Move weights to be the second argument in tf.bincount.

This matches np.bincount(x, weights, minlength).
Change: 153476565

tensorflow/python/kernel_tests/bincount_op_test.py

@@ -73,13 +73,13 @@ def test_random_with_weights(self):
         else:
           weights = np.random.random(num_samples)
         self.assertAllEqual(
-            math_ops.bincount(arr, weights=weights).eval(),
+            math_ops.bincount(arr, weights).eval(),
             np.bincount(arr, weights))
 
   def test_zero_weights(self):
     with self.test_session():
       self.assertAllEqual(
-          math_ops.bincount(np.arange(1000), weights=np.zeros(1000)).eval(),
+          math_ops.bincount(np.arange(1000), np.zeros(1000)).eval(),
           np.zeros(1000))
 
   def test_negative(self):

tensorflow/python/ops/math_ops.py

@@ -2026,9 +2026,9 @@ def tanh(x, name=None):
 
 
 def bincount(arr,
+             weights=None,
              minlength=None,
              maxlength=None,
-             weights=None,
              dtype=dtypes.int32):
   """Counts the number of occurrences of each value in an integer array.
 
@@ -2040,13 +2040,13 @@ def bincount(arr,
 
   Args:
     arr: An int32 tensor of non-negative values.
+    weights: If non-None, must be the same shape as arr. For each value in
+        `arr`, the bin will be incremented by the corresponding weight instead
+        of 1.
     minlength: If given, ensures the output has length at least `minlength`,
         padding with zeros at the end if necessary.
     maxlength: If given, skips values in `arr` that are equal or greater than
         `maxlength`, ensuring that the output has length at most `maxlength`.
-    weights: If non-None, must be the same shape as arr. For each value in
-        `arr`, the bin will be incremented by the corresponding weight instead
-        of 1.
     dtype: If `weights` is None, determines the type of the output bins.
 
   Returns: