From ac95d3f464d2e19389f29de09f12423546a514d2 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 23 Dec 2024 02:34:15 +0000
Subject: [PATCH] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 CHANGELOG.md                | 1 +
 torch_frame/data/dataset.py | 8 ++++----
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 770fd3a5..60d39093 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,6 +6,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ## \[Unreleased\]
 
 ### Added
+
 - Added support for materializing dataset for train and test dataframe separately([#470](https://github.com/pyg-team/pytorch-frame/issues/470))
 - Added support for PyTorch 2.5 ([#464](https://github.com/pyg-team/pytorch-frame/pull/464))
 - Added a benchmark script to compare PyTorch Frame with PyTorch Tabular ([#398](https://github.com/pyg-team/pytorch-frame/pull/398), [#444](https://github.com/pyg-team/pytorch-frame/pull/444))
diff --git a/torch_frame/data/dataset.py b/torch_frame/data/dataset.py
index 8b84de0b..ddf31051 100644
--- a/torch_frame/data/dataset.py
+++ b/torch_frame/data/dataset.py
@@ -571,7 +571,7 @@ def materialize(
                 :obj:`path`. If :obj:`path` is :obj:`None`, this will
                 materialize the dataset without caching.
                 (default: :obj:`None`)
-            col_stats (Dict[str, Dict[StatType, Any]], optional): optional 
+            col_stats (Dict[str, Dict[StatType, Any]], optional): optional
             col_stats provided by the user. If not provided, the statistics
             is calculated from the dataframe itself. (default: :obj:`None`)
 
@@ -604,9 +604,9 @@ def materialize(
                     sep=self.col_to_sep.get(col, None),
                     time_format=self.col_to_time_format.get(col, None),
                 )
-            # For a target column, sort categories lexicographically such that
-            # we do not accidentally swap labels in binary classification
-            # tasks.
+                # For a target column, sort categories lexicographically such that
+                # we do not accidentally swap labels in binary classification
+                # tasks.
                 if col == self.target_col and stype == torch_frame.categorical:
                     index, value = self._col_stats[col][StatType.COUNT]
                     if len(index) == 2: