[Sparse] Swap examples of from_coo and spmatrix. (#5228)

* swap-example

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

python/dgl/sparse/sparse_matrix.py

@@ -484,15 +484,14 @@ def spmatrix(
 
     Case1: Sparse matrix with row and column indices without values.
 
-    >>> dst = torch.tensor([1, 1, 2])
-    >>> src = torch.tensor([2, 4, 3])
-    >>> A = dglsp.from_coo(dst, src)
+    >>> indices = torch.tensor([[1, 1, 2], [2, 4, 3]])
+    >>> A = dglsp.spmatrix(indices)
     SparseMatrix(indices=tensor([[1, 1, 2],
                                  [2, 4, 3]]),
                  values=tensor([1., 1., 1.]),
                  shape=(3, 5), nnz=3)
     >>> # Specify shape
-    >>> A = dglsp.from_coo(dst, src, shape=(5, 5))
+    >>> A = dglsp.spmatrix(indices, shape=(5, 5))
     SparseMatrix(indices=tensor([[1, 1, 2],
                                  [2, 4, 3]]),
                  values=tensor([1., 1., 1.]),
@@ -512,10 +511,9 @@ def spmatrix(
 
     Case3: Sparse matrix with vector values.
 
-    >>> dst = torch.tensor([1, 1, 2])
-    >>> src = torch.tensor([2, 4, 3])
+    >>> indices = torch.tensor([[1, 1, 2], [2, 4, 3]])
     >>> val = torch.tensor([[1., 1.], [2., 2.], [3., 3.]])
-    >>> A = dglsp.from_coo(dst, src, val)
+    >>> A = dglsp.spmatrix(indices, val)
     SparseMatrix(indices=tensor([[1, 1, 2],
                                  [2, 4, 3]]),
                  values=tensor([[1., 1.],
@@ -562,14 +560,15 @@ def from_coo(
 
     Case1: Sparse matrix with row and column indices without values.
 
-    >>> indices = torch.tensor([[1, 1, 2], [2, 4, 3]])
-    >>> A = dglsp.spmatrix(indices)
+    >>> dst = torch.tensor([1, 1, 2])
+    >>> src = torch.tensor([2, 4, 3])
+    >>> A = dglsp.from_coo(dst, src)
     SparseMatrix(indices=tensor([[1, 1, 2],
                                  [2, 4, 3]]),
                  values=tensor([1., 1., 1.]),
                  shape=(3, 5), nnz=3)
     >>> # Specify shape
-    >>> A = dglsp.spmatrix(indices, shape=(5, 5))
+    >>> A = dglsp.from_coo(dst, src, shape=(5, 5))
     SparseMatrix(indices=tensor([[1, 1, 2],
                                  [2, 4, 3]]),
                  values=tensor([1., 1., 1.]),
@@ -585,13 +584,14 @@ def from_coo(
                  values=tensor([[1.],
                                 [2.],
                                 [3.]]),
-                 size=(3, 5), nnz=3, val_size=(1,))
+                 shape=(3, 5), nnz=3, val_size=(1,))
 
     Case3: Sparse matrix with vector values.
 
-    >>> indices = torch.tensor([[1, 1, 2], [2, 4, 3]])
+    >>> dst = torch.tensor([1, 1, 2])
+    >>> src = torch.tensor([2, 4, 3])
     >>> val = torch.tensor([[1., 1.], [2., 2.], [3., 3.]])
-    >>> A = dglsp.spmatrix(indices, val)
+    >>> A = dglsp.from_coo(dst, src, val)
     SparseMatrix(indices=tensor([[1, 1, 2],
                                  [2, 4, 3]]),
                  values=tensor([[1., 1.],