linting

README.md

@@ -112,7 +112,7 @@ Transposes dimensions 0 and 1 of a sparse matrix.
 * **value** *(Tensor)* - The value tensor of sparse matrix.
 * **m** *(int)* - The first dimension of corresponding dense matrix.
 * **n** *(int)* - The second dimension of corresponding dense matrix.
-* **coalesce** *(bool, optional)* - To return coalesced index and value or not (default: `True`)
+* **coalesced** *(bool, optional)* - If set to `False`, will not coalesce the output (default: `True`)
 
 ### Returns
 

torch_sparse/spspmm.py

@@ -17,8 +17,8 @@ def spspmm(indexA, valueA, indexB, valueB, m, k, n):
         indexB (:class:`LongTensor`): The index tensor of second sparse matrix.
         valueB (:class:`Tensor`): The value tensor of second sparse matrix.
         m (int): The first dimension of first corresponding dense matrix.
-        k (int): The second dimension of first corresponding dense matrix and first
-            dimension of second corresponding dense matrix.
+        k (int): The second dimension of first corresponding dense matrix and
+            first dimension of second corresponding dense matrix.
         n (int): The second dimension of second corresponding dense matrix.
 
     :rtype: (:class:`LongTensor`, :class:`Tensor`)

torch_sparse/transpose.py

@@ -2,7 +2,7 @@ import torch
 from torch_sparse import to_scipy, from_scipy, coalesce
 
 
-def transpose(index, value, m, nm coalesce=True):
+def transpose(index, value, m, n, coalesced=True):
     """Transposes dimensions 0 and 1 of a sparse tensor.
 
     Args:
@@ -10,7 +10,8 @@ def transpose(index, value, m, nm coalesce=True):
         value (:class:`Tensor`): The value tensor of sparse matrix.
         m (int): The first dimension of corresponding dense matrix.
         n (int): The second dimension of corresponding dense matrix.
-        coalesce (bool, optional): To return coalesced index and value or not (default: :obj:`True`) 
+        coalesced (bool, optional): If set to :obj:`False`, will not coalesce
+            the output (default: :obj:`True`)
     :rtype: (:class:`LongTensor`, :class:`Tensor`)
     """