sloving test failures

doc/extending/extending_theano.txt

@@ -350,7 +350,7 @@ Example: Op definition
                 return eval_points
             return self.grad(inputs, eval_points)
 
-        doubleOp2 = DoubleOp2()
+    doubleOp2 = DoubleOp2()
 
 At a high level, the code fragment declares a class (e.g., ``DoubleOp1``) and then 
 creates one instance of it (e.g., ``doubleOp1``).
@@ -412,8 +412,9 @@ but it must not influence the semantics of the Op output.
 
 You can try the new Op as follows:
 
-.. testcode:: example(Using make_node)
+.. testcode:: example
 
+    import theano
     x = theano.tensor.matrix()
     f = theano.function([x], DoubleOp1()(x))
     import numpy
@@ -427,9 +428,6 @@ You can try the new Op as follows:
    :hide:
    :options: +ELLIPSIS
 
-   ...
-   ...
-
 .. code-block:: none
 
     [[ 0.08257206  0.34308357  0.5288043   0.06582951]
@@ -443,8 +441,9 @@ You can try the new Op as follows:
      [ 1.5465443   1.30803715  1.53125983  1.88291403]
      [ 1.6904152   0.61000201  1.76861002  1.9163731 ]]
 
-.. testcode:: example (Using itypes and otypes)
+.. testcode:: example
 
+    import theano
     x = theano.tensor.matrix()
     f = theano.function([x], DoubleOp2()(x))
     import numpy
@@ -459,9 +458,6 @@ You can try the new Op as follows:
    :hide:
    :options: +ELLIPSIS
 
-   ...
-   ...
-
 .. code-block:: none
 
     [[ 0.02443785  0.67833979  0.91954769  0.95444365]
@@ -503,8 +499,8 @@ and ``b`` are equal.
 
         def make_node(self, x):
             # check that the theano version has support for __props__.
-            assert hasattr(self, '_props'), "Your version of theano is too old 
-            to support __props__."
+            assert hasattr(self, '_props'), ("Your version of theano is too"
+                                             "old to support __props__.")
             x = theano.tensor.as_tensor_variable(x)
             return theano.Apply(self, [x], [x.type()])
 

doc/tutorial/examples.txt

@@ -116,7 +116,7 @@ one. You can do it like this:
 >>> from theano import function
 >>> x, y = T.dscalars('x', 'y')
 >>> z = x + y
->>> f = function([x, In(y, default=1)], z)
+>>> f = function([x, In(y, value=1)], z)
 >>> f(33)
 array(34.0)
 >>> f(33, 2)
@@ -125,7 +125,7 @@ array(35.0)
 This makes use of the :ref:`In <function_inputs>` class which allows
 you to specify properties of your function's parameters with greater detail. Here we
 give a default value of 1 for *y* by creating a ``In`` instance with
-its ``default`` field set to 1.
+its ``value`` field set to 1.
 
 Inputs with default values must follow inputs without default
 values (like Python's functions).  There can be multiple inputs with default values. These parameters can
@@ -137,7 +137,7 @@ be set positionally or by name, as in standard Python:
 
 >>> x, y, w = T.dscalars('x', 'y', 'w')
 >>> z = (x + y) * w
->>> f = function([x, In(y, default=1), In(w, default=2, name='w_by_name')], z)
+>>> f = function([x, In(y, value=1), In(w, value=2, name='w_by_name')], z)
 >>> f(33)
 array(68.0)
 >>> f(33, 2)
@@ -154,8 +154,8 @@ array(33.0)
    that are passed as arguments.  The symbolic variable objects have name
    attributes (set by ``dscalars`` in the example above) and *these* are the
    names of the keyword parameters in the functions that we build.  This is
-   the mechanism at work in ``In(y, default=1)``.  In the case of ``In(w,
-   default=2, name='w_by_name')``. We override the symbolic variable's name
+   the mechanism at work in ``In(y, value=1)``.  In the case of ``In(w,
+   value=2, name='w_by_name')``. We override the symbolic variable's name
    attribute with a name to be used for this function.
 
 

doc/tutorial/profiling.txt

@@ -33,7 +33,7 @@ functions using either of the following two options:
     - You can also combine the profile of many functions:
 
       .. testcode::
-
+          :hide:
           profile = theano.compile.ProfileStats()
           f = theano.function(..., profile=profile)
           g = theano.function(..., profile=profile)