pep8

theano/sandbox/cuda/dnn.py

@@ -738,7 +738,8 @@ if (err%(name)s != CUDNN_STATUS_SUCCESS) {
                cudnnGetErrorString(err%(name)s));
   %(fail)s
 }
-""" % dict(output_grad=out_grad, desc=desc, fail=sub['fail'], id=sub['struct_id'],
+""" % dict(output_grad=out_grad, desc=desc,
+           fail=sub['fail'], id=sub['struct_id'],
            name=name, set_in=set_in,
            set_out=set_out, input=inp, input_grad=inp_grad, output=out,
            input_desc="input"+str(sub['struct_id']),
@@ -943,7 +944,6 @@ if cuda_available:
 
     gpu_optimizer.register("conv_cudnn", local_conv_dnn, 'cudnn')
 
-
     @local_optimizer([GpuDownsampleFactorMax])
     def local_pool_dnn(node):
         if isinstance(node.op, GpuDownsampleFactorMax):
@@ -955,7 +955,6 @@ if cuda_available:
 
     gpu_optimizer.register("pool_cudnn", local_pool_dnn, 'cudnn')
 
-
     @local_optimizer([GpuDownsampleFactorMaxGrad])
     def local_pool_dnn_grad(node):
         if isinstance(node.op, GpuDownsampleFactorMaxGrad):
@@ -967,17 +966,18 @@ if cuda_available:
             desc = GpuDnnPoolDesc(ws=ds, stride=ds, mode="max")()
 
             return [GpuDnnPoolGrad()(gpu_contiguous(inp),
-                gpu_contiguous(inp_grad), gpu_contiguous(out), desc)]
+                                     gpu_contiguous(inp_grad),
+                                     gpu_contiguous(out), desc)]
 
     gpu_optimizer.register("pool_cudnn_grad", local_pool_dnn_grad, 'cudnn')
 
-
     @local_optimizer([GpuSoftmax])
     def local_softmax_dnn(node):
         raise_no_dnn()
         if isinstance(node.op, GpuSoftmax):
             ins = node.inputs[0].dimshuffle(0, 1, 'x', 'x')
-            out = GpuDnnSoftmax('bc01', 'accurate', 'channel')(gpu_contiguous(ins))
+            ins = gpu_contiguous(ins)
+            out = GpuDnnSoftmax('bc01', 'accurate', 'channel')(ins)
             out = as_cuda_ndarray_variable(out.dimshuffle(0, 1))
             return [out]