Replace theano.tensor alias T with tt in theano.sandbox

theano/sandbox/multinomial.py

+import copy
+
 import numpy as np
 import warnings
 
 import theano
+import theano.tensor as tt
+
 from theano import Op, Apply
-import theano.tensor as T
 from theano.scalar import as_scalar
-import copy
 
 
 class MultinomialFromUniform(Op):
-    # TODO : need description for parameter 'odtype'
     """
     Converts samples from a uniform into sample from a multinomial.
 
+    TODO : need description for parameter 'odtype'
     """
 
     __props__ = ("odtype",)
@@ -31,8 +33,8 @@ class MultinomialFromUniform(Op):
             self.odtype = "auto"
 
     def make_node(self, pvals, unis, n=1):
-        pvals = T.as_tensor_variable(pvals)
-        unis = T.as_tensor_variable(unis)
+        pvals = tt.as_tensor_variable(pvals)
+        unis = tt.as_tensor_variable(unis)
         if pvals.ndim != 2:
             raise NotImplementedError("pvals ndim should be 2", pvals.ndim)
         if unis.ndim != 1:
@@ -41,16 +43,16 @@ class MultinomialFromUniform(Op):
             odtype = pvals.dtype
         else:
             odtype = self.odtype
-        out = T.tensor(dtype=odtype, broadcastable=pvals.type.broadcastable)
+        out = tt.tensor(dtype=odtype, broadcastable=pvals.type.broadcastable)
         return Apply(self, [pvals, unis, as_scalar(n)], [out])
 
     def grad(self, ins, outgrads):
         pvals, unis, n = ins
         (gz,) = outgrads
         return [
-            T.zeros_like(x, dtype=theano.config.floatX)
-            if x.dtype in T.discrete_dtypes
-            else T.zeros_like(x)
+            tt.zeros_like(x, dtype=theano.config.floatX)
+            if x.dtype in tt.discrete_dtypes
+            else tt.zeros_like(x)
             for x in ins
         ]
 
@@ -237,8 +239,8 @@ class ChoiceFromUniform(MultinomialFromUniform):
             self.replace = False
 
     def make_node(self, pvals, unis, n=1):
-        pvals = T.as_tensor_variable(pvals)
-        unis = T.as_tensor_variable(unis)
+        pvals = tt.as_tensor_variable(pvals)
+        unis = tt.as_tensor_variable(unis)
         if pvals.ndim != 2:
             raise NotImplementedError("pvals ndim should be 2", pvals.ndim)
         if unis.ndim != 1:
@@ -247,7 +249,7 @@ class ChoiceFromUniform(MultinomialFromUniform):
             odtype = "int64"
         else:
             odtype = self.odtype
-        out = T.tensor(dtype=odtype, broadcastable=pvals.type.broadcastable)
+        out = tt.tensor(dtype=odtype, broadcastable=pvals.type.broadcastable)
         return Apply(self, [pvals, unis, as_scalar(n)], [out])
 
     def c_code_cache_version(self):