Fix typos in comment.

theano/compile/function_module.py

@@ -1176,20 +1176,22 @@ class FunctionMaker(object):
             # 1) We preload the cache here to don't have its timming
             #    included in optimization that compile function.
             # 2) If other repo that import Theano have Theano ops defined,
-            #    we need to refresh the cache here. Otherwise, their is import
+            #    we need to refresh the cache here. Otherwise, there are import
             #    order problems.
-            #    When device=gpu, we compile during Theano import. This trigger
-            #    the loading of the cache. But unpickling the cache ask that the
-            #    other repos Ops are completly loaded, which isn't always the
-            #    case!
-            #    If a module isn't completly loaded and their unpickling fail,
-            #    it mean it is safe for this function compilation to skip them,
-            #    but not for futur compilation. So reloading the cache at each
-            #    compilation fix this problem.
-            # 3) This help propagate knowledge of newly compiled module to
-            #    concurrent process.
+            #    When device=gpu, we compile during Theano
+            #    import. This triggers the loading of the cache. But
+            #    unpickling the cache asks that the external Ops are
+            #    completly loaded, which isn't always the case!
+            #    If a module isn't completly loaded and its unpickling
+            #    fails, it means it is safe for this function
+            #    compilation to skip them, but not for future
+            #    compilations. So reloading the cache at each
+            #    compilation fixes this problem.
+            # 3) This helps propagate knowledge of newly compiled modules to
+            #    concurrent processes.
             theano.gof.cc.get_module_cache().refresh()
-        # Handle the case where inputs and/or outputs is a single Variable (not in a list)
+        # Handle the case where inputs and/or outputs is a single
+        # Variable (not in a list)
         self.orig_outputs = outputs
         unpack_single = False
         return_none = False