fix lint

python/dgl/sparse/sparse_matrix.py

@@ -678,28 +678,33 @@ def relabel(
         dim: int,
         leading_indices: Optional[torch.Tensor] = None,
     ):
-        """Relabels indices of a dimension and remove rows or columns without non-zero
-        elements in the sparse matrix.
-    This function serves a dual purpose: it allows you to reorganize the indices
-    within a specific dimension (rows or columns) of the sparse matrix and, if
-    needed, place certain 'heading_indices' at the beginning of the relabeled dimension.
-
-    In the absence of 'heading_indices' (when it's set to `None`), the order of
-    relabeled indices remains the same as the original order, except that rows or
-    columns without non-zero elements are removed. When 'heading_indices' are provided,
-    they are positioned at the start of the relabeled dimension.
-
-    This function mimics 'dgl.to_block', a method used to compress a sampled
-    subgraph by eliminating redundant nodes. The 'heading_indices' parameter
-    replicates the behavior of 'include_dst_in_src' in 'dgl.to_block.' Setting
-    'heading_indices' to column IDs when relabeling the row dimension, for example,
-    achieves the same effect as including destination nodes in source nodes.
+        """Relabels indices of a dimension and remove rows or columns without
+        non-zero elements in the sparse matrix.
+
+        This function serves a dual purpose: it allows you to reorganize the
+        indices within a specific dimension (rows or columns) of the sparse
+        matrix and, if needed, place certain 'leading_indices' at the beginning
+        of the relabeled dimension.
+
+        In the absence of 'leading_indices' (when it's set to `None`), the order
+        of relabeled indices remains the same as the original order, except that
+        rows or columns without non-zero elements are removed. When
+        'leading_indices' are provided, they are positioned at the start of the
+        relabeled dimension.
+
+        This function mimics 'dgl.to_block', a method used to compress a sampled
+        subgraph by eliminating redundant nodes. The 'leading_indices' parameter
+        replicates the behavior of 'include_dst_in_src' in 'dgl.to_block.'
+        Setting 'leading_indices' to column IDs when relabeling the row
+        dimension, for example, achieves the same effect as including destination
+        nodes in source nodes.
+
         Parameters
         ----------
         dim : int
             The dimension to relabel. Should be 0 or 1. Use `dim = 0` for rowwise
             relabeling and `dim = 1` for columnwise relabeling.
-        heading_indices : torch.Tensor, optional
+        leading_indices : torch.Tensor, optional
             An optional tensor containing row or column ids that should be placed
             at the beginning of the relabeled dimension.
 
@@ -717,7 +722,7 @@ def relabel(
 
         Case 1: Relabel rows without indices.
 
-        >>> B, original_rows = A.relabel(dim=0, heading_indices=None)
+        >>> B, original_rows = A.relabel(dim=0, leading_indices=None)
         >>> print(B)
         SparseMatrix(indices=tensor([[0, 1], [1, 2]]),
                      shape=(2, 3), nnz=2)
@@ -726,7 +731,7 @@ def relabel(
 
         Case 2: Relabel rows with indices.
 
-        >>> B, original_rows = A.relabel(dim=0, heading_indices=[1, 2])
+        >>> B, original_rows = A.relabel(dim=0, leading_indices=[1, 2])
         >>> print(B)
         SparseMatrix(indices=tensor([[1, 2], [2, 1]]),
                      shape=(3, 3), nnz=2)