Fix most of the remaining warning for the doc.

doc/library/sandbox/linalg.txt

 ..  ../../../../theano/sandbox/linalg/ops.py
 ..  ../../../../theano/sandbox/linalg
 
-.. _libdoc_linalg:
+.. _libdoc_sandbox_linalg:
 
 ===================================================================
 :mod:`sandbox.linalg` --  Linear Algebra Ops

doc/library/tensor/nnet/conv.txt

@@ -32,18 +32,20 @@ TODO: Give examples on how to use these things! They are pretty complicated.
       Most of the more efficient GPU implementations listed below can be used
       as an automatic replacement for nnet.conv2d by enabling specific graph
       optimizations.
-    - :func:`conv2d_fft <theano.sandbox.cuda.fftconv.conv2d_fft>`
-      This is a GPU-only version of nnet.conv2d that uses an FFT transform
-      to perform the work. conv2d_fft should not be called directly as it
-      does not provide a gradient. Instead, use nnet.conv2d and allow
-      Theano's graph optimizer to replace it by the FFT version by setting
-      ``THEANO_FLAGS=optimizer_including=conv_fft_valid:conv_fft_full``
+    - :func:`conv2d_fft <theano.sandbox.cuda.fftconv.conv2d_fft>` This
+      is a GPU-only version of nnet.conv2d that uses an FFT transform
+      to perform the work. conv2d_fft should not be used directly as
+      it does not provide a gradient. Instead, use nnet.conv2d and
+      allow Theano's graph optimizer to replace it by the FFT version
+      by setting
+      'THEANO_FLAGS=optimizer_including=conv_fft_valid:conv_fft_full'
       in your environement.  This is not enabled by default because it
-      has some restrictions on input and uses a lot more memory.  Also note
-      that it requires CUDA >= 5.0, scikits.cuda >= 0.5.0 and PyCUDA to run.
-      To deactivate the FFT optimization on a specific nnet.conv2d
-      while the optimization flags are active, you can set its ``version``
-      parameter to ``'no_fft'``. To enable it for just one Theano function:
+      has some restrictions on input and uses a lot more memory.  Also
+      note that it requires CUDA >= 5.0, scikits.cuda >= 0.5.0 and
+      PyCUDA to run.  To deactivate the FFT optimization on a specific
+      nnet.conv2d while the optimization flags are active, you can set
+      its ``version`` parameter to ``'no_fft'``. To enable it for just
+      one Theano function:
 
       .. code-block:: python
 

doc/library/tensor/slinalg.txt

 ..  ../../../../theano/sandbox/slinalg.py
 
-.. _libdoc_linalg:
+.. _libdoc_slinalg:
 
 ===================================================================
 :mod:`tensor.slinalg` --  Linear Algebra Ops Using Scipy

theano/tensor/nnet/Conv3D.py

@@ -533,31 +533,33 @@ class Conv3D(theano.Op):
 
         return strutil.render_string(codeSource,locals())
 
+_conv3D = Conv3D()
 
-conv3D = Conv3D()
-"""
-3D "convolution" of multiple filters on a minibatch
-(does not flip the kernel, moves kernel with a user specified stride)
-
-:param V: Visible unit, input.
-    dimensions: (batch, row, column, time, in channel)
-:param W: Weights, filter.
-    dimensions: (out channel, row, column, time ,in channel)
-:param b: bias, shape == (W.shape[0],)
-:param d: strides when moving the filter over the input(dx, dy, dt)
-
-:note: The order of dimensions does not correspond to the one in `conv2d`.
-       This is for optimization.
-
-:note: The GPU implementation is very slow. You should use
-    :func:`conv3d2d <theano.tensor.nnet.conv3d2d.conv3d>` for a GPU
-    graph instead.
-
-:see: Someone made a script that shows how to swap the axes between
-      both 3d convolution implementations in Theano. See the last
-      `attachment <https://groups.google.com/d/msg/theano-users/1S9_bZgHxVw/0cQR9a4riFUJ>`_.
-
+def conv3D(V, W, b, d):
+    """
+    3D "convolution" of multiple filters on a minibatch
+    (does not flip the kernel, moves kernel with a user specified stride)
+
+    :param V: Visible unit, input.
+        dimensions: (batch, row, column, time, in channel)
+    :param W: Weights, filter.
+        dimensions: (out channel, row, column, time ,in channel)
+    :param b: bias, shape == (W.shape[0],)
+    :param d: strides when moving the filter over the input(dx, dy, dt)
+
+    :note: The order of dimensions does not correspond to the one in `conv2d`.
+           This is for optimization.
+
+    :note: The GPU implementation is very slow. You should use
+           :func:`conv3d2d <theano.tensor.nnet.conv3d2d.conv3d>` for a
+           GPU graph instead.
+
+    :see: Someone made a script that shows how to swap the axes
+          between both 3d convolution implementations in Theano. See
+          the last `attachment
+          <https://groups.google.com/d/msg/theano-users/1S9_bZgHxVw/0cQR9a4riFUJ>`_.
 """
+    return _conv3D(V, W, b, d)
 
 def computeH(V,W,b,d):
     assert len(W.shape) == 5