After discussion, remove the wrapper thing since this is contrary to the direction we want.

theano/tensor/var.py

@@ -18,43 +18,40 @@ class AsTensorError(TypeError):
 
 
 class _tensor_py_operators:
-    def _wrap(self, val):
-        return val
-
     # UNARY
     def __abs__(self):
-        return self._wrap(theano.tensor.basic.abs_(self))
+        return theano.tensor.basic.abs_(self)
 
     def __neg__(self):
-        return self._wrap(theano.tensor.basic.neg(self))
+        return theano.tensor.basic.neg(self)
 
     # CASTS
     #### REMOVED THESE BECAUSE PYTHON appears to require __int__ to return
     #### an int. -JB 20081112
-    #def __int__(self): return self._wrap(convert_to_int32(self))
-    #def __float__(self): return self._wrap(convert_to_float64(self))
-    #def __complex__(self): return self._wrap(convert_to_complex128(self))
+    #def __int__(self): return convert_to_int32(self)
+    #def __float__(self): return convert_to_float64(self)
+    #def __complex__(self): return convert_to_complex128(self)
 
     # COMPARISONS
     _is_nonzero = True
 
     def __lt__(self, other):
-        rval = self._wrap(theano.tensor.basic.lt(self, other))
+        rval = theano.tensor.basic.lt(self, other)
         rval._is_nonzero = False
         return rval
 
     def __le__(self, other):
-        rval = self._wrap(theano.tensor.basic.le(self, other))
+        rval = theano.tensor.basic.le(self, other)
         rval._is_nonzero = False
         return rval
 
     def __gt__(self, other):
-        rval = self._wrap(theano.tensor.basic.gt(self, other))
+        rval = theano.tensor.basic.gt(self, other)
         rval._is_nonzero = False
         return rval
 
     def __ge__(self, other):
-        rval = self._wrap(theano.tensor.basic.ge(self, other))
+        rval = theano.tensor.basic.ge(self, other)
         rval._is_nonzero = False
         return rval
 
@@ -84,39 +81,39 @@ class _tensor_py_operators:
 
     # BITWISE
     def __invert__(self):
-        return self._wrap(theano.tensor.basic.invert(self))
+        return theano.tensor.basic.invert(self)
 
     def __and__(self, other):
-        return self._wrap(theano.tensor.basic.and_(self, other))
+        return theano.tensor.basic.and_(self, other)
 
     def __or__(self, other):
-        return self._wrap(theano.tensor.basic.or_(self, other))
+        return theano.tensor.basic.or_(self, other)
 
     def __xor__(self, other):
-        return self._wrap(theano.tensor.basic.xor(self, other))
+        return theano.tensor.basic.xor(self, other)
 
     def __rand__(self, other):
-        return self._wrap(theano.tensor.basic.and_(other, self))
+        return theano.tensor.basic.and_(other, self)
 
     def __ror__(self, other):
-        return self._wrap(theano.tensor.basic.or_(other, self))
+        return theano.tensor.basic.or_(other, self)
 
     def __rxor__(self, other):
-        return self._wrap(theano.tensor.basic.xor(other, self))
+        return theano.tensor.basic.xor(other, self)
 
     # def __iand__(self, other):
-    #    return self._wrap(_and_inplace(self, other))
+    #    return _and_inplace(self, other)
     #
     # def __ior__(self, other):
-    #    return self._wrap(_or_inplace(self, other))
+    #    return _or_inplace(self, other)
     #
     #def __ixor__(self, other):
-    #    return self._wrap(_xor_inplace(self, other))
+    #    return _xor_inplace(self, other)
 
     # ARITHMETIC - NORMAL
     def __add__(self, other):
         try:
-            return self._wrap(theano.tensor.basic.add(self, other))
+            return theano.tensor.basic.add(self, other)
         # We should catch the minimum number of exception here.
         # Otherwise this will convert error when Theano flags
         # compute_test_value is used
@@ -135,7 +132,7 @@ class _tensor_py_operators:
         # See explanation in __add__ for the error catched
         # and the return value in that case
         try:
-            return self._wrap(theano.tensor.basic.sub(self, other))
+            return theano.tensor.basic.sub(self, other)
         except (NotImplementedError, AsTensorError):
             return NotImplemented
 
@@ -143,7 +140,7 @@ class _tensor_py_operators:
         # See explanation in __add__ for the error catched
         # and the return value in that case
         try:
-            return self._wrap(theano.tensor.mul(self, other))
+            return theano.tensor.mul(self, other)
         except (NotImplementedError, AsTensorError):
             return NotImplemented
 
@@ -151,7 +148,7 @@ class _tensor_py_operators:
         # See explanation in __add__ for the error catched
         # and the return value in that case
         try:
-            return self._wrap(theano.tensor.basic.div_proxy(self, other))
+            return theano.tensor.basic.div_proxy(self, other)
         except IntegerDivisionError:
             # This is to raise the exception that occurs when trying to divide
             # two integer arrays (currently forbidden).
@@ -165,7 +162,7 @@ class _tensor_py_operators:
         # See explanation in __add__ for the error catched
         # adn the return value in that case
         try:
-            return self._wrap(theano.tensor.basic.pow(self, other))
+            return theano.tensor.basic.pow(self, other)
         except (NotImplementedError, AsTensorError):
             return NotImplemented
 
@@ -173,7 +170,7 @@ class _tensor_py_operators:
         # See explanation in __add__ for the error catched
         # adn the return value in that case
         try:
-            return self._wrap(theano.tensor.basic.mod_check(self, other))
+            return theano.tensor.basic.mod_check(self, other)
         except ComplexError:
             # This is to raise the exception that occurs when trying to compute
             # x % y with either x or y a complex number.
@@ -182,55 +179,55 @@ class _tensor_py_operators:
             return NotImplemented
 
     def __truediv__(self, other):
-        return self._wrap(theano.tensor.basic.true_div(self, other))
+        return theano.tensor.basic.true_div(self, other)
 
     def __floordiv__(self, other):
-        return self._wrap(theano.tensor.basic.floor_div(self, other))
+        return theano.tensor.basic.floor_div(self, other)
 
     def __rtruediv__(self, other):
-        return self._wrap(theano.tensor.basic.true_div(other, self))
+        return theano.tensor.basic.true_div(other, self)
 
     def __rfloordiv__(self, other):
-        return self._wrap(theano.tensor.basic.floor_div(other, self))
+        return theano.tensor.basic.floor_div(other, self)
 
     ##### DO NOT USE THESE BECAUSE INPLACE OPS SHOULD BE INSERTED
     ##### BY OPTIMIZATIONS ONLY
     ## ARITHMETIC - INPLACE
     #def __iadd__(self, other):
-    #    return self._wrap(_add_inplace(self, other))
+    #    return _add_inplace(self, other)
     #def __isub__(self, other):
-    #    return self._wrap(_sub_inplace(self, other))
+    #    return _sub_inplace(self, other)
     #
     #def __imul__(self, other):
-    #    return self._wrap(_mul_inplace(self, other))
+    #    return _mul_inplace(self, other)
     #
     #def __idiv__(self, other):
-    #    return self._wrap(_div_inplace(self, other))
+    #    return _div_inplace(self, other)
     #
     #def __ipow__(self, other):
-    #    return self._wrap(_pow_inplace(self, other))
+    #    return _pow_inplace(self, other)
 
     # ARITHMETIC - RIGHT-OPERAND
     def __radd__(self, other):
-        return self._wrap(theano.tensor.basic.add(other, self))
+        return theano.tensor.basic.add(other, self)
 
     def __rsub__(self, other):
-        return self._wrap(theano.tensor.basic.sub(other, self))
+        return theano.tensor.basic.sub(other, self)
 
     def __rmul__(self, other):
-        return self._wrap(theano.tensor.basic.mul(other, self))
+        return theano.tensor.basic.mul(other, self)
 
     def __rdiv__(self, other):
-        return self._wrap(theano.tensor.basic.div_proxy(other, self))
+        return theano.tensor.basic.div_proxy(other, self)
 
     def __rmod__(self, other):
-        return self._wrap(theano.tensor.basic.mod(other, self))
+        return theano.tensor.basic.mod(other, self)
 
     def __rpow__(self, other):
-        return self._wrap(theano.tensor.basic.pow(other, self))
+        return theano.tensor.basic.pow(other, self)
 
     # TRANSPOSE
-    T = property(lambda self: self._wrap(theano.tensor.basic.transpose(self)))
+    T = property(lambda self: theano.tensor.basic.transpose(self))
 
     def transpose(self, *axes):
         """
@@ -243,16 +240,16 @@ class _tensor_py_operators:
 
         """
         if len(axes) == 0:
-            return self._wrap(theano.tensor.basic.transpose(self))
+            return theano.tensor.basic.transpose(self)
         try:
             iter(axes[0])
             iterable = True
         except TypeError:
             iterable = False
         if len(axes) == 1 and iterable:
-            return self._wrap(theano.tensor.basic.transpose(self, axes[0]))
+            return theano.tensor.basic.transpose(self, axes[0])
         else:
-            return self._wrap(theano.tensor.basic.transpose(self, axes))
+            return theano.tensor.basic.transpose(self, axes)
 
     shape = property(lambda self: theano.tensor.basic.shape(self))
 
@@ -260,12 +257,10 @@ class _tensor_py_operators:
 
     # We can't implement __len__ to provide a better error message.
     def any(self, axis=None, keepdims=False):
-        return self._wrap(theano.tensor.basic.any(self, axis=axis,
-                                                  keepdims=keepdims))
+        return theano.tensor.basic.any(self, axis=axis, keepdims=keepdims)
 
     def all(self, axis=None, keepdims=False):
-        return self._wrap(theano.tensor.basic.all(self, axis=axis,
-                                                  keepdims=keepdims))
+        return theano.tensor.basic.all(self, axis=axis, keepdims=keepdims)
 
     # Otherwise TensorVariable[:-1] does not work as Python 2.5.1 calls
     # __len__ before calling __getitem__. It also does not catch the raised
@@ -298,7 +293,7 @@ class _tensor_py_operators:
                 raise ValueError("Expected ndim to be an integer, is " +
                                  str(type(ndim)))
 
-        return self._wrap(theano.tensor.basic.reshape(self, shape, ndim=ndim))
+        return theano.tensor.basic.reshape(self, shape, ndim=ndim)
 
     def dimshuffle(self, *pattern):
         """
@@ -325,21 +320,20 @@ class _tensor_py_operators:
             pattern = pattern[0]
         op = theano.tensor.basic.DimShuffle(list(self.type.broadcastable),
                                             pattern)
-        return self._wrap(op(self))
+        return op(self)
 
     def flatten(self, ndim=1):
-        return self._wrap(theano.tensor.basic.flatten(self, ndim))
+        return theano.tensor.basic.flatten(self, ndim)
 
     def ravel(self):
-        return self._wrap(theano.tensor.basic.flatten(self))
+        return theano.tensor.basic.flatten(self)
 
     def diagonal(self, offset=0, axis1=0, axis2=1):
-        return self._wrap(theano.tensor.basic.diagonal(self, offset,
-                                                       axis1, axis2))
+        return theano.tensor.basic.diagonal(self, offset, axis1, axis2)
 
     # CASTING
     def astype(self, dtype):
-        return self._wrap(theano.tensor.cast(self, dtype))
+        return theano.tensor.cast(self, dtype)
 
     # SLICING
     # Do not define __getslice__ here:
@@ -381,9 +375,9 @@ class _tensor_py_operators:
                         TensorVariable,
                         TensorConstant,
                         theano.tensor.sharedvar.TensorSharedVariable))):
-                return self._wrap(self.take(arg, axis))
+                return self.take(arg, axis)
             else:
-                return self._wrap(theano.tensor.subtensor.AdvancedSubtensor()(self, *args))
+                return theano.tensor.subtensor.AdvancedSubtensor()(self, *args)
         else:
             if numpy.newaxis in args:
                 # None (aka np.newaxis) in numpy indexing means to add a
@@ -405,24 +399,23 @@ class _tensor_py_operators:
                         new_args.append(arg)
                 view = self.dimshuffle(pattern)
                 rval = view.__getitem__(tuple(new_args))
-                return self._wrap(rval)
+                return rval
             else:
-                return self._wrap(theano.tensor.subtensor.Subtensor(args)(
+                return theano.tensor.subtensor.Subtensor(args)(
                     self, *theano.tensor.subtensor.Subtensor.collapse(args,
-                                  lambda entry: isinstance(entry, Variable))))
+                    lambda entry: isinstance(entry, Variable)))
 
     def take(self, indices, axis=None, mode='raise'):
-        return self._wrap(theano.tensor.subtensor.take(self, indices,
-                                                       axis, mode))
+        return theano.tensor.subtensor.take(self, indices, axis, mode)
 
     # COPYING
     def copy(self):
-        return self._wrap(theano.tensor.basic.tensor_copy(self))
+        return theano.tensor.basic.tensor_copy(self)
 
     def __iter__(self):
         try:
             for i in xrange(theano.tensor.basic.get_vector_length(self)):
-                yield self._wrap(self[i])
+                yield self[i]
         except TypeError:
             # This prevents accidental iteration via builtin.sum(self)
             raise TypeError(('TensorType does not support iteration. '
@@ -444,26 +437,24 @@ class _tensor_py_operators:
 
     # extra pseudo-operator symbols
     def __dot__(left, right):
-        return left._wrap(theano.tensor.basic.dot(left, right))
+        return theano.tensor.basic.dot(left, right)
 
     def __rdot__(right, left):
-        return right._wrap(theano.tensor.basic.dot(left, right))
+        return theano.tensor.basic.dot(left, right)
 
     dot = __dot__
 
     def sum(self, axis=None, dtype=None, keepdims=False, acc_dtype=None):
         """See `theano.tensor.sum`"""
-        return self._wrap(theano.tensor.basic.sum(self, axis=axis,
-                                                  dtype=dtype,
-                                                  keepdims=keepdims,
-                                                  acc_dtype=acc_dtype))
+        return theano.tensor.basic.sum(self, axis=axis,
+                                       dtype=dtype, keepdims=keepdims,
+                                       acc_dtype=acc_dtype)
 
     def prod(self, axis=None, dtype=None, keepdims=False, acc_dtype=None):
         """See `theano.tensor.prod`"""
-        return self._wrap(theano.tensor.basic.prod(self, axis=axis,
-                                                   dtype=dtype,
-                                                   keepdims=keepdims,
-                                                   acc_dtype=acc_dtype))
+        return theano.tensor.basic.prod(self, axis=axis,
+                                        dtype=dtype, keepdims=keepdims,
+                                        acc_dtype=acc_dtype)
 
     def norm(self, L, axis=None):
         if L == 0:
@@ -471,87 +462,80 @@ class _tensor_py_operators:
         if numpy.isinf(L):
             raise NotImplementedError()
         # optimizations will/should catch cases like L=1, L=2
-        return self._wrap(theano.tensor.basic.pow(
+        return theano.tensor.basic.pow(
             theano.tensor.basic.pow(
-                theano.tensor.basic.abs_(self), L).sum(axis=axis), 1.0 / L))
+                theano.tensor.basic.abs_(self), L).sum(axis=axis), 1.0 / L)
 
     def mean(self, axis=None, dtype=None, keepdims=False, acc_dtype=None):
         """See `theano.tensor.mean`"""
-        return self._wrap(theano.tensor.basic.mean(self, axis=axis,
-                                                   dtype=dtype,
-                                                   keepdims=keepdims,
-                                                   acc_dtype=acc_dtype))
+        return theano.tensor.basic.mean(self, axis=axis,
+                                        dtype=dtype, keepdims=keepdims,
+                                        acc_dtype=acc_dtype)
 
     def var(self, axis=None, keepdims=False):
         """See `theano.tensor.var`"""
-        return self._wrap(theano.tensor.basic.var(self, axis,
-                                                  keepdims=keepdims))
+        return theano.tensor.basic.var(self, axis, keepdims=keepdims)
 
     def std(self, axis=None, keepdims=False):
         """See `theano.tensor.std`"""
-        return self._wrap(theano.tensor.basic.std(self, axis,
-                                                  keepdims=keepdims))
+        return theano.tensor.basic.std(self, axis, keepdims=keepdims)
 
     def min(self, axis=None, keepdims=False):
         """See `theano.tensor.min`"""
-        return self._wrap(theano.tensor.basic.min(self, axis,
-                                                  keepdims=keepdims))
+        return theano.tensor.basic.min(self, axis, keepdims=keepdims)
 
     def max(self, axis=None, keepdims=False):
         """See `theano.tensor.max`"""
-        return self._wrap(theano.tensor.basic.max(self, axis,
-                                                  keepdims=keepdims))
+        return theano.tensor.basic.max(self, axis, keepdims=keepdims)
 
     def argmin(self, axis=None, keepdims=False):
         """See `theano.tensor.argmin`"""
-        return self._wrap(theano.tensor.basic.argmin(self, axis,
-                                                     keepdims=keepdims))
+        return theano.tensor.basic.argmin(self, axis, keepdims=keepdims)
 
     def argmax(self, axis=None, keepdims=False):
         """See `theano.tensor.argmax`"""
-        return self._wrap(theano.tensor.basic.argmax(self, axis,
-                                                     keepdims=keepdims))
+        return theano.tensor.basic.argmax(self, axis, keepdims=keepdims)
 
     def nonzero(self, return_matrix=False):
         """See `theano.tensor.nonzero`"""
-        return self._wrap(theano.tensor.basic.nonzero(self, return_matrix=return_matrix))
+        return theano.tensor.basic.nonzero(self, return_matrix=return_matrix)
 
     def nonzero_values(self):
         """See `theano.tensor.nonzero_values`"""
-        return self._wrap(theano.tensor.basic.nonzero_values(self))
+        return theano.tensor.basic.nonzero_values(self)
 
     def sort(self, axis=-1, kind='quicksort', order=None):
         """See `theano.tensor.sort`"""
         from theano.tensor.sort import sort
-        return self._wrap(sort(self, axis, kind, order))
+        return sort(self, axis, kind, order)
 
     def argsort(self, axis=-1, kind='quicksort', order=None):
         """See `theano.tensor.argsort`"""
         from theano.tensor.sort import argsort
-        return self._wrap(argsort(self, axis, kind, order))
+        return argsort(self, axis, kind, order)
 
     def clip(self, a_min, a_max):
         "Clip (limit) the values in an array."
-        return self._wrap(theano.tensor.basic.clip(self, a_min, a_max))
+        return theano.tensor.basic.clip(self, a_min, a_max)
 
     def conj(self):
         """See `theano.tensor.conj`"""
-        return self._wrap(theano.tensor.basic.conj(self))
+        return theano.tensor.basic.conj(self)
 
     conjugate = conj
 
     def repeat(self, repeats, axis=None):
         """See `theano.tensor.repeat`"""
         from theano.tensor.extra_ops import repeat
-        return self._wrap(repeat(self, repeats, axis))
+        return repeat(self, repeats, axis)
 
     def round(self, mode="half_away_from_zero"):
         """See `theano.tensor.round`"""
-        return self._wrap(theano.tensor.basic.round(self, mode))
+        return theano.tensor.basic.round(self, mode)
 
     def trace(self):
         from theano.sandbox.linalg import trace
-        return self._wrap(trace(self))
+        return trace(self)
 
     # TO TRUMP NUMPY OPERATORS
     __array_priority__ = 1000
@@ -560,7 +544,8 @@ class _tensor_py_operators:
         return theano.tensor.basic.get_scalar_constant_value(self)
 
     def zeros_like(model, dtype=None):
-        return self._wrap(theano.tensor.basic.zeros_like(model, dtype=dtype))
+        return theano.tensor.basic.zeros_like(model, dtype=dtype)
+
 
 class TensorVariable(_tensor_py_operators, Variable):
     """Subclass to add the tensor operators to the basic `Variable` class."""