetrics.functional.audio.pesq.perceptual_evaluation_speech_quality`.
 
diff --git a/src/torchmetrics/audio/srmr.py b/src/torchmetrics/audio/srmr.py
index 0ced6d6f24f..6c910738880 100644
--- a/src/torchmetrics/audio/srmr.py
+++ b/src/torchmetrics/audio/srmr.py
@@ -48,11 +48,12 @@ class SpeechReverberationModulationEnergyRatio(Metric):
 
     - ``srmr`` (:class:`~torch.Tensor`): float scaler tensor
 
-    .. note:: using this metrics requires you to have ``gammatone`` and ``torchaudio`` installed.
+    .. hint::
+        Using this metrics requires you to have ``gammatone`` and ``torchaudio`` installed.
         Either install as ``pip install torchmetrics[audio]`` or ``pip install torchaudio``
         and ``pip install git+https://github.com/detly/gammatone``.
 
-    .. note::
+    .. attention::
         This implementation is experimental, and might not be consistent with the matlab
         implementation `SRMRToolbox`_, especially the fast implementation.
         The slow versions, a) fast=False, norm=False, max_cf=128, b) fast=False, norm=True, max_cf=30, have
diff --git a/src/torchmetrics/audio/stoi.py b/src/torchmetrics/audio/stoi.py
index 253dab3ea38..cea3df3514e 100644
--- a/src/torchmetrics/audio/stoi.py
+++ b/src/torchmetrics/audio/stoi.py
@@ -50,7 +50,8 @@ class ShortTimeObjectiveIntelligibility(Metric):
 
     - ``stoi`` (:class:`~torch.Tensor`): float scalar tensor
 
-    .. note:: using this metrics requires you to have ``pystoi`` install. Either install as ``pip install
+    .. hint::
+        Using this metrics requires you to have ``pystoi`` install. Either install as ``pip install
         torchmetrics[audio]`` or ``pip install pystoi``.
 
     Args:
diff --git a/src/torchmetrics/classification/calibration_error.py b/src/torchmetrics/classification/calibration_error.py
index f1fe5cc17c7..404d9089bbc 100644
--- a/src/torchmetrics/classification/calibration_error.py
+++ b/src/torchmetrics/classification/calibration_error.py
@@ -214,7 +214,7 @@ class MulticlassCalibrationError(Metric):
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)`` containing ground truth labels, and
       therefore only contain values in the [0, n_classes-1] range (except if `ignore_index` is specified).
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
diff --git a/src/torchmetrics/classification/cohen_kappa.py b/src/torchmetrics/classification/cohen_kappa.py
index 58537f6acfe..3531eb6b106 100644
--- a/src/torchmetrics/classification/cohen_kappa.py
+++ b/src/torchmetrics/classification/cohen_kappa.py
@@ -50,7 +50,7 @@ class labels.
       Additionally, we convert to int tensor with thresholding using the value in ``threshold``.
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``.
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
@@ -175,7 +175,7 @@ class labels.
       convert probabilities/logits into an int tensor.
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``.
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
diff --git a/src/torchmetrics/classification/dice.py b/src/torchmetrics/classification/dice.py
index 59601104e9e..df9807cea7f 100644
--- a/src/torchmetrics/classification/dice.py
+++ b/src/torchmetrics/classification/dice.py
@@ -76,7 +76,7 @@ class Dice(Metric):
             - ``'samples'``: Calculate the metric for each sample, and average the metrics
               across samples (with equal weights for each sample).
 
-            .. note::
+            .. hint::
                What is considered a sample in the multi-dimensional multi-class case
                depends on the value of ``mdmc_average``.
 
diff --git a/src/torchmetrics/classification/hinge.py b/src/torchmetrics/classification/hinge.py
index fbd3cf04fdd..5514f98cccc 100644
--- a/src/torchmetrics/classification/hinge.py
+++ b/src/torchmetrics/classification/hinge.py
@@ -55,7 +55,7 @@ class BinaryHingeLoss(Metric):
       ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). The value
       1 always encodes the positive class.
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
@@ -189,7 +189,7 @@ class MulticlassHingeLoss(Metric):
       ground truth labels, and therefore only contain values in the [0, n_classes-1] range (except if `ignore_index`
       is specified).
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
diff --git a/src/torchmetrics/classification/jaccard.py b/src/torchmetrics/classification/jaccard.py
index 0ea04849696..385009d5a6a 100644
--- a/src/torchmetrics/classification/jaccard.py
+++ b/src/torchmetrics/classification/jaccard.py
@@ -52,7 +52,7 @@ class BinaryJaccardIndex(BinaryConfusionMatrix):
       Additionally, we convert to int tensor with thresholding using the value in ``threshold``.
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``.
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
@@ -170,7 +170,7 @@ class MulticlassJaccardIndex(MulticlassConfusionMatrix):
       probabilities/logits into an int tensor.
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``.
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
@@ -307,7 +307,7 @@ class MultilabelJaccardIndex(MultilabelConfusionMatrix):
       sigmoid per element. Additionally, we convert to int tensor with thresholding using the value in ``threshold``.
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
diff --git a/src/torchmetrics/classification/matthews_corrcoef.py b/src/torchmetrics/classification/matthews_corrcoef.py
index 8fab20badf5..49de1f03795 100644
--- a/src/torchmetrics/classification/matthews_corrcoef.py
+++ b/src/torchmetrics/classification/matthews_corrcoef.py
@@ -48,7 +48,7 @@ class BinaryMatthewsCorrCoef(BinaryConfusionMatrix):
       per element. Additionally, we convert to int tensor with thresholding using the value in ``threshold``.
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
@@ -156,7 +156,7 @@ class MulticlassMatthewsCorrCoef(MulticlassConfusionMatrix):
       probabilities/logits into an int tensor.
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
@@ -268,7 +268,7 @@ class MultilabelMatthewsCorrCoef(MultilabelConfusionMatrix):
       per element. Additionally, we convert to int tensor with thresholding using the value in ``threshold``.
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
diff --git a/src/torchmetrics/classification/precision_fixed_recall.py b/src/torchmetrics/classification/precision_fixed_recall.py
index 9db713f53c6..c761f9aa8a9 100644
--- a/src/torchmetrics/classification/precision_fixed_recall.py
+++ b/src/torchmetrics/classification/precision_fixed_recall.py
@@ -60,7 +60,7 @@ class BinaryPrecisionAtFixedRecall(BinaryPrecisionRecallCurve):
       ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). The value
       1 always encodes the positive class.
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
@@ -193,7 +193,7 @@ class MulticlassPrecisionAtFixedRecall(MulticlassPrecisionRecallCurve):
       ground truth labels, and therefore only contain values in the [0, n_classes-1] range (except if `ignore_index`
       is specified).
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns a tuple of either 2 tensors or 2 lists containing:
@@ -338,7 +338,7 @@ class MultilabelPrecisionAtFixedRecall(MultilabelPrecisionRecallCurve):
       ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). The value
       1 always encodes the positive class.
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns a tuple of either 2 tensors or 2 lists containing:
diff --git a/src/torchmetrics/classification/precision_recall_curve.py b/src/torchmetrics/classification/precision_recall_curve.py
index 366f11710d4..d0f9c632c02 100644
--- a/src/torchmetrics/classification/precision_recall_curve.py
+++ b/src/torchmetrics/classification/precision_recall_curve.py
@@ -67,7 +67,7 @@ class BinaryPrecisionRecallCurve(Metric):
       ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). The value
       1 always encodes the positive class.
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
@@ -244,7 +244,7 @@ class MulticlassPrecisionRecallCurve(Metric):
       ground truth labels, and therefore only contain values in the [0, n_classes-1] range (except if `ignore_index`
       is specified).
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
@@ -441,7 +441,7 @@ class MultilabelPrecisionRecallCurve(Metric):
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``. Target should be a tensor containing
       ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified).
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns the following a tuple of either 3 tensors or
diff --git a/src/torchmetrics/classification/ranking.py b/src/torchmetrics/classification/ranking.py
index 4d2df8a9151..9dda737030d 100644
--- a/src/torchmetrics/classification/ranking.py
+++ b/src/torchmetrics/classification/ranking.py
@@ -51,7 +51,7 @@ class MultilabelCoverageError(Metric):
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``. Target should be a tensor
       containing ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified).
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
@@ -171,7 +171,7 @@ class MultilabelRankingAveragePrecision(Metric):
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``. Target should be a tensor
       containing ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified).
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
@@ -291,7 +291,7 @@ class MultilabelRankingLoss(Metric):
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``. Target should be a tensor
       containing ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified).
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
diff --git a/src/torchmetrics/classification/recall_fixed_precision.py b/src/torchmetrics/classification/recall_fixed_precision.py
index 76880f2c3da..58a460b7b2e 100644
--- a/src/torchmetrics/classification/recall_fixed_precision.py
+++ b/src/torchmetrics/classification/recall_fixed_precision.py
@@ -59,7 +59,7 @@ class BinaryRecallAtFixedPrecision(BinaryPrecisionRecallCurve):
       ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). The value
       1 always encodes the positive class.
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
@@ -194,7 +194,7 @@ class MulticlassRecallAtFixedPrecision(MulticlassPrecisionRecallCurve):
       ground truth labels, and therefore only contain values in the [0, n_classes-1] range (except if `ignore_index`
       is specified).
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns a tuple of either 2 tensors or 2 lists containing:
@@ -337,7 +337,7 @@ class MultilabelRecallAtFixedPrecision(MultilabelPrecisionRecallCurve):
       ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). The value
       1 always encodes the positive class.
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns a tuple of either 2 tensors or 2 lists containing:
diff --git a/src/torchmetrics/classification/roc.py b/src/torchmetrics/classification/roc.py
index 2b69df1dac2..fc378e53ee0 100644
--- a/src/torchmetrics/classification/roc.py
+++ b/src/torchmetrics/classification/roc.py
@@ -54,7 +54,7 @@ class BinaryROC(BinaryPrecisionRecallCurve):
       ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). The value
       1 always encodes the positive class.
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns a tuple of 3 tensors containing:
@@ -71,8 +71,8 @@ class BinaryROC(BinaryPrecisionRecallCurve):
        `thresholds` argument to either an integer, list or a 1d tensor will use a binned version that uses memory of
        size :math:`\mathcal{O}(n_{thresholds})` (constant memory).
 
-    .. note::
-       The outputted thresholds will be in reversed order to ensure that they corresponds to both fpr and
+    .. attention::
+       The outputted thresholds will be in reversed order to ensure that they correspond to both fpr and
        tpr which are sorted in reversed order during their calculation, such that they are monotome increasing.
 
     Args:
@@ -191,7 +191,7 @@ class MulticlassROC(MulticlassPrecisionRecallCurve):
       ground truth labels, and therefore only contain values in the [0, n_classes-1] range (except if `ignore_index`
       is specified).
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns a tuple of either 3 tensors or 3 lists containing
@@ -216,8 +216,8 @@ class MulticlassROC(MulticlassPrecisionRecallCurve):
        `thresholds` argument to either an integer, list or a 1d tensor will use a binned version that uses memory of
        size :math:`\mathcal{O}(n_{thresholds} \times n_{classes})` (constant memory).
 
-    .. note::
-       Note that outputted thresholds will be in reversed order to ensure that they corresponds to both fpr
+    .. attention::
+       Note that outputted thresholds will be in reversed order to ensure that they correspond to both fpr
        and tpr which are sorted in reversed order during their calculation, such that they are monotome increasing.
 
     Args:
@@ -357,7 +357,7 @@ class MultilabelROC(MultilabelPrecisionRecallCurve):
     - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``. Target should be a tensor
       containing ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified).
 
-    .. note::
+    .. tip::
        Additional dimension ``...`` will be flattened into the batch dimension.
 
     As output to ``forward`` and ``compute`` the metric returns a tuple of either 3 tensors or 3 lists containing
@@ -382,8 +382,8 @@ class MultilabelROC(MultilabelPrecisionRecallCurve):
        `thresholds` argument to either an integer, list or a 1d tensor will use a binned version that uses memory of
        size :math:`\mathcal{O}(n_{thresholds} \times n_{labels})` (constant memory).
 
-    .. note::
-       The outputted thresholds will be in reversed order to ensure that they corresponds to both fpr and tpr
+    .. attention::
+       The outputted thresholds will be in reversed order to ensure that they correspond to both fpr and tpr
        which are sorted in reversed order during their calculation, such that they are monotome increasing.
 
     Args:
diff --git a/src/torchmetrics/collections.py b/src/torchmetrics/collections.py
index baa5b5ae9a6..b4ec0c4e4cc 100644
--- a/src/torchmetrics/collections.py
+++ b/src/torchmetrics/collections.py
@@ -83,7 +83,7 @@ class name as key for the output dict.
             this argument is ``True`` which enables this feature. Set this argument to `False` for disabling
             this behaviour. Can also be set to a list of lists of metrics for setting the compute groups yourself.
 
-    .. note::
+    .. tip::
         The compute groups feature can significantly speedup the calculation of metrics under the right conditions.
         First, the feature is only available when calling the ``update`` method and not when calling ``forward`` method
         due to the internal logic of ``forward`` preventing this. Secondly, since we compute groups share metric
@@ -91,7 +91,7 @@ class name as key for the output dict.
         reference and a copy of states are instead returned in this case (reference will be reestablished on the next
         call to ``update``).
 
-    .. note::
+    .. important::
         Metric collections can be nested at initialization (see last example) but the output of the collection will
         still be a single flatten dictionary combining the prefix and postfix arguments from the nested collection.
 
diff --git a/src/torchmetrics/detection/_mean_ap.py b/src/torchmetrics/detection/_mean_ap.py
index de0b11143a5..a6cc0e35921 100644
--- a/src/torchmetrics/detection/_mean_ap.py
+++ b/src/torchmetrics/detection/_mean_ap.py
@@ -202,16 +202,16 @@ class MeanAveragePrecision(Metric):
 
     For an example on how to use this metric check the `torchmetrics mAP example`_.
 
-    .. note::
-        ``map`` score is calculated with @[ IoU=self.iou_thresholds | area=all | max_dets=max_detection_thresholds ].
+    .. attention::
+        The ``map`` score is calculated with @[ IoU=self.iou_thresholds | area=all | max_dets=max_detection_thresholds ].
         Caution: If the initialization parameters are changed, dictionary keys for mAR can change as well.
         The default properties are also accessible via fields and will raise an ``AttributeError`` if not available.
 
-    .. note::
+    .. important::
         This metric is following the mAP implementation of `pycocotools`_ a standard implementation for the mAP metric
         for object detection.
 
-    .. note::
+    .. hint::
         This metric requires you to have `torchvision` version 0.8.0 or newer installed
         (with corresponding version 1.7.0 of torch or newer). This metric requires `pycocotools`
         installed when iou_type is `segm`. Please install with ``pip install torchvision`` or
diff --git a/src/torchmetrics/detection/mean_ap.py b/src/torchmetrics/detection/mean_ap.py
index f7aa6eb0276..1e2d0ee9840 100644
--- a/src/torchmetrics/detection/mean_ap.py
+++ b/src/torchmetrics/detection/mean_ap.py
@@ -153,14 +153,14 @@ class MeanAveragePrecision(Metric):
 
     For an example on how to use this metric check the `torchmetrics mAP example`_.
 
-    .. note::
-        ``map`` score is calculated with @[ IoU=self.iou_thresholds | area=all | max_dets=max_detection_thresholds ]
+    .. attention::
+        The ``map`` score is calculated with @[ IoU=self.iou_thresholds | area=all | max_dets=max_detection_thresholds ]
         e.g. the mean average precision for IoU thresholds 0.50, 0.55, 0.60, ..., 0.95 averaged over all classes and
         all areas and all max detections per image. If the IoU thresholds are changed this value will be calculated with
         the new thresholds. Caution: If the initialization parameters are changed, dictionary keys for mAR can change as
         well.
 
-    .. note::
+    .. important::
         This metric supports, at the moment, two different backends for the evaluation. The default backend is
         ``"pycocotools"``, which either require the official `pycocotools`_ implementation or this
         `fork of pycocotools`_ to be installed. We recommend using the fork as it is better maintained and easily
diff --git a/src/torchmetrics/functional/audio/dnsmos.py b/src/torchmetrics/functional/audio/dnsmos.py
index 9b0dca883db..8f74f8374c9 100644
--- a/src/torchmetrics/functional/audio/dnsmos.py
+++ b/src/torchmetrics/functional/audio/dnsmos.py
@@ -194,7 +194,8 @@ def deep_noise_suppression_mean_opinion_score(
     `DNSMOS P.835 paper <https://arxiv.org/abs/2110.01763>`_.
 
 
-    .. note:: using this metric requires you to have ``librosa``, ``onnxruntime`` and ``requests`` installed. Install
+    .. hint::
+        Using this metric requires you to have ``librosa``, ``onnxruntime`` and ``requests`` installed. Install
         as ``pip install torchmetrics['audio']`` or alternatively ``pip install librosa onnxruntime-gpu requests``
         (if you do not have GPU enabled machine install ``onnxruntime`` instead of ``onnxruntime-gpu``)
 
diff --git a/src/torchmetrics/functional/audio/nisqa.py b/src/torchmetrics/functional/audio/nisqa.py
index 6696f93965d..1a1c066ebf0 100644
--- a/src/torchmetrics/functional/audio/nisqa.py
+++ b/src/torchmetrics/functional/audio/nisqa.py
@@ -66,7 +66,8 @@
 def non_intrusive_speech_quality_assessment(preds: Tensor, fs: int) -> Tensor:
     """`Non-Intrusive Speech Quality Assessment`_ (NISQA v2.0) [1], [2].
 
-    .. note:: Using this metric requires you to have ``librosa`` and ``requests`` installed. Install as
+    .. hint::
+        Usingsing this metric requires you to have ``librosa`` and ``requests`` installed. Install as
         ``pip install librosa requests``.
 
     Args:
diff --git a/src/torchmetrics/functional/audio/pesq.py b/src/torchmetrics/functional/audio/pesq.py
index 516014434bf..04cf1ebace2 100644
--- a/src/torchmetrics/functional/audio/pesq.py
+++ b/src/torchmetrics/functional/audio/pesq.py
@@ -40,7 +40,8 @@ def perceptual_evaluation_speech_quality(
     This metric is a wrapper for the `pesq package`_. Note that input will be moved to `cpu` to perform the metric
     calculation.
 
-    .. note:: using this metrics requires you to have ``pesq`` install. Either install as ``pip install
+    .. hint::
+        Usingsing this metrics requires you to have ``pesq`` install. Either install as ``pip install
         torchmetrics[audio]`` or ``pip install pesq``. Note that ``pesq`` will compile with your currently
         installed version of numpy, meaning that if you upgrade numpy at some point in the future you will
         most likely have to reinstall ``pesq``.
diff --git a/src/torchmetrics/functional/audio/srmr.py b/src/torchmetrics/functional/audio/srmr.py
index d098366df6b..26d27f00999 100644
--- a/src/torchmetrics/functional/audio/srmr.py
+++ b/src/torchmetrics/functional/audio/srmr.py
@@ -202,11 +202,12 @@ def speech_reverberation_modulation_energy_ratio(
             Note: this argument is inherited from `SRMRpy`_. As the translated code is based to pytorch,
             setting `fast=True` may slow down the speed for calculating this metric on GPU.
 
-    .. note:: using this metrics requires you to have ``gammatone`` and ``torchaudio`` installed.
+    .. hint::
+        Usingsing this metrics requires you to have ``gammatone`` and ``torchaudio`` installed.
         Either install as ``pip install torchmetrics[audio]`` or ``pip install torchaudio``
         and ``pip install git+https://github.com/detly/gammatone``.
 
-    .. note::
+    .. attention::
         This implementation is experimental, and might not be consistent with the matlab
         implementation `SRMRToolbox`_, especially the fast implementation.
         The slow versions, a) fast=False, norm=False, max_cf=128, b) fast=False, norm=True, max_cf=30, have
diff --git a/src/torchmetrics/functional/audio/stoi.py b/src/torchmetrics/functional/audio/stoi.py
index 48e9e78510b..e029b721ef8 100644
--- a/src/torchmetrics/functional/audio/stoi.py
+++ b/src/torchmetrics/functional/audio/stoi.py
@@ -39,7 +39,8 @@ def short_time_objective_intelligibility(
     calculations on CPU, all input will automatically be moved to CPU to perform the metric calculation before being
     moved back to the original device.
 
-    .. note:: using this metrics requires you to have ``pystoi`` install. Either install as ``pip install
+    .. hint::
+        Usingsing this metrics requires you to have ``pystoi`` install. Either install as ``pip install
         torchmetrics[audio]`` or ``pip install pystoi``
 
     Args:
diff --git a/src/torchmetrics/functional/classification/dice.py b/src/torchmetrics/functional/classification/dice.py
index 845ed7162d7..0a0ffb457de 100644
--- a/src/torchmetrics/functional/classification/dice.py
+++ b/src/torchmetrics/functional/classification/dice.py
@@ -107,10 +107,12 @@ def dice(
             - ``'samples'``: Calculate the metric for each sample, and average the metrics
               across samples (with equal weights for each sample).
 
-            .. note:: What is considered a sample in the multi-dimensional multi-class case
+            .. tip::
+                What is considered a sample in the multi-dimensional multi-class case
                 depends on the value of ``mdmc_average``.
 
-            .. note:: If ``'none'`` and a given class doesn't occur in the ``preds`` or ``target``,
+            .. hint::
+                If ``'none'`` and a given class doesn't occur in the ``preds`` or ``target``,
                 the value for the class will be ``nan``.
 
         mdmc_average:
diff --git a/src/torchmetrics/functional/image/gradients.py b/src/torchmetrics/functional/image/gradients.py
index 03f751f0870..e87d4a75198 100644
--- a/src/torchmetrics/functional/image/gradients.py
+++ b/src/torchmetrics/functional/image/gradients.py
@@ -70,7 +70,8 @@ def image_gradients(img: Tensor) -> Tuple[Tensor, Tensor]:
                 [5., 5., 5., 5., 5.],
                 [0., 0., 0., 0., 0.]])
 
-    .. note:: The implementation follows the 1-step finite difference method as followed
+    .. note::
+           The implementation follows the 1-step finite difference method as followed
            by the TF implementation. The values are organized such that the gradient of
            [I(x+1, y)-[I(x, y)]] are at the (x, y) location
 
diff --git a/src/torchmetrics/functional/image/psnr.py b/src/torchmetrics/functional/image/psnr.py
index adb80e2bc77..7bd93ba94e1 100644
--- a/src/torchmetrics/functional/image/psnr.py
+++ b/src/torchmetrics/functional/image/psnr.py
@@ -134,8 +134,8 @@ def peak_signal_noise_ratio(
         >>> peak_signal_noise_ratio(pred, target)
         tensor(2.5527)
 
-    .. note::
-        Half precision is only support on GPU for this metric
+    .. attention::
+        Half precision is only support on GPU for this metric.
 
     """
     if dim is None and reduction != "elementwise_mean":
diff --git a/src/torchmetrics/functional/multimodal/clip_iqa.py b/src/torchmetrics/functional/multimodal/clip_iqa.py
index 49e710e248a..0fe74cc1855 100644
--- a/src/torchmetrics/functional/multimodal/clip_iqa.py
+++ b/src/torchmetrics/functional/multimodal/clip_iqa.py
@@ -271,7 +271,8 @@ def clip_image_quality_assessment(
             available prompts (see above). If tuple is provided, it must be of length 2 and the first string must be a
             positive prompt and the second string must be a negative prompt.
 
-    .. note:: If using the default `clip_iqa` model, the package `piq` must be installed. Either install with
+    .. hint::
+         If using the default `clip_iqa` model, the package `piq` must be installed. Either install with
         `pip install piq` or `pip install torchmetrics[multimodal]`.
 
     Returns:
diff --git a/src/torchmetrics/functional/multimodal/clip_score.py b/src/torchmetrics/functional/multimodal/clip_score.py
index 920eb6972e6..f70f37d534b 100644
--- a/src/torchmetrics/functional/multimodal/clip_score.py
+++ b/src/torchmetrics/functional/multimodal/clip_score.py
@@ -135,7 +135,8 @@ def clip_score(
     textual CLIP embedding :math:`E_C` for an caption :math:`C`. The score is bound between 0 and 100 and the closer
     to 100 the better.
 
-    .. note:: Metric is not scriptable
+    .. caution::
+        Metric is not scriptable
 
     Args:
         images: Either a single [N, C, H, W] tensor or a list of [C, H, W] tensors
diff --git a/src/torchmetrics/functional/regression/log_mse.py b/src/torchmetrics/functional/regression/log_mse.py
index 96d2938a8ee..3ba27ab9e99 100644
--- a/src/torchmetrics/functional/regression/log_mse.py
+++ b/src/torchmetrics/functional/regression/log_mse.py
@@ -68,8 +68,8 @@ def mean_squared_log_error(preds: Tensor, target: Tensor) -> Tensor:
         >>> mean_squared_log_error(x, y)
         tensor(0.0207)
 
-    .. note::
-        Half precision is only support on GPU for this metric
+    .. attention::
+        Half precision is only support on GPU for this metric.
 
     """
     sum_squared_log_error, num_obs = _mean_squared_log_error_update(preds, target)
diff --git a/src/torchmetrics/image/fid.py b/src/torchmetrics/image/fid.py
index 8c2e5d3cf76..ac559ed0c68 100644
--- a/src/torchmetrics/image/fid.py
+++ b/src/torchmetrics/image/fid.py
@@ -211,9 +211,8 @@ class FrechetInceptionDistance(Metric):
     that you calculate using `torch.float64` (default is `torch.float32`) which can be set using the `.set_dtype`
     method of the metric.
 
-    .. note:: using this metrics requires you to have torch 1.9 or higher installed
-
-    .. note:: using this metric with the default feature extractor requires that ``torch-fidelity``
+    .. hint::
+        Using this metric with the default feature extractor requires that ``torch-fidelity``
         is installed. Either install as ``pip install torchmetrics[image]`` or ``pip install torch-fidelity``
 
     As input to ``forward`` and ``update`` the metric accepts the following input
diff --git a/src/torchmetrics/image/inception.py b/src/torchmetrics/image/inception.py
index 2b125542a17..20d53d10f2b 100644
--- a/src/torchmetrics/image/inception.py
+++ b/src/torchmetrics/image/inception.py
@@ -49,7 +49,8 @@ class InceptionScore(Metric):
     ``normalize`` is set to ``False`` images are expected to have dtype uint8 and take values in the ``[0, 255]``
     range. All images will be resized to 299 x 299 which is the size of the original training data.
 
-    .. note:: using this metric with the default feature extractor requires that ``torch-fidelity``
+    .. hint::
+        Using this metric with the default feature extractor requires that ``torch-fidelity``
         is installed. Either install as ``pip install torchmetrics[image]`` or
         ``pip install torch-fidelity``
 
diff --git a/src/torchmetrics/image/kid.py b/src/torchmetrics/image/kid.py
index 975edf800eb..018fc7a7511 100644
--- a/src/torchmetrics/image/kid.py
+++ b/src/torchmetrics/image/kid.py
@@ -97,7 +97,8 @@ class KernelInceptionDistance(Metric):
     effect and update method expects to have the tensor given to `imgs` argument to be in the correct shape and
     type that is compatible to the custom feature extractor.
 
-    .. note:: using this metric with the default feature extractor requires that ``torch-fidelity``
+    .. hint::
+        Using this metric with the default feature extractor requires that ``torch-fidelity``
         is installed. Either install as ``pip install torchmetrics[image]`` or
         ``pip install torch-fidelity``
 
diff --git a/src/torchmetrics/image/lpip.py b/src/torchmetrics/image/lpip.py
index f1adb73648a..1893fb734ba 100644
--- a/src/torchmetrics/image/lpip.py
+++ b/src/torchmetrics/image/lpip.py
@@ -47,12 +47,10 @@ class LearnedPerceptualImagePatchSimilarity(Metric):
     Both input image patches are expected to have shape ``(N, 3, H, W)``. The minimum size of `H, W` depends on the
     chosen backbone (see `net_type` arg).
 
-    .. note:: using this metrics requires you to have ``torchvision`` package installed. Either install as
+    .. hint::
+        Using this metrics requires you to have ``torchvision`` package installed. Either install as
         ``pip install torchmetrics[image]`` or ``pip install torchvision``.
 
-    .. note:: this metric is not scriptable when using ``torch<1.8``. Please update your pytorch installation
-        if this is a issue.
-
     As input to ``forward`` and ``update`` the metric accepts the following input
 
     - ``img1`` (:class:`~torch.Tensor`): tensor with images of shape ``(N, 3, H, W)``
diff --git a/src/torchmetrics/image/mifid.py b/src/torchmetrics/image/mifid.py
index 7d7237b7416..a1e2d2f4c0a 100644
--- a/src/torchmetrics/image/mifid.py
+++ b/src/torchmetrics/image/mifid.py
@@ -84,10 +84,12 @@ class MemorizationInformedFrechetInceptionDistance(Metric):
     flag ``real`` determines if the images should update the statistics of the real distribution or the
     fake distribution.
 
-    .. note:: using this metrics requires you to have ``scipy`` install. Either install as ``pip install
+    .. hint::
+        Using this metrics requires you to have ``scipy`` install. Either install as ``pip install
         torchmetrics[image]`` or ``pip install scipy``
 
-    .. note:: using this metric with the default feature extractor requires that ``torch-fidelity``
+    .. hint::
+        Using this metric with the default feature extractor requires that ``torch-fidelity``
         is installed. Either install as ``pip install torchmetrics[image]`` or
         ``pip install torch-fidelity``
 
diff --git a/src/torchmetrics/image/perceptual_path_length.py b/src/torchmetrics/image/perceptual_path_length.py
index 4c312404934..117dca6f8cb 100644
--- a/src/torchmetrics/image/perceptual_path_length.py
+++ b/src/torchmetrics/image/perceptual_path_length.py
@@ -51,7 +51,8 @@ class PerceptualPathLength(Metric):
     if `conditional=False`, and `forward(z: Tensor, labels: Tensor) -> Tensor` if `conditional=True`. The returned
     tensor should have shape `(num_samples, C, H, W)` and be scaled to the range [0, 255].
 
-    .. note:: using this metric with the default feature extractor requires that ``torchvision`` is installed.
+    .. hint::
+        Using this metric with the default feature extractor requires that ``torchvision`` is installed.
         Either install as ``pip install torchmetrics[image]`` or ``pip install torchvision``
 
     As input to ``forward`` and ``update`` the metric accepts the following input
diff --git a/src/torchmetrics/image/psnrb.py b/src/torchmetrics/image/psnrb.py
index bac58b84e46..c9fb157d6ff 100644
--- a/src/torchmetrics/image/psnrb.py
+++ b/src/torchmetrics/image/psnrb.py
@@ -34,7 +34,7 @@ class PeakSignalNoiseRatioWithBlockedEffect(Metric):
     Where :math:`\text{MSE}` denotes the `mean-squared-error`_ function. This metric is a modified version of PSNR that
     better supports evaluation of images with blocked artifacts, that oftens occur in compressed images.
 
-    .. note::
+    .. attention::
         Metric only supports grayscale images. If you have RGB images, please convert them to grayscale first.
 
     As input to ``forward`` and ``update`` the metric accepts the following input
diff --git a/src/torchmetrics/metric.py b/src/torchmetrics/metric.py
index dd912aaa28e..279537aac51 100644
--- a/src/torchmetrics/metric.py
+++ b/src/torchmetrics/metric.py
@@ -239,11 +239,11 @@ def add_state(
             - If the metric state is a ``list``, the synced value will be a ``list`` containing the
               combined elements from all processes.
 
-        .. note::
+        .. important::
             When passing a custom function to ``dist_reduce_fx``, expect the synchronized metric state to follow
             the format discussed in the above note.
 
-        .. note::
+        .. caution::
             The values inserted into a list state are deleted whenever :meth:`~Metric.reset` is called. This allows
             device memory to be automatically reallocated, but may produce unexpected effects when referencing list
             states. To retain such values after :meth:`~Metric.reset` is called, you must first copy them to another
diff --git a/src/torchmetrics/multimodal/clip_iqa.py b/src/torchmetrics/multimodal/clip_iqa.py
index 7a66de4ae3e..a28a360a0f1 100644
--- a/src/torchmetrics/multimodal/clip_iqa.py
+++ b/src/torchmetrics/multimodal/clip_iqa.py
@@ -112,7 +112,8 @@ class CLIPImageQualityAssessment(Metric):
             positive prompt and the second string must be a negative prompt.
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
-    .. note:: If using the default `clip_iqa` model, the package `piq` must be installed. Either install with
+    .. hint::
+         If using the default `clip_iqa` model, the package `piq` must be installed. Either install with
         `pip install piq` or `pip install torchmetrics[image]`.
 
     Raises:
diff --git a/src/torchmetrics/multimodal/clip_score.py b/src/torchmetrics/multimodal/clip_score.py
index f385fbc145d..92ca7ad6b4f 100644
--- a/src/torchmetrics/multimodal/clip_score.py
+++ b/src/torchmetrics/multimodal/clip_score.py
@@ -54,7 +54,8 @@ class CLIPScore(Metric):
     textual CLIP embedding :math:`E_C` for an caption :math:`C`. The score is bound between 0 and 100 and the closer
     to 100 the better.
 
-    .. note:: Metric is not scriptable
+    .. caution::
+        Metric is not scriptable
 
     As input to ``forward`` and ``update`` the metric accepts the following input
 
diff --git a/src/torchmetrics/regression/kl_divergence.py b/src/torchmetrics/regression/kl_divergence.py
index 10fc6d90bc9..253b7a05002 100644
--- a/src/torchmetrics/regression/kl_divergence.py
+++ b/src/torchmetrics/regression/kl_divergence.py
@@ -64,8 +64,8 @@ class KLDivergence(Metric):
         ValueError:
             If ``reduction`` is not one of ``'mean'``, ``'sum'``, ``'none'`` or ``None``.
 
-    .. note::
-        Half precision is only support on GPU for this metric
+    .. attention::
+        Half precision is only support on GPU for this metric.
 
     Example:
         >>> from torch import tensor
diff --git a/src/torchmetrics/regression/log_mse.py b/src/torchmetrics/regression/log_mse.py
index 85e01dba314..da190016844 100644
--- a/src/torchmetrics/regression/log_mse.py
+++ b/src/torchmetrics/regression/log_mse.py
@@ -52,8 +52,8 @@ class MeanSquaredLogError(Metric):
         >>> mean_squared_log_error(preds, target)
         tensor(0.0397)
 
-    .. note::
-        Half precision is only support on GPU for this metric
+    .. attention::
+        Half precision is only support on GPU for this metric.
 
     """
 
diff --git a/src/torchmetrics/retrieval/base.py b/src/torchmetrics/retrieval/base.py
index 5446d8668b1..f9a0a4f8cc4 100644
--- a/src/torchmetrics/retrieval/base.py
+++ b/src/torchmetrics/retrieval/base.py
@@ -50,10 +50,11 @@ class RetrievalMetric(Metric, ABC):
     - ``indexes`` (:class:`~torch.Tensor`): A long tensor of shape ``(N, ...)`` which indicate to which query a
       prediction belongs
 
-    .. note:: ``indexes``, ``preds`` and ``target`` must have the same dimension and will be flatten
-    to single dimension once provided.
+    .. hint::
+        The ``indexes``, ``preds`` and ``target`` must have the same dimension and will be flattened
+        to single dimension once provided.
 
-    .. note::
+    .. attention::
         Predictions will be first grouped by ``indexes`` and then the real metric, defined by overriding
         the `_metric` method, will be computed as the mean of the scores over each query.
 
diff --git a/src/torchmetrics/retrieval/precision_recall_curve.py b/src/torchmetrics/retrieval/precision_recall_curve.py
index 701e89b1433..9b4f4d38d0e 100644
--- a/src/torchmetrics/retrieval/precision_recall_curve.py
+++ b/src/torchmetrics/retrieval/precision_recall_curve.py
@@ -303,9 +303,10 @@ class RetrievalRecallAtFixedPrecision(RetrievalPrecisionRecallCurve):
     - ``indexes`` (:class:`~torch.Tensor`): A long tensor of shape ``(N, ...)`` which indicate to which query a
       prediction belongs
 
-    .. note:: All ``indexes``, ``preds`` and ``target`` must have the same dimension.
+    .. important::
+         All ``indexes``, ``preds`` and ``target`` must have the same dimension.
 
-    .. note::
+    .. attention::
         Predictions will be first grouped by ``indexes`` and then `RetrievalRecallAtFixedPrecision`
         will be computed as the mean of the `RetrievalRecallAtFixedPrecision` over each query.
 
diff --git a/src/torchmetrics/wrappers/multitask.py b/src/torchmetrics/wrappers/multitask.py
index fa2f04db97d..04ddd87ad71 100644
--- a/src/torchmetrics/wrappers/multitask.py
+++ b/src/torchmetrics/wrappers/multitask.py
@@ -43,8 +43,8 @@ class MultitaskWrapper(WrapperMetric):
         postfix:
             A string to append after the keys of the output dict. If not provided, will default to an empty string.
 
-    .. note::
-        The use pre prefix and postfix allows for easily creating task wrappers for training, validation and test.
+    .. tip::
+        The use prefix and postfix allows for easily creating task wrappers for training, validation and test.
         The arguments are only changing the output keys of the computed metrics and not the input keys. This means
         that a ``MultitaskWrapper`` initialized as ``MultitaskWrapper({"task": Metric()}, prefix="train_")`` will
         still expect the input to be a dictionary with the key "task", but the output will be a dictionary with the key