Fix flake8 errors in gpuarray optimizations

theano/gpuarray/opt.py

@@ -162,6 +162,7 @@ def register_inplace(*tags, **kwargs):
         return local_opt
     return f
 
+
 register_opt('fast_compile')(theano.tensor.opt.local_track_shape_i)
 register_opt(final_opt=True, name='gpua_constant_folding')(
     tensor.opt.constant_folding)
@@ -582,6 +583,7 @@ def local_cut_gpu_transfers(node):
                 else:
                     return [node.op(n2.inputs[0])]
 
+
 gpu_cut_copies.register('cut_gpua_host_transfers', local_cut_gpu_transfers,
                         'fast_compile', 'fast_run', 'gpuarray')
 gpu_cut_copies.register('cut_gpua_constant_transfers',
@@ -652,6 +654,8 @@ def local_gpua_alloc_empty_to_zeros(node):
         z = np.asarray(0, dtype=node.outputs[0].dtype)
         return [GpuAlloc(context_name)(as_gpuarray_variable(z, context_name),
                                        *node.inputs)]
+
+
 optdb.register('local_gpua_alloc_empty_to_zeros',
                theano.tensor.opt.in2out(local_gpua_alloc_empty_to_zeros),
                # After move to gpu and merge2, before inplace.
@@ -1521,6 +1525,8 @@ def local_conv_gpu_conv(node):
                 return [tensor.as_tensor_variable(out)]
             else:
                 return [out]
+
+
 register_opt()(local_conv_gpu_conv)
 
 
@@ -1792,6 +1798,8 @@ def local_gpu_pool(op, ctx_name, inputs, outputs):
         inp_padded = pad_dims(inp, 2, nd)
         ret_padded = op(inp_padded, ws, stride, pad)
         return unpad_dims(ret_padded, inp, 2, nd)
+
+
 pool_db = LocalGroupDB()
 pool_db2 = LocalGroupDB(local_opt=theano.gof.opt.GraphToGPULocalOptGroup)
 pool_db2.__name__ = "pool_db2"
@@ -1829,6 +1837,8 @@ def local_gpu_max_pool_grad(op, ctx_name, inputs, outputs):
         ret_padded = op(inp_padded, out_padded, out_grad_padded,
                         ws, stride, pad)
         return unpad_dims(ret_padded, inp, 2, nd)
+
+
 lifter = op_lifter([pool.MaxPoolGrad])(local_gpu_max_pool_grad)
 pool_db.register("local_gpu_max_pool_grad", lifter,
                  'gpuarray', 'fast_compile', 'fast_run',
@@ -1859,6 +1869,8 @@ def local_gpu_average_pool_grad(op, ctx_name, inputs, outputs):
         ret_padded = op(inp_padded, out_grad_padded,
                         ws, stride, pad)
         return unpad_dims(ret_padded, inp, 2, nd)
+
+
 lifter = op_lifter([pool.AveragePoolGrad])(local_gpu_average_pool_grad)
 pool_db.register("local_gpu_average_pool_grad", lifter,
                  'gpuarray', 'fast_compile', 'fast_run',
@@ -1956,6 +1968,7 @@ def local_assert_no_cpu_op(node):
             elif config.assert_no_cpu_op == "pdb":
                 pdb.set_trace()
 
+
 # Register the local_assert_no_cpu_op:
 assert_no_cpu_op = theano.tensor.opt.in2out(local_assert_no_cpu_op,
                                             name='assert_no_cpu_op')
@@ -2280,15 +2293,17 @@ def local_gpu_magma_svd(op, context_name, inputs, outputs):
             out = [out.astype('float16')]
     return out
 
+
 @register_opt('ctc', 'fast_compile')
 @op_lifter([theano.tensor.nnet.ctc.ConnectionistTemporalClassification])
-@register_opt2([theano.tensor.nnet.ctc.ConnectionistTemporalClassification], 'ctc', 'fast_compile')
+@register_opt2([ConnectionistTemporalClassification], 'ctc', 'fast_compile')
 def local_gpu_ctc(op, context_name, inputs, outputs):
     if not config.ctc.enabled:
         return
     op = GpuConnectionistTemporalClassification(compute_grad=op.compute_grad)
     return list(op(*inputs))
 
+
 # Do not register in fast_run or fast_compile.
 # It will be added to fast_run if the GPU is enabled.
 optdb.register('gpua_scanOp_make_inplace',