[Misc] Fix indent for AddSelfLoop. (#5843)

Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>

python/dgl/transforms/module.py

@@ -510,53 +510,53 @@ class LaplacianPE(LapPE):
 class AddSelfLoop(BaseTransform):
     r"""Add self-loops for each node in the graph and return a new graph.
 
-     For heterogeneous graphs, self-loops are added only for edge types with same
-     source and destination node types.
-
-     Parameters
-     ----------
-     allow_duplicate : bool, optional
-         If False, it will first remove self-loops to prevent duplicate self-loops.
-     new_etypes : bool, optional
-         If True, it will add an edge type 'self' per node type, which holds self-loops.
-     edge_feat_names : list[str], optional
-         The names of the self-loop features to apply `fill_data`. If None, it
-         will apply `fill_data` to all self-loop features. Default: None.
-     fill_data : int, float or str, optional
-         The value to fill the self-loop features. Default: 1.
-
-         * If ``fill_data`` is ``int`` or ``float``, self-loop features will be directly given by
-           ``fill_data``.
-         * if ``fill_data`` is ``str``, self-loop features will be generated by aggregating the
-           features of the incoming edges of the corresponding nodes. The supported aggregation are:
-           ``'mean'``, ``'sum'``, ``'max'``, ``'min'``.
-
-     Example
-     -------
-
-     >>> import dgl
-     >>> from dgl import AddSelfLoop
-
-     Case1: Add self-loops for a homogeneous graph
-
-     >>> transform = AddSelfLoop(fill_data='sum')
-     >>> g = dgl.graph(([0, 0, 2], [2, 1, 0]))
-     >>> g.edata['he'] = torch.arange(3).float().reshape(-1, 1)
-     >>> new_g = transform(g)
-     >>> print(new_g.edges())
-     (tensor([1, 0, 1, 2]), tensor([2, 0, 1, 2]))
-     >>> print(new_g.edata('he'))
-     tensor([[0.],
-             [1.],
-             [2.],
-             [2.],
-             [1.],
-             [0.]])
-
-     Case2: Add self-loops for a heterogeneous graph
-
-     >>> transform = AddSelfLoop(fill_data='sum')
-     >>> g = dgl.heterograph({
+    For heterogeneous graphs, self-loops are added only for edge types with same
+    source and destination node types.
+
+    Parameters
+    ----------
+    allow_duplicate : bool, optional
+        If False, it will first remove self-loops to prevent duplicate self-loops.
+    new_etypes : bool, optional
+        If True, it will add an edge type 'self' per node type, which holds self-loops.
+    edge_feat_names : list[str], optional
+        The names of the self-loop features to apply `fill_data`. If None, it
+        will apply `fill_data` to all self-loop features. Default: None.
+    fill_data : int, float or str, optional
+        The value to fill the self-loop features. Default: 1.
+
+        * If ``fill_data`` is ``int`` or ``float``, self-loop features will be directly given by
+          ``fill_data``.
+        * if ``fill_data`` is ``str``, self-loop features will be generated by aggregating the
+          features of the incoming edges of the corresponding nodes. The supported aggregation are:
+          ``'mean'``, ``'sum'``, ``'max'``, ``'min'``.
+
+    Example
+    -------
+
+    >>> import dgl
+    >>> from dgl import AddSelfLoop
+
+    Case1: Add self-loops for a homogeneous graph
+
+    >>> transform = AddSelfLoop(fill_data='sum')
+    >>> g = dgl.graph(([0, 0, 2], [2, 1, 0]))
+    >>> g.edata['he'] = torch.arange(3).float().reshape(-1, 1)
+    >>> new_g = transform(g)
+    >>> print(new_g.edges())
+    (tensor([1, 0, 1, 2]), tensor([2, 0, 1, 2]))
+    >>> print(new_g.edata('he'))
+    tensor([[0.],
+            [1.],
+            [2.],
+            [2.],
+            [1.],
+            [0.]])
+
+    Case2: Add self-loops for a heterogeneous graph
+
+    >>> transform = AddSelfLoop(fill_data='sum')
+    >>> g = dgl.heterograph({
     ...     ('user', 'follows', 'user'): (torch.tensor([1, 2]),
     ...                                   torch.tensor([0, 1])),
     ...     ('user', 'plays', 'game'): (torch.tensor([0, 1]),
@@ -564,23 +564,23 @@ class AddSelfLoop(BaseTransform):
     >>> g.edata['feat'] = {('user', 'follows', 'user'): torch.randn(2, 5),
     ...                    ('user', 'plays', 'game'): torch.randn(2, 5)}
     >>> g.edata['feat1'] = {('user', 'follows', 'user'): torch.randn(2, 15),
-      ...                   ('user', 'plays', 'game'): torch.randn(2, 15)}
-     >>> new_g = transform(g)
-     >>> print(new_g.edges(etype='plays'))
-     (tensor([0, 1]), tensor([0, 1]))
-     >>> print(new_g.edges(etype='follows'))
-     (tensor([1, 2]), tensor([0, 1]))
-     >>> print(new_g.edata['feat'][('user', 'follows', 'user')].shape)
-     torch.Size([5, 5])
-
-     Case3: Add self-etypes for a heterogeneous graph
-
-     >>> transform = AddSelfLoop(new_etypes=True)
-     >>> new_g = transform(g)
-     >>> print(new_g.edges(etype='follows'))
-     (tensor([1, 0, 1, 2]), tensor([2, 0, 1, 2]))
-     >>> print(new_g.edges(etype=('game', 'self', 'game')))
-     (tensor([0, 1]), tensor([0, 1]))
+    ...                     ('user', 'plays', 'game'): torch.randn(2, 15)}
+    >>> new_g = transform(g)
+    >>> print(new_g.edges(etype='plays'))
+    (tensor([0, 1]), tensor([0, 1]))
+    >>> print(new_g.edges(etype='follows'))
+    (tensor([1, 2]), tensor([0, 1]))
+    >>> print(new_g.edata['feat'][('user', 'follows', 'user')].shape)
+    torch.Size([5, 5])
+
+    Case3: Add self-etypes for a heterogeneous graph
+
+    >>> transform = AddSelfLoop(new_etypes=True)
+    >>> new_g = transform(g)
+    >>> print(new_g.edges(etype='follows'))
+    (tensor([1, 0, 1, 2]), tensor([2, 0, 1, 2]))
+    >>> print(new_g.edges(etype=('game', 'self', 'game')))
+    (tensor([0, 1]), tensor([0, 1]))
     """
 
     def __init__(