Change example code for indices creation from two line to one line to avoid test break. (#5222)

* add_backslash

* fix

* more

* blabla
Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>

python/dgl/sparse/elementwise_op.py

@@ -42,8 +42,7 @@ def add(
 
     Examples
     --------
-    >>> indices = torch.tensor([[1, 0, 2],
-    >>>                         [0, 1, 2]])
+    >>> indices = torch.tensor([[1, 0, 2], [0, 1, 2]])
     >>> val = torch.tensor([10, 20, 30])
     >>> A = dglsp.spmatrix(indices, val)
     >>> B = dglsp.diag(torch.arange(1, 4))
@@ -89,8 +88,7 @@ def sub(
 
     Examples
     --------
-    >>> indices = torch.tensor([[1, 0, 2],
-    >>>                         [0, 1, 2]])
+    >>> indices = torch.tensor([[1, 0, 2], [0, 1, 2]])
     >>> val = torch.tensor([10, 20, 30])
     >>> A = dglsp.spmatrix(indices, val)
     >>> B = dglsp.diag(torch.arange(1, 4))
@@ -136,8 +134,7 @@ def mul(
 
     Examples
     --------
-    >>> indices = torch.tensor([[1, 0, 2],
-    >>>                         [0, 3, 2]])
+    >>> indices = torch.tensor([[1, 0, 2], [0, 3, 2]])
     >>> val = torch.tensor([10, 20, 30])
     >>> A = dglsp.spmatrix(indices, val)
     >>> dglsp.mul(A, 2)
@@ -202,8 +199,7 @@ def div(
     DiagMatrix(val=tensor([0.5000, 1.0000, 1.5000]),
                shape=(3, 3))
 
-    >>> indices = torch.tensor([[1, 0, 2],
-    >>>                         [0, 3, 2]])
+    >>> indices = torch.tensor([[1, 0, 2], [0, 3, 2]])
     >>> val = torch.tensor([1, 2, 3])
     >>> A = dglsp.spmatrix(indices, val, shape=(3, 4))
     >>> dglsp.div(A, 2)
@@ -247,8 +243,7 @@ def power(
 
     Examples
     --------
-    >>> indices = torch.tensor([[1, 0, 2],
-    >>>                         [0, 3, 2]])
+    >>> indices = torch.tensor([[1, 0, 2], [0, 3, 2]])
     >>> val = torch.tensor([10, 20, 30])
     >>> A = dglsp.spmatrix(indices, val)
     >>> dglsp.power(A, 2)

python/dgl/sparse/elementwise_op_sp.py

@@ -30,8 +30,7 @@ def sp_add(A: SparseMatrix, B: SparseMatrix) -> SparseMatrix:
     Examples
     --------
 
-    >>> indices = torch.tensor([[1, 0, 2],
-    >>>                         [0, 3, 2]])
+    >>> indices = torch.tensor([[1, 0, 2], [0, 3, 2]])
     >>> val = torch.tensor([10, 20, 30])
     >>> A = dglsp.spmatrix(indices, val, shape=(3, 4))
     >>> A + A
@@ -63,8 +62,7 @@ def sp_sub(A: SparseMatrix, B: SparseMatrix) -> SparseMatrix:
     Examples
     --------
 
-    >>> indices = torch.tensor([[1, 0, 2],
-    >>>                         [0, 3, 2]])
+    >>> indices = torch.tensor([[1, 0, 2], [0, 3, 2]])
     >>> val = torch.tensor([10, 20, 30])
     >>> val2 = torch.tensor([5, 10, 15])
     >>> A = dglsp.spmatrix(indices, val, shape=(3, 4))
@@ -99,8 +97,7 @@ def sp_mul(A: SparseMatrix, B: Scalar) -> SparseMatrix:
     Examples
     --------
 
-    >>> indices = torch.tensor([[1, 0, 2],
-    >>>                         [0, 3, 2]])
+    >>> indices = torch.tensor([[1, 0, 2], [0, 3, 2]])
     >>> val = torch.tensor([1, 2, 3])
     >>> A = dglsp.spmatrix(indices, val, shape=(3, 4))
 
@@ -142,8 +139,7 @@ def sp_div(A: SparseMatrix, B: Scalar) -> SparseMatrix:
 
     Examples
     --------
-    >>> indices = torch.tensor([[1, 0, 2],
-    >>>                         [0, 3, 2]])
+    >>> indices = torch.tensor([[1, 0, 2], [0, 3, 2]])
     >>> val = torch.tensor([1, 2, 3])
     >>> A = dglsp.spmatrix(indices, val, shape=(3, 4))
     >>> A / 2
@@ -177,8 +173,7 @@ def sp_power(A: SparseMatrix, scalar: Scalar) -> SparseMatrix:
 
     Examples
     --------
-    >>> indices = torch.tensor([[1, 0, 2],
-    >>>                         [0, 3, 2]])
+    >>> indices = torch.tensor([[1, 0, 2], [0, 3, 2]])
     >>> val = torch.tensor([10, 20, 30])
     >>> A = dglsp.spmatrix(indices, val)
     >>> A ** 2

python/dgl/sparse/matmul.py

@@ -29,8 +29,7 @@ def spmm(A: Union[SparseMatrix, DiagMatrix], X: torch.Tensor) -> torch.Tensor:
     Examples
     --------
 
-    >>> indices = torch.tensor([[0, 1, 1],
-    >>>                         [1, 0, 1]])
+    >>> indices = torch.tensor([[0, 1, 1], [1, 0, 1]])
     >>> val = torch.randn(len(row))
     >>> A = dglsp.spmatrix(indices, val)
     >>> X = torch.randn(2, 3)
@@ -72,8 +71,7 @@ def bspmm(A: Union[SparseMatrix, DiagMatrix], X: torch.Tensor) -> torch.Tensor:
     Examples
     --------
 
-    >>> indices = torch.tensor([[0, 1, 1],
-    >>>                         [1, 0, 2]])
+    >>> indices = torch.tensor([[0, 1, 1], [1, 0, 2]])
     >>> val = torch.randn(len(row), 2)
     >>> A = dglsp.spmatrix(indices, val, shape=(3, 3))
     >>> X = torch.randn(3, 3, 2)
@@ -197,12 +195,10 @@ def spspmm(
     Examples
     --------
 
-    >>> indices1 = torch.tensor([[0, 1, 1],
-    >>>                          [1, 0, 1]])
+    >>> indices1 = torch.tensor([[0, 1, 1], [1, 0, 1]])
     >>> val1 = torch.ones(len(row1))
     >>> A = dglsp.spmatrix(indices1, val1)
-    >>> indices2 = torch.tensor([[0, 1, 1],
-    >>>                          [0, 2, 1]])
+    >>> indices2 = torch.tensor([[0, 1, 1], [0, 2, 1]])
     >>> val2 = torch.ones(len(row2))
     >>> B = dglsp.spmatrix(indices2, val2)
     >>> dglsp.spspmm(A, B)
@@ -293,8 +289,7 @@ def matmul(
 
     Multiplies a sparse matrix with a dense matrix.
 
-    >>> indices = torch.tensor([[0, 1, 1],
-    >>>                         [1, 0, 1]])
+    >>> indices = torch.tensor([[0, 1, 1], [1, 0, 1]])
     >>> val = torch.randn(len(row))
     >>> A = dglsp.spmatrix(indices, val)
     >>> X = torch.randn(2, 3)
@@ -306,12 +301,10 @@ def matmul(
 
     Multiplies a sparse matrix with a sparse matrix.
 
-    >>> indices1 = torch.tensor([[0, 1, 1],
-    >>>                          [1, 0, 1]])
+    >>> indices1 = torch.tensor([[0, 1, 1], [1, 0, 1]])
     >>> val1 = torch.ones(len(row1))
     >>> A = dglsp.spmatrix(indices1, val1)
-    >>> indices2 = torch.tensor([[0, 1, 1],
-    >>>                          [0, 2, 1]])
+    >>> indices2 = torch.tensor([[0, 1, 1], [0, 2, 1]])
     >>> val2 = torch.ones(len(row2))
     >>> B = dglsp.spmatrix(indices2, val2)
     >>> result = dglsp.matmul(A, B)

python/dgl/sparse/reduction.py

@@ -44,8 +44,7 @@ def reduce(input: SparseMatrix, dim: Optional[int] = None, rtype: str = "sum"):
 
     Case1: scalar-valued sparse matrix
 
-    >>> indices = torch.tensor([[0, 1, 1],
-    >>>                         [0, 0, 2]])
+    >>> indices = torch.tensor([[0, 1, 1], [0, 0, 2]])
     >>> val = torch.tensor([1, 1, 2])
     >>> A = dglsp.spmatrix(indices, val, shape=(4, 3))
     >>> dglsp.reduce(A, rtype='sum')
@@ -61,8 +60,7 @@ def reduce(input: SparseMatrix, dim: Optional[int] = None, rtype: str = "sum"):
 
     Case2: vector-valued sparse matrix
 
-    >>> indices = torch.tensor([[0, 1, 1],
-    >>>                         [0, 0, 2]])
+    >>> indices = torch.tensor([[0, 1, 1], [0, 0, 2]])
     >>> val = torch.tensor([[1., 2.], [2., 1.], [2., 2.]])
     >>> A = dglsp.spmatrix(indices, val, shape=(4, 3))
     >>> dglsp.reduce(A, rtype='sum')
@@ -113,8 +111,7 @@ def sum(input: SparseMatrix, dim: Optional[int] = None):
 
     Case1: scalar-valued sparse matrix
 
-    >>> indices = torch.tensor([[0, 1, 1],
-    >>>                         [0, 0, 2]])
+    >>> indices = torch.tensor([[0, 1, 1], [0, 0, 2]])
     >>> val = torch.tensor([1, 1, 2])
     >>> A = dglsp.spmatrix(indices, val, shape=(4, 3))
     >>> dglsp.sum(A)
@@ -126,8 +123,7 @@ def sum(input: SparseMatrix, dim: Optional[int] = None):
 
     Case2: vector-valued sparse matrix
 
-    >>> indices = torch.tensor([[0, 1, 1],
-    >>>                         [0, 0, 2]])
+    >>> indices = torch.tensor([[0, 1, 1], [0, 0, 2]])
     >>> val = torch.tensor([[1, 2], [2, 1], [2, 2]])
     >>> A = dglsp.spmatrix(indices, val, shape=(4, 3))
     >>> dglsp.sum(A)
@@ -172,8 +168,7 @@ def smax(input: SparseMatrix, dim: Optional[int] = None):
 
     Case1: scalar-valued sparse matrix
 
-    >>> indices = torch.tensor([[0, 1, 1],
-    >>>                         [0, 0, 2]])
+    >>> indices = torch.tensor([[0, 1, 1], [0, 0, 2]])
     >>> val = torch.tensor([1, 1, 2])
     >>> A = dglsp.spmatrix(indices, val, shape=(4, 3))
     >>> dglsp.smax(A)
@@ -185,8 +180,7 @@ def smax(input: SparseMatrix, dim: Optional[int] = None):
 
     Case2: vector-valued sparse matrix
 
-    >>> indices = torch.tensor([[0, 1, 1],
-    >>>                         [0, 0, 2]])
+    >>> indices = torch.tensor([[0, 1, 1], [0, 0, 2]])
     >>> val = torch.tensor([[1, 2], [2, 1], [2, 2]])
     >>> A = dglsp.spmatrix(indices, val, shape=(4, 3))
     >>> dglsp.smax(A)
@@ -232,8 +226,7 @@ def smin(input: SparseMatrix, dim: Optional[int] = None):
 
     Case1: scalar-valued sparse matrix
 
-    >>> indices = torch.tensor([[0, 1, 1],
-    >>>                         [0, 0, 2]])
+    >>> indices = torch.tensor([[0, 1, 1], [0, 0, 2]])
     >>> val = torch.tensor([1, 1, 2])
     >>> A = dglsp.spmatrix(indices, val, shape=(4, 3))
     >>> dglsp.smin(A)
@@ -245,8 +238,7 @@ def smin(input: SparseMatrix, dim: Optional[int] = None):
 
     Case2: vector-valued sparse matrix
 
-    >>> indices = torch.tensor([[0, 1, 1],
-    >>>                         [0, 0, 2]])
+    >>> indices = torch.tensor([[0, 1, 1], [0, 0, 2]])
     >>> val = torch.tensor([[1, 2], [2, 1], [2, 2]])
     >>> A = dglsp.spmatrix(indices, val, shape=(4, 3))
     >>> dglsp.smin(A)
@@ -296,8 +288,7 @@ def smean(input: SparseMatrix, dim: Optional[int] = None):
 
     Case1: scalar-valued sparse matrix
 
-    >>> indices = torch.tensor([[0, 1, 1],
-    >>>                         [0, 0, 2]])
+    >>> indices = torch.tensor([[0, 1, 1], [0, 0, 2]])
     >>> val = torch.tensor([1., 1., 2.])
     >>> A = dglsp.spmatrix(indices, val, shape=(4, 3))
     >>> dglsp.smean(A)
@@ -309,8 +300,7 @@ def smean(input: SparseMatrix, dim: Optional[int] = None):
 
     Case2: vector-valued sparse matrix
 
-    >>> indices = torch.tensor([[0, 1, 1],
-    >>>                         [0, 0, 2]])
+    >>> indices = torch.tensor([[0, 1, 1], [0, 0, 2]])
     >>> val = torch.tensor([[1., 2.], [2., 1.], [2., 2.]])
     >>> A = dglsp.spmatrix(indices, val, shape=(4, 3))
     >>> dglsp.smean(A)
@@ -360,8 +350,7 @@ def sprod(input: SparseMatrix, dim: Optional[int] = None):
 
     Case1: scalar-valued sparse matrix
 
-    >>> indices = torch.tensor([[0, 1, 1],
-    >>>                         [0, 0, 2]])
+    >>> indices = torch.tensor([[0, 1, 1], [0, 0, 2]])
     >>> val = torch.tensor([1, 1, 2])
     >>> A = dglsp.spmatrix(indices, val, shape=(4, 3))
     >>> dglsp.sprod(A)
@@ -373,8 +362,7 @@ def sprod(input: SparseMatrix, dim: Optional[int] = None):
 
     Case2: vector-valued sparse matrix
 
-    >>> indices = torch.tensor([[0, 1, 1],
-    >>>                         [0, 0, 2]])
+    >>> indices = torch.tensor([[0, 1, 1], [0, 0, 2]])
     >>> val = torch.tensor([[1, 2], [2, 1], [2, 2]])
     >>> A = dglsp.spmatrix(indices, val, shape=(4, 3))
     >>> dglsp.sprod(A)

python/dgl/sparse/sddmm.py

@@ -39,8 +39,7 @@ def sddmm(A: SparseMatrix, X1: torch.Tensor, X2: torch.Tensor) -> SparseMatrix:
     Examples
     --------
 
-    >>> indices = torch.tensor([[1, 1, 2],
-    >>>                         [2, 3, 3]])
+    >>> indices = torch.tensor([[1, 1, 2], [2, 3, 3]])
     >>> val = torch.arange(1, 4).float()
     >>> A = dglsp.spmatrix(indices, val, (3, 4))
     >>> X1 = torch.randn(3, 5)
@@ -89,8 +88,7 @@ def bsddmm(A: SparseMatrix, X1: torch.Tensor, X2: torch.Tensor) -> SparseMatrix:
     Examples
     --------
 
-    >>> indices = torch.tensor([[1, 1, 2],
-    >>>                         [2, 3, 3]])
+    >>> indices = torch.tensor([[1, 1, 2], [2, 3, 3]])
     >>> val = torch.arange(1, 4).float()
     >>> A = dglsp.spmatrix(indices, val, (3, 4))
     >>> X1 = torch.arange(0, 3 * 5 * 2).view(3, 5, 2).float()

python/dgl/sparse/softmax.py

@@ -34,8 +34,7 @@ def softmax(input: SparseMatrix) -> SparseMatrix:
 
     Case1: matrix with values of shape (nnz)
 
-    >>> indices = torch.tensor([[0, 0, 1, 2],
-    >>>                         [1, 2, 2, 0]])
+    >>> indices = torch.tensor([[0, 0, 1, 2], [1, 2, 2, 0]])
     >>> nnz = len(row)
     >>> val = torch.arange(nnz).float()
     >>> A = dglsp.spmatrix(indices, val)
@@ -47,8 +46,7 @@ def softmax(input: SparseMatrix) -> SparseMatrix:
 
     Case2: matrix with values of shape (nnz, D)
 
-    >>> indices = torch.tensor([[0, 0, 1, 2],
-    >>>                         [1, 2, 2, 0]])
+    >>> indices = torch.tensor([[0, 0, 1, 2], [1, 2, 2, 0]])
     >>> val = torch.tensor([[0., 7.], [1., 3.], [2., 2.], [3., 1.]])
     >>> A = dglsp.spmatrix(indices, val)
     >>> dglsp.softmax(A)

python/dgl/sparse/sparse_matrix.py

@@ -108,8 +108,7 @@ class SparseMatrix:
         Examples
         --------
 
-        >>> indices = torch.tensor([[1, 2, 1],
-        >>>                         [2, 4, 3]])
+        >>> indices = torch.tensor([[1, 2, 1], [2, 4, 3]])
         >>> A = from_coo(dst, src)
         >>> A.coo()
         (tensor([1, 2, 1]), tensor([2, 4, 3]))
@@ -140,8 +139,7 @@ class SparseMatrix:
         Examples
         --------
 
-        >>> indices = torch.tensor([[1, 2, 1],
-        >>>                         [2, 4, 3]])
+        >>> indices = torch.tensor([[1, 2, 1], [2, 4, 3]])
         >>> A = from_coo(dst, src)
         >>> A.csr()
         (tensor([0, 0, 2, 3]), tensor([2, 3, 4]), tensor([0, 2, 1]))
@@ -172,8 +170,7 @@ class SparseMatrix:
         Examples
         --------
 
-        >>> indices = torch.tensor([[1, 2, 1],
-        >>>                         [2, 4, 3]])
+        >>> indices = torch.tensor([[1, 2, 1], [2, 4, 3]])
         >>> A = from_coo(dst, src)
         >>> A.csc()
         (tensor([0, 0, 0, 1, 2, 3]), tensor([1, 1, 2]), tensor([0, 2, 1]))
@@ -215,8 +212,7 @@ class SparseMatrix:
         Examples
         --------
 
-        >>> indices = torch.tensor([[1, 1, 3],
-        >>>                         [2, 1, 3]])
+        >>> indices = torch.tensor([[1, 1, 3], [2, 1, 3]])
         >>> val = torch.tensor([1, 1, 2])
         >>> A = dglsp.spmatrix(indices, val)
         >>> A = A.transpose()
@@ -248,8 +244,7 @@ class SparseMatrix:
         Examples
         --------
 
-        >>> indices = torch.tensor([[1, 1, 2],
-        >>>                         [1, 2, 0]])
+        >>> indices = torch.tensor([[1, 1, 2], [1, 2, 0]])
         >>> A = dglsp.spmatrix(indices, shape=(3, 4))
         >>> A.to(device='cuda:0', dtype=torch.int32)
         SparseMatrix(indices=tensor([[1, 1, 2],
@@ -289,8 +284,7 @@ class SparseMatrix:
         Examples
         --------
 
-        >>> indices = torch.tensor([[1, 1, 2],
-        >>>                         [1, 2, 0]])
+        >>> indices = torch.tensor([[1, 1, 2], [1, 2, 0]])
         >>> A = dglsp.spmatrix(indices, shape=(3, 4))
         >>> A.cuda()
         SparseMatrix(indices=tensor([[1, 1, 2],
@@ -312,8 +306,7 @@ class SparseMatrix:
         Examples
         --------
 
-        >>> indices = torch.tensor([[1, 1, 2],
-        >>>                         [1, 2, 0]]).to("cuda")
+        >>> indices = torch.tensor([[1, 1, 2], [1, 2, 0]]).to("cuda")
         >>> A = dglsp.spmatrix(indices, shape=(3, 4))
         >>> A.cpu()
         SparseMatrix(indices=tensor([[1, 1, 2],
@@ -335,8 +328,7 @@ class SparseMatrix:
         Examples
         --------
 
-        >>> indices = torch.tensor([[1, 1, 2],
-        >>>                         [1, 2, 0]])
+        >>> indices = torch.tensor([[1, 1, 2], [1, 2, 0]])
         >>> val = torch.ones(len(row)).long()
         >>> A = dglsp.spmatrix(indices, val, shape=(3, 4))
         >>> A.float()
@@ -359,8 +351,7 @@ class SparseMatrix:
         Examples
         --------
 
-        >>> indices = torch.tensor([[1, 1, 2],
-        >>>                         [1, 2, 0]])
+        >>> indices = torch.tensor([[1, 1, 2], [1, 2, 0]])
         >>> A = dglsp.spmatrix(indices, shape=(3, 4))
         >>> A.double()
         SparseMatrix(indices=tensor([[1, 1, 2],
@@ -382,8 +373,7 @@ class SparseMatrix:
         Examples
         --------
 
-        >>> indices = torch.tensor([[1, 1, 2],
-        >>>                         [1, 2, 0]])
+        >>> indices = torch.tensor([[1, 1, 2], [1, 2, 0]])
         >>> A = dglsp.spmatrix(indices, shape=(3, 4))
         >>> A.int()
         SparseMatrix(indices=tensor([[1, 1, 2],
@@ -405,8 +395,7 @@ class SparseMatrix:
         Examples
         --------
 
-        >>> indices = torch.tensor([[1, 1, 2],
-        >>>                         [1, 2, 0]])
+        >>> indices = torch.tensor([[1, 1, 2], [1, 2, 0]])
         >>> A = dglsp.spmatrix(indices, shape=(3, 4))
         >>> A.long()
         SparseMatrix(indices=tensor([[1, 1, 2],
@@ -436,8 +425,7 @@ class SparseMatrix:
 
         Examples
         --------
-        >>> indices = torch.tensor([[1, 0, 0, 0, 1],
-        >>>                         [1, 1, 1, 2, 2]])
+        >>> indices = torch.tensor([[1, 0, 0, 0, 1], [1, 1, 1, 2, 2]])
         >>> val = torch.tensor([0, 1, 2, 3, 4])
         >>> A = dglsp.spmatrix(indices, val)
         >>> A.coalesce()
@@ -453,8 +441,7 @@ class SparseMatrix:
 
         Examples
         --------
-        >>> indices = torch.tensor([[1, 0, 0, 0, 1],
-        >>>                         [1, 1, 1, 2, 2]])
+        >>> indices = torch.tensor([[1, 0, 0, 0, 1], [1, 1, 1, 2, 2]])
         >>> val = torch.tensor([0, 1, 2, 3, 4])
         >>> A = dglsp.spmatrix(indices, val)
         >>> A.has_duplicate()
@@ -512,8 +499,7 @@ def spmatrix(
 
     Case2: Sparse matrix with scalar values.
 
-    >>> indices = torch.tensor([[1, 1, 2],
-    >>>                         [2, 4, 3]])
+    >>> indices = torch.tensor([[1, 1, 2], [2, 4, 3]])
     >>> val = torch.tensor([[1.], [2.], [3.]])
     >>> A = dglsp.spmatrix(indices, val)
     SparseMatrix(indices=tensor([[1, 1, 2],
@@ -575,8 +561,7 @@ def from_coo(
 
     Case1: Sparse matrix with row and column indices without values.
 
-    >>> indices = torch.tensor([[1, 1, 2],
-    >>>                         [2, 4, 3]])
+    >>> indices = torch.tensor([[1, 1, 2], [2, 4, 3]])
     >>> A = dglsp.spmatrix(indices)
     SparseMatrix(indices=tensor([[1, 1, 2],
                                  [2, 4, 3]]),
@@ -591,8 +576,7 @@ def from_coo(
 
     Case2: Sparse matrix with scalar values.
 
-    >>> indices = torch.tensor([[1, 1, 2],
-    >>>                         [2, 4, 3]])
+    >>> indices = torch.tensor([[1, 1, 2], [2, 4, 3]])
     >>> val = torch.tensor([[1.], [2.], [3.]])
     >>> A = dglsp.spmatrix(indices, val)
     SparseMatrix(indices=tensor([[1, 1, 2],
@@ -604,8 +588,7 @@ def from_coo(
 
     Case3: Sparse matrix with vector values.
 
-    >>> indices = torch.tensor([[1, 1, 2],
-    >>>                         [2, 4, 3]])
+    >>> indices = torch.tensor([[1, 1, 2], [2, 4, 3]])
     >>> val = torch.tensor([[1., 1.], [2., 2.], [3., 3.]])
     >>> A = dglsp.spmatrix(indices, val)
     SparseMatrix(indices=tensor([[1, 1, 2],
@@ -828,8 +811,7 @@ def val_like(mat: SparseMatrix, val: torch.Tensor) -> SparseMatrix:
     Examples
     --------
 
-    >>> indices = torch.tensor([[1, 1, 2],
-    >>>                         [2, 4, 3]])
+    >>> indices = torch.tensor([[1, 1, 2], [2, 4, 3]])
     >>> val = torch.ones(3)
     >>> A = dglsp.spmatrix(indices, val)
     >>> A = dglsp.val_like(A, torch.tensor([2, 2, 2]))

python/dgl/sparse/unary_op_sp.py

@@ -14,8 +14,7 @@ def neg(A: SparseMatrix) -> SparseMatrix:
     Examples
     --------
 
-    >>> indices = torch.tensor([[1, 1, 3],
-    >>>                         [1, 2, 3]])
+    >>> indices = torch.tensor([[1, 1, 3], [1, 2, 3]])
     >>> val = torch.tensor([1., 1., 2.])
     >>> A = dglsp.spmatrix(indices, val)
     >>> A = -A