Added stacklist from gh-1335 in the doc of Theano.

doc/library/tensor/basic.txt

@@ -646,6 +646,35 @@ dimensions, see :meth:`_tensor_py_operators.dimshuffle`.
     >>> x = T.concatenate([x0, x1[0], T.shape_padright(x2)], axis=1)
     >>> # x.ndim == 2
 
+.. function:: stacklist(tensor_list)
+
+    :type tensor_list: an iterable that contain tensors or iterable
+        with at the end tensors.
+    :param tensor_list: tensors to be
+        stackend together.
+
+    Recursivly stack lists of tensors to maintain similar structure.
+
+    This function can create a tensor from a shaped list of scalars
+
+    >>> from theano.tensor import stacklists, scalar, matrix
+    >>> from theano import function
+    >>> a,b,c,d = map(scalar, 'abcd')
+    >>> X = stacklists([[a, b], [c, d]])
+    >>> f = function([a, b, c, d], X)
+    >>> f(1, 2, 3, 4)
+    >>> # array([[ 1.,  2.], [ 3.,  4.]], dtype=float32)
+
+    We can also stack arbitrarily shaped tensors.  Here we stack matrices into
+    a 2 by 2 grid.
+
+    >>> from numpy import ones
+    >>> a,b,c,d, = [tensor.matrix(a) for a in 'abcd']
+    >>> X = stacklists([[a, b], [c, d]])
+    >>> f = function([a, b, c, d], X)
+    >>> x = ones((4, 4), 'float32')
+    >>> f(x, x, x, x).shape
+    >>> # (2, 2, 4, 4)
 
 Reductions
 ==========

theano/tensor/basic.py

@@ -8241,8 +8241,7 @@ def stacklists(arg):
     >>> from theano.tensor import stacklists, scalar, matrix
     >>> from theano import function
     >>> a,b,c,d = map(scalar, 'abcd')
-    >>> X = stacklists([[a, b],
-    ...                 [c, d]])
+    >>> X = stacklists([[a, b], [c, d]])
     >>> f = function([a, b, c, d], X)
     >>> f(1, 2, 3, 4)
     array([[ 1.,  2.],
@@ -8252,8 +8251,7 @@ def stacklists(arg):
     a 2 by 2 grid.
     >>> from numpy import ones
     >>> a,b,c,d, = [tensor.matrix(a) for a in 'abcd']
-    >>> X = stacklists([[a, b],
-    ...                 [c, d]])
+    >>> X = stacklists([[a, b], [c, d]])
     >>> f = function([a, b, c, d], X)
     >>> x = ones((4, 4), 'float32')
     >>> f(x, x, x, x).shape