added comment

theano/sandbox/conv.py

@@ -438,6 +438,18 @@ using namespace std;
 
 def convolve2(kerns, kshp, nkern, images, imshp, bsize, step=(1,1),
               bias=None, mode='valid', **d):
+    """
+    param kerns: kernel tensor
+    param kshp:  tuple(kern row, kern wid)
+    param nkern: int the number of kernel
+    param images:image tensor
+    param imshp: tuple([stack size,] image row, image wid)
+    param bsize: batch size
+    param step:  subsampling to apply to the output tuple(row, wid)
+    param bias:  if True, will add a bias
+    param mode:  'valid' or 'full'
+    return:      tuple(theano graph with the output of ConvOp flattened to 2 dimensions, ?)
+    """
     #TODO: remove the bias argument from this function because convolution has nothing to do with a bias
 
     # if imshp, is a tuple, images contains one input dimension
@@ -461,7 +473,6 @@ def convolve2(kerns, kshp, nkern, images, imshp, bsize, step=(1,1),
     rval = tensor.flatten(convout, 2)
     return rval, N.hstack((nkern, convop.outshp))
 
-
 _conv_op_code_a = """
 const int mode=%(mode)s;
 int typenum=0, typenum_f=0;

theano/tensor/basic.py

@@ -2882,13 +2882,18 @@ def verify_grad(op, pt, n_tests=2, rng=None, eps=None, tol=None, mode=None, cast
     the given tolerance.
 
     :param op: something that behaves like an Op instance with a single output
-               (can be a python function combining multiple ops)
+               (can be a python function combining multiple ops, but see note below)
     :param pt: the list of numpy.ndarrays to use as inputs to the op
     :param n_tests: number of times to run the test
     :param rng: random number generator from which to draw random samples
     :param eps: stepsize used in the Finite Difference Method (Default None is type-dependent)
     :param tol: relative tolerance used as threshold for gradient comparison
 
+    :note: WARNING to unit-test writers: if `op` is a function that builds a graph,
+           try to make it a SMALL graph.  Often verify grad is run in
+           debug mode, which can be very slow if it has to verify a lot
+           of intermediate computations.
+    
     """
     pt = [numpy.array(p) for p in pt]