Ajusted the code format and added an interface to swapaxes in the TensorVariable object.

theano/tensor/basic.py

@@ -5032,6 +5032,6 @@ def swapaxes(y,axis1,axis2):
 	"swap axes of inputted tensor"
 	y = as_tensor_variable(y)
 	ndim = y.ndim
-	li = range(0,ndim)
+	li = range(0, ndim)
 	li[axis1], li[axis2] = li[axis2], li[axis1]
 	return y.dimshuffle(li)

theano/tensor/tests/test_basic.py

@@ -6901,21 +6901,21 @@ if __name__ == '__main__':
 
 class T_swapaxes(unittest.TestCase):                            
 	def test_no_dimensional_input(self):                                                        
-		self.assertRaises(IndexError, swapaxes, 2,0,1)
+		self.assertRaises(IndexError, swapaxes, 2, 0, 1)
         
 	def test_unidimensional_input(self):
-		self.assertRaises(IndexError, swapaxes, [2,1],0,1)
+		self.assertRaises(IndexError, swapaxes, [2, 1], 0, 1)
 		
 	def test_not_enough_dimension(self):
-		self.assertRaises(IndexError, swapaxes, [[2,1],[3,4]], 3, 4)
+		self.assertRaises(IndexError, swapaxes, [[2, 1], [3, 4]], 3, 4)
 
 	def test_doubleswap(self):
 		y = matrix()
-		n = swapaxes(y,0,1)
+		n = swapaxes(y, 0, 1)
 		f = function([y], n)
-		testMatrix = [[2,1],[3,4]]
+		testMatrix = [[2, 1], [3, 4]]
 
-		self.assertTrue(numpy.array_equal(testMatrix,f(f(testMatrix))))
+		self.assertTrue(numpy.array_equal(testMatrix, f(f(testMatrix))))
 
 class T_Power():
     def test_numpy_compare(self):

theano/tensor/var.py

@@ -699,4 +699,11 @@ class TensorConstant(_tensor_py_operators, Constant):
                           copy.deepcopy(self.data, memo),
                           copy.deepcopy(self.name, memo))
 
+def swapaxes(self, axis1, axis2)
+	"""Return 'tensor.swapaxes(self, axis1, axis2)
+
+	If a matrix is provided with the right axes, its transpose will be returned.
+	"""
+	return theano.tensor.basic.swapaxes(self, axis1, axis2)
+
 TensorType.Constant = TensorConstant