MAINT: lint

Author: ev-br

torch_np/_detail/implementations.py

@@ -618,6 +618,7 @@ def _flatten(tensor, order="C"):
 
 # ### swap/move/roll axis ###
 
+
 def moveaxis(tensor, source, destination):
     source = _util.normalize_axis_tuple(source, tensor.ndim, "source")
     destination = _util.normalize_axis_tuple(destination, tensor.ndim, "destination")

torch_np/_funcs.py

@@ -1,7 +1,7 @@
 import torch
 
 from . import _decorators, _helpers
-from ._detail import _util, _flips
+from ._detail import _flips, _util
 from ._detail import implementations as _impl
 
 
@@ -120,54 +120,52 @@ def searchsorted(a, v, side="left", sorter=None):
 
 
 def moveaxis(a, source, destination):
-    tensor, = _helpers.to_tensors(a)
+    (tensor,) = _helpers.to_tensors(a)
     result = _impl.moveaxis(tensor, source, destination)
     return _helpers.array_from(result)
 
 
 def swapaxes(a, axis1, axis2):
-    tensor, = _helpers.to_tensors(a)
+    (tensor,) = _helpers.to_tensors(a)
     result = _flips.swapaxes(tensor, axis1, axis2)
     return _helpers.array_from(result)
 
 
 def rollaxis(a, axis, start=0):
-    tensor, = _helpers.to_tensors(a)
+    (tensor,) = _helpers.to_tensors(a)
     result = _flips.rollaxis(a, axis, start)
     return _helpers.array_from(result)
 
 
 # ### shape manipulations ###
 
+
 def squeeze(a, axis=None):
-    tensor, = _helpers.to_tensors(a)
+    (tensor,) = _helpers.to_tensors(a)
     result = _impl.squeeze(tensor, axis)
     return _helpers.array_from(result, a)
 
 
 def reshape(a, newshape, order="C"):
-    tensor, = _helpers.to_tensors(a)    
+    (tensor,) = _helpers.to_tensors(a)
     result = _impl.reshape(tensor, newshape, order=order)
     return _helpers.array_from(result, a)
 
 
 def transpose(a, axes=None):
-    tensor, = _helpers.to_tensors(a)    
+    (tensor,) = _helpers.to_tensors(a)
     result = _impl.transpose(tensor, axes)
     return _helpers.array_from(result, a)
 
 
 def ravel(a, order="C"):
-    tensor, = _helpers.to_tensors(a)    
+    (tensor,) = _helpers.to_tensors(a)
     result = _impl.ravel(tensor)
-    return _helpers.array_from(result, a)  
+    return _helpers.array_from(result, a)
 
 
 # leading underscore since arr.flatten exists but np.flatten does not
 def _flatten(a, order="C"):
-    tensor, = _helpers.to_tensors(a)    
+    (tensor,) = _helpers.to_tensors(a)
     result = _impl._flatten(tensor)
-    return _helpers.array_from(result, a)  
-
-
-
+    return _helpers.array_from(result, a)

torch_np/_helpers.py

@@ -75,6 +75,7 @@ def result_or_out(result_tensor, out_array=None, promote_scalar=False):
 
 def array_from(tensor, base=None):
     from ._ndarray import ndarray
+
     base = base if isinstance(base, ndarray) else None
     return ndarray._from_tensor_and_base(tensor, base)  # XXX: nuke .base
 

torch_np/_wrapper.py

@@ -110,8 +110,8 @@ def _concat_check(tup, dtype, out):
 
 @_decorators.dtype_to_torch
 def concatenate(ar_tuple, axis=0, out=None, dtype=None, casting="same_kind"):
-    _concat_check(ar_tuple, dtype, out=out)
     tensors = _helpers.to_tensors(*ar_tuple)
+    _concat_check(tensors, dtype, out=out)
     result = _impl.concatenate(tensors, axis, out, dtype, casting)
     return _helpers.result_or_out(result, out)