linting

.travis.yml

@@ -57,7 +57,6 @@ jobs:
     - os: windows
       env: TORCH_VERSION=1.4.0 PYTHON_VERSION=3.8 IDX=cu101
 
-
 install:
   - source script/cuda.sh
   - source script/conda.sh

torch_sparse/cat.py

-from typing import List
+from typing import Optional, List
 
 import torch
 from torch_sparse.storage import SparseStorage
@@ -63,18 +63,10 @@ def cat(tensors: List[SparseTensor], dim: int) -> SparseTensor:
         if len(rowcounts) == len(tensors):
             rowcount = torch.cat(rowcounts, dim=0)
 
-        storage = SparseStorage(
-            row=row,
-            rowptr=rowptr,
-            col=col,
-            value=value,
-            sparse_sizes=sparse_sizes,
-            rowcount=rowcount,
-            colptr=None,
-            colcount=None,
-            csr2csc=None,
-            csc2csr=None,
-            is_sorted=True)
+        storage = SparseStorage(row=row, rowptr=rowptr, col=col, value=value,
+                                sparse_sizes=sparse_sizes, rowcount=rowcount,
+                                colptr=None, colcount=None, csr2csc=None,
+                                csc2csr=None, is_sorted=True)
         return tensors[0].from_storage(storage)
 
     elif dim == 1:
@@ -126,18 +118,10 @@ def cat(tensors: List[SparseTensor], dim: int) -> SparseTensor:
         if len(colcounts) == len(tensors):
             colcount = torch.cat(colcounts, dim=0)
 
-        storage = SparseStorage(
-            row=row,
-            rowptr=None,
-            col=col,
-            value=value,
-            sparse_sizes=sparse_sizes,
-            rowcount=None,
-            colptr=colptr,
-            colcount=colcount,
-            csr2csc=None,
-            csc2csr=None,
-            is_sorted=False)
+        storage = SparseStorage(row=row, rowptr=None, col=col, value=value,
+                                sparse_sizes=sparse_sizes, rowcount=None,
+                                colptr=colptr, colcount=colcount, csr2csc=None,
+                                csc2csr=None, is_sorted=False)
         return tensors[0].from_storage(storage)
 
     elif dim > 1 and dim < tensors[0].dim():
@@ -251,16 +235,8 @@ def cat_diag(tensors: List[SparseTensor]) -> SparseTensor:
     if len(csc2csrs) == len(tensors):
         csc2csr = torch.cat(csc2csrs, dim=0)
 
-    storage = SparseStorage(
-        row=row,
-        rowptr=rowptr,
-        col=col,
-        value=value,
-        sparse_sizes=sparse_sizes,
-        rowcount=rowcount,
-        colptr=colptr,
-        colcount=colcount,
-        csr2csc=csr2csc,
-        csc2csr=csc2csr,
-        is_sorted=True)
+    storage = SparseStorage(row=row, rowptr=rowptr, col=col, value=value,
+                            sparse_sizes=sparse_sizes, rowcount=rowcount,
+                            colptr=colptr, colcount=colcount, csr2csc=csr2csc,
+                            csc2csr=csc2csr, is_sorted=True)
     return tensors[0].from_storage(storage)