Lint

src/pystencils_autodiff/backends/_pytorch.py

@@ -100,7 +100,9 @@ def tensor_to_gpuarray(tensor):
         raise ValueError(
             'Cannot convert CPU tensor to GPUArray (call `cuda()` on it)')
     else:
-        return pycuda.gpuarray.GPUArray(tensor.shape, dtype=torch_dtype_to_numpy(tensor.dtype), gpudata=tensor.data_ptr())
+        return pycuda.gpuarray.GPUArray(tensor.shape,
+                                        dtype=torch_dtype_to_numpy(tensor.dtype),
+                                        gpudata=tensor.data_ptr())
 
 
 def gpuarray_to_tensor(gpuarray, context=None):

src/pystencils_autodiff/backends/_tensorflow_cpp.py

@@ -6,7 +6,8 @@ Advantages:
 - Custom Ops will be serializable
 
 Disadavantages:
-- C++ Code has to be build with correct parameters and ABI for present Tensorflow version (best integrated into Tensorflow build)
+- C++ Code has to be build with correct parameters and ABI
+for present Tensorflow version (best integrated into Tensorflow build)
 
 """
 

src/pystencils_autodiff/lbm/__init__.py

 from lbmpy.autodiff._autodiff_lbstep import AutoDiffLatticeBoltzmannStep
 from lbmpy.autodiff.adjoint_boundaryconditions import AdjointBoundaryCondition
+
+__all__ = ['AutoDiffLatticeBoltzmannStep', 'AdjointBoundaryCondition']

src/pystencils_autodiff/tensorflow.py

@@ -24,5 +24,3 @@ def tf_variable_from_field(field, init_val=0, constraint=None):
 
 def tf_placeholder_from_field(field):
     return tf.placeholder(dtype=field.dtype.numpy_dtype, name=field.name + '_placeholder', shape=field.shape)
-
-