changing the order of tf.svd's arguments to be the same as numpy's

Change: 137996377

tensorflow/python/ops/linalg_ops.py

@@ -239,7 +239,7 @@ def self_adjoint_eigvals(tensor, name=None):
   return e
 
 
-def svd(tensor, compute_uv=True, full_matrices=False, name=None):
+def svd(tensor, full_matrices=False, compute_uv=True, name=None):
   """Computes the singular value decompositions of one or more matrices.
 
   Computes the SVD of each inner matrix in `tensor` such that
@@ -258,12 +258,12 @@ def svd(tensor, compute_uv=True, full_matrices=False, name=None):
   Args:
     matrix: `Tensor` of shape `[..., M, N]`. Let `P` be the minimum of `M` and
       `N`.
-    compute_uv: If `True` then left and right singular vectors will be
-      computed and returned in `u` and `v`, respectively. Otherwise, only the
-      singular values will be computed, which can be significantly faster.
     full_matrices: If true, compute full-sized `u` and `v`. If false
       (the default), compute only the leading `P` singular vectors.
       Ignored if `compute_uv` is `False`.
+    compute_uv: If `True` then left and right singular vectors will be
+      computed and returned in `u` and `v`, respectively. Otherwise, only the
+      singular values will be computed, which can be significantly faster.
     name: string, optional name of the operation.
 
   Returns: