Automated rollback of change 139400135

Change: 139632235

3 files changed, 21 insertions, 21 deletions

diff --git a/tensorflow/contrib/solvers/python/ops/lanczos.py b/tensorflow/contrib/solvers/python/ops/lanczos.py
index 0a6c17eea2..2dfc9763a3 100644
--- a/tensorflow/contrib/solvers/python/ops/lanczos.py
+++ b/tensorflow/contrib/solvers/python/ops/lanczos.py
@@ -46,7 +46,7 @@ def lanczos_bidiag(operator,
 
   Args:
     operator: An object representing a linear operator with attributes:
-      - shape: Either a list of integers or a 1-D `Output` of type `int32` of
+      - shape: Either a list of integers or a 1-D `Tensor` of type `int32` of
         length 2. `shape[0]` is the dimension on the domain of the operator,
         `shape[1]` is the dimension of the co-domain of the operator. On other
         words, if operator represents an M x N matrix A, `shape` must contain
@@ -65,20 +65,20 @@ def lanczos_bidiag(operator,
       may terminate before `k` steps have been run.
     orthogonalize: If `True`, perform full orthogonalization. If `False` no
       orthogonalization is performed.
-    starting_vector: If not null, must be an `Output` of shape `[n]`.
+    starting_vector: If not null, must be a `Tensor` of shape `[n]`.
     name: A name scope for the operation.
 
   Returns:
     output: A namedtuple representing a Lanczos bidiagonalization of
       `operator` with attributes:
-      u: A rank-2 `Output` of type `operator.dtype` and shape
+      u: A rank-2 `Tensor` of type `operator.dtype` and shape
         `[operator.shape[0], k_actual+1]`, where `k_actual` is the number of
         steps run.
-      v: A rank-2 `Output` of type `operator.dtype` and shape
+      v: A rank-2 `Tensor` of type `operator.dtype` and shape
         `[operator.shape[1], k_actual]`, where `k_actual` is the number of steps
         run.
-      alpha: A rank-1 `Output` of type `operator.dtype` and shape `[k]`.
-      beta: A rank-1 `Output` of type `operator.dtype` and shape `[k]`.
+      alpha: A rank-1 `Tensor` of type `operator.dtype` and shape `[k]`.
+      beta: A rank-1 `Tensor` of type `operator.dtype` and shape `[k]`.
   """
 
   def tarray(size, dtype, name):
@@ -209,9 +209,9 @@ def bidiag_matmul(matrix, alpha, beta, adjoint_b=False, name="bidiag_matmul"):
       A * diag(alpha) + [zeros(m,1), A[:, :-1] * diag(beta[:-1])]
 
   Args:
-    matrix: A rank-2 `Output` representing matrix A.
-    alpha: A rank-1 `Output` representing the diagonal of B.
-    beta: A rank-1 `Output` representing the lower subdiagonal diagonal of B.
+    matrix: A rank-2 `Tensor` representing matrix A.
+    alpha: A rank-1 `Tensor` representing the diagonal of B.
+    beta: A rank-1 `Tensor` representing the lower subdiagonal diagonal of B.
     adjoint_b: `bool` determining what to compute.
     name: A name scope for the operation.
 
diff --git a/tensorflow/contrib/solvers/python/ops/least_squares.py b/tensorflow/contrib/solvers/python/ops/least_squares.py
index f80910ffe7..9a2d3b24dd 100644
--- a/tensorflow/contrib/solvers/python/ops/least_squares.py
+++ b/tensorflow/contrib/solvers/python/ops/least_squares.py
@@ -40,7 +40,7 @@ def cgls(operator, rhs, tol=1e-6, max_iter=20, name="cgls"):
 
   Args:
     operator: An object representing a linear operator with attributes:
-      - shape: Either a list of integers or a 1-D `Output` of type `int32` of
+      - shape: Either a list of integers or a 1-D `Tensor` of type `int32` of
         length 2. `shape[0]` is the dimension on the domain of the operator,
         `shape[1]` is the dimension of the co-domain of the operator. On other
         words, if operator represents an M x N matrix A, `shape` must contain
@@ -55,7 +55,7 @@ def cgls(operator, rhs, tol=1e-6, max_iter=20, name="cgls"):
         to `x`, i.e. if `operator` represents matrix `A`, `apply_adjoint` should
         return `conj(transpose(A)) * x`.
 
-    rhs: A rank-1 `Output` of shape `[M]` containing the right-hand size vector.
+    rhs: A rank-1 `Tensor` of shape `[M]` containing the right-hand size vector.
     tol: A float scalar convergence tolerance.
     max_iter: An integer giving the maximum number of iterations.
     name: A name scope for the operation.
@@ -63,10 +63,10 @@ def cgls(operator, rhs, tol=1e-6, max_iter=20, name="cgls"):
 
   Returns:
     output: A namedtuple representing the final state with fields:
-      - i: A scalar `int32` `Output`. Number of iterations executed.
-      - x: A rank-1 `Output` of shape `[N]` containing the computed solution.
-      - r: A rank-1 `Output` of shape `[M]` containing the residual vector.
-      - p: A rank-1 `Output` of shape `[N]`. The next descent direction.
+      - i: A scalar `int32` `Tensor`. Number of iterations executed.
+      - x: A rank-1 `Tensor` of shape `[N]` containing the computed solution.
+      - r: A rank-1 `Tensor` of shape `[M]` containing the residual vector.
+      - p: A rank-1 `Tensor` of shape `[N]`. The next descent direction.
       - gamma: \\(||A^* r||_2^2\\)
   """
   # ephemeral class holding CGLS state.
diff --git a/tensorflow/contrib/solvers/python/ops/linear_equations.py b/tensorflow/contrib/solvers/python/ops/linear_equations.py
index 38c94addd0..41fd6e466b 100644
--- a/tensorflow/contrib/solvers/python/ops/linear_equations.py
+++ b/tensorflow/contrib/solvers/python/ops/linear_equations.py
@@ -41,7 +41,7 @@ def conjugate_gradient(operator,
 
   Args:
     operator: An object representing a linear operator with attributes:
-      - shape: Either a list of integers or a 1-D `Output` of type `int32` of
+      - shape: Either a list of integers or a 1-D `Tensor` of type `int32` of
         length 2. `shape[0]` is the dimension on the domain of the operator,
         `shape[1]` is the dimension of the co-domain of the operator. On other
         words, if operator represents an N x N matrix A, `shape` must contain
@@ -50,17 +50,17 @@ def conjugate_gradient(operator,
       - apply: Callable object taking a vector `x` as input and returning a
         vector with the result of applying the operator to `x`, i.e. if
        `operator` represents matrix `A`, `apply` should return `A * x`.
-    rhs: A rank-1 `Output` of shape `[N]` containing the right-hand size vector.
+    rhs: A rank-1 `Tensor` of shape `[N]` containing the right-hand size vector.
     tol: A float scalar convergence tolerance.
     max_iter: An integer giving the maximum number of iterations.
     name: A name scope for the operation.
 
   Returns:
     output: A namedtuple representing the final state with fields:
-      - i: A scalar `int32` `Output`. Number of iterations executed.
-      - x: A rank-1 `Output` of shape `[N]` containing the computed solution.
-      - r: A rank-1 `Output` of shape `[M]` containing the residual vector.
-      - p: A rank-1 `Output` of shape `[N]`. `A`-conjugate basis vector.
+      - i: A scalar `int32` `Tensor`. Number of iterations executed.
+      - x: A rank-1 `Tensor` of shape `[N]` containing the computed solution.
+      - r: A rank-1 `Tensor` of shape `[M]` containing the residual vector.
+      - p: A rank-1 `Tensor` of shape `[N]`. `A`-conjugate basis vector.
       - gamma: \\(||r||_2^2\\)
   """
   # ephemeral class holding CG state.