Reorganize and comment TensorFromScalar.grad

theano/tensor/basic.py

@@ -1921,10 +1921,18 @@ class TensorFromScalar(Op):
     def grad(self, inp, grads):
         s, = inp
         dt, = grads
-        assert dt.type.dtype.find('float') != -1
-        if s.type.dtype.find('int') != -1:
+        if s.type.dtype in float_dtypes:
+            assert dt.type.dtype in float_dtypes
+            return [scalar_from_tensor(dt)]
+
+        # If the input dtype is an integer, then so is the output dtype,
+        # and the "zero" gradient can be represented in that int dtype.
+        # Currently, theano.grad insists that the dtype of the returned
+        # gradient has a float dtype, so we use floatX.
+        if s.type.dtype in discrete_dtypes:
             return [s.zeros_like().astype(theano.config.floatX)]
-        return [scalar_from_tensor(dt)]
+
+        raise NotImplementedError("grad not implemented for complex dtypes")
 
     def __str__(self):
         return self.__class__.__name__