All cosmeticomments fixed

theano/tensor/basic.py

@@ -49,6 +49,9 @@ continuous_dtypes = map(str, scal.continuous_types)
 discrete_dtypes = map(str, scal.discrete_types)
 all_dtypes = map(str, scal.all_types)
 
+# Do a lazy import of the sparse module
+sparse_module_ref = None
+
 
 class ShapeError(Exception):
     """Raised when the shape cannot be computed."""
@@ -619,7 +622,7 @@ class TensorType(Type):
     Inf entries. (Used in `DebugMode`)
     """
 
-    def __init__(self, dtype, broadcastable, name=None, sparsegrad=False):
+    def __init__(self, dtype, broadcastable, name=None, sparse_grad=False):
         """Initialize self.dtype and self.broadcastable.
 
         :Parameters:
@@ -644,7 +647,7 @@ class TensorType(Type):
         self.dtype_specs()  # error checking is done there
         self.name = name
         self.numpy_dtype = numpy.dtype(self.dtype)
-        self.sparsegrad = sparsegrad 
+        self.sparse_grad = sparse_grad 
 
     def filter(self, data, strict=False, allow_downcast=None):
         """Convert `data` to something which can be associated to a
@@ -6521,11 +6524,14 @@ class AdvancedSubtensor1(Op):
         return rval
 
     def grad(self, inputs, grads):
-
+        global sparse_module_ref
         gz, = grads
         assert len(inputs) == 2
-        if inputs[0].type.sparsegrad:
-           rval1 = [theano.sparse.ConstructSparseFromList()((inputs[0]), gz, inputs[1])]
+        if inputs[0].type.sparse_grad:
+            if sparse_module_ref is None:
+                import theano.sparse as sparse_module_ref
+
+            rval1 = [sparse_module_ref.ConstructSparseFromList()((inputs[0]), gz, inputs[1])]
         else:
            rval1 = [advanced_inc_subtensor1(zeros_like(inputs[0]), gz, inputs[1])]
         return rval1 + [DisconnectedType()()] * (len(inputs) - 1)