Automated rollback of change 139371663

Change: 139598626

7 files changed, 28 insertions, 28 deletions

diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc
index 25972252c5..72724007d8 100644
--- a/tensorflow/core/ops/array_ops.cc
+++ b/tensorflow/core/ops/array_ops.cc
@@ -305,7 +305,7 @@ concat_dim: 0-D.  The dimension along which to concatenate.  Must be in the
   range [0, rank(values)).
 values: The `N` Tensors to concatenate. Their ranks and types must match,
   and their sizes must match in all dimensions except `concat_dim`.
-output: An `Output` with the concatenation of values stacked along the
+output: A `Tensor` with the concatenation of values stacked along the
   `concat_dim` dimension.  This tensor's shape matches that of `values` except
   in `concat_dim` where it has the sum of the sizes.
 )doc");
@@ -325,7 +325,7 @@ values: List of `N` Tensors to concatenate. Their ranks and types must match,
   and their sizes must match in all dimensions except `concat_dim`.
 axis: 0-D.  The dimension along which to concatenate.  Must be in the
   range [0, rank(values)).
-output: An `Output` with the concatenation of values stacked along the
+output: A `Tensor` with the concatenation of values stacked along the
   `concat_dim` dimension.  This tensor's shape matches that of `values` except
   in `concat_dim` where it has the sum of the sizes.
 )doc");
@@ -4395,7 +4395,7 @@ input_mins: The minimum scalar values for each of the input tensors.
 input_maxes: The maximum scalar values for each of the input tensors.
 output_min: The float value that the minimum quantized output value represents.
 output_max: The float value that the maximum quantized output value represents.
-output: An `Output` with the concatenation of values stacked along the
+output: A `Tensor` with the concatenation of values stacked along the
   `concat_dim` dimension.  This tensor's shape matches that of `values` except
   in `concat_dim` where it has the sum of the sizes.
 )doc");
@@ -4512,7 +4512,7 @@ operator which extracts values or slices from a given tensor.
 TODO(simister): Add a link to Variable.__getitem__ documentation on slice
 syntax.
 
-`shape` is a `TensorShape` with rank `P` and `indices` is an `Output` of rank
+`shape` is a `TensorShape` with rank `P` and `indices` is a `Tensor` of rank
 `Q`.
 
 `indices` must be integer tensor, containing indices into `shape`.
diff --git a/tensorflow/core/ops/linalg_ops.cc b/tensorflow/core/ops/linalg_ops.cc
index 90a33f7e6c..c8cae11305 100644
--- a/tensorflow/core/ops/linalg_ops.cc
+++ b/tensorflow/core/ops/linalg_ops.cc
@@ -304,7 +304,7 @@ e, v = self_adjoint_eig(a)
 e = self_adjoint_eig(a, compute_v=False)
 ```
 
-input: `Output` input of shape `[N, N]`.
+input: `Tensor` input of shape `[N, N]`.
 compute_v: If `True` then eigenvectors will be computed and returned in `v`.
   Otherwise, only the eigenvalues will be computed.
 e: Eigenvalues. Shape is `[N]`.
diff --git a/tensorflow/core/ops/logging_ops.cc b/tensorflow/core/ops/logging_ops.cc
index 5bf4e306ef..42bd12a5b3 100644
--- a/tensorflow/core/ops/logging_ops.cc
+++ b/tensorflow/core/ops/logging_ops.cc
@@ -153,7 +153,7 @@ normalization algorithms:
    is at 127.  They are then rescaled so that either the smallest value is 0,
    or the largest one is 255.
 
-The `tag` argument is a scalar `Output` of type `string`.  It is used to
+The `tag` argument is a scalar `Tensor` of type `string`.  It is used to
 build the `tag` of the summary values:
 
 *  If `max_images` is 1, the summary value tag is '*tag*/image'.
@@ -190,7 +190,7 @@ audio is built from `tensor` which must be 3-D with shape `[batch_size,
 frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are
 assumed to be in the range of `[-1.0, 1.0]` with a sample rate of `sample_rate`.
 
-The `tag` argument is a scalar `Output` of type `string`.  It is used to
+The `tag` argument is a scalar `Tensor` of type `string`.  It is used to
 build the `tag` of the summary values:
 
 *  If `max_outputs` is 1, the summary value tag is '*tag*/audio'.
@@ -220,7 +220,7 @@ audio is built from `tensor` which must be 3-D with shape `[batch_size,
 frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are
 assumed to be in the range of `[-1.0, 1.0]` with a sample rate of `sample_rate`.
 
-The `tag` argument is a scalar `Output` of type `string`.  It is used to
+The `tag` argument is a scalar `Tensor` of type `string`.  It is used to
 build the `tag` of the summary values:
 
 *  If `max_outputs` is 1, the summary value tag is '*tag*/audio'.
diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc
index f854ad288b..9e692ed0bc 100644
--- a/tensorflow/core/ops/math_ops.cc
+++ b/tensorflow/core/ops/math_ops.cc
@@ -92,7 +92,7 @@ REGISTER_OP("BatchMatMul")
     .Doc(R"doc(
 Multiplies slices of two tensors in batches.
 
-Multiplies all slices of `Output` `x` and `y` (each slice can be
+Multiplies all slices of `Tensor` `x` and `y` (each slice can be
 viewed as an element of a batch), and arranges the individual results
 in a single output tensor of the same batch size. Each of the
 individual slices can optionally be adjointed (to adjoint a matrix
@@ -1064,11 +1064,11 @@ select(condition, t, e) ==> [[1, 2],
 
 ```
 
-t:= An `Output` which may have the same shape as `condition`.
+t:= A `Tensor` which may have the same shape as `condition`.
     If `condition` is rank 1, `t` may have higher rank,
     but its first dimension must match the size of `condition`.
-e:= An `Output` with the same type and shape as `t`.
-output:= An `Output` with the same type and shape as `t` and `e`.
+e:= A `Tensor` with the same type and shape as `t`.
+output:= A `Tensor` with the same type and shape as `t` and `e`.
 )doc");
 
 // --------------------------------------------------------------------------
diff --git a/tensorflow/core/ops/sparse_ops.cc b/tensorflow/core/ops/sparse_ops.cc
index c115894f29..860b3475e9 100644
--- a/tensorflow/core/ops/sparse_ops.cc
+++ b/tensorflow/core/ops/sparse_ops.cc
@@ -199,7 +199,7 @@ REGISTER_OP("SerializeSparse")
       return Status::OK();
     })
     .Doc(R"doc(
-Serialize a `SparseTensor` into a string 3-vector (1-D `Output`) object.
+Serialize a `SparseTensor` into a string 3-vector (1-D `Tensor`) object.
 
 sparse_indices: 2-D.  The `indices` of the `SparseTensor`.
 sparse_values: 1-D.  The `values` of the `SparseTensor`.
@@ -221,7 +221,7 @@ REGISTER_OP("SerializeManySparse")
       return Status::OK();
     })
     .Doc(R"doc(
-Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]` string `Output`.
+Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]` string `Tensor`.
 
 The `SparseTensor` must have rank `R` greater than 1, and the first dimension
 is treated as the minibatch dimension.  Elements of the `SparseTensor`
@@ -621,7 +621,7 @@ REGISTER_OP("SparseTensorDenseAdd")
       return Status::OK();
     })
     .Doc(R"doc(
-Adds up a `SparseTensor` and a dense `Output`, producing a dense `Output`.
+Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`.
 
 This Op does not require `a_indices` be sorted in standard lexicographic order.
 
@@ -644,7 +644,7 @@ REGISTER_OP("SparseReduceSum")
 Computes the sum of elements across dimensions of a SparseTensor.
 
 This Op takes a SparseTensor and is the sparse counterpart to
-`tf.reduce_sum()`.  In particular, this Op also returns a dense `Output`
+`tf.reduce_sum()`.  In particular, this Op also returns a dense `Tensor`
 instead of a sparse one.
 
 Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
diff --git a/tensorflow/core/ops/state_ops.cc b/tensorflow/core/ops/state_ops.cc
index cb1c2ec6ea..b300fbbe26 100644
--- a/tensorflow/core/ops/state_ops.cc
+++ b/tensorflow/core/ops/state_ops.cc
@@ -516,7 +516,7 @@ REGISTER_OP("ScatterNdUpdate")
 Applies sparse `updates` to individual values or slices within a given
 variable according to `indices`.
 
-`ref` is an `Output` with rank `P` and `indices` is an `Output` of rank `Q`.
+`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
 
 `indices` must be integer tensor, containing indices into `ref`.
 It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
@@ -525,7 +525,7 @@ The innermost dimension of `indices` (with length `K`) corresponds to
 indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
 dimension of `ref`.
 
-`updates` is `Output` of rank `Q-1+P-K` with shape:
+`updates` is `Tensor` of rank `Q-1+P-K` with shape:
 
 ```
 [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
@@ -573,7 +573,7 @@ REGISTER_OP("ScatterNdAdd")
 Applies sparse addition between `updates` and individual values or slices
 within a given variable according to `indices`.
 
-`ref` is an `Output` with rank `P` and `indices` is an `Output` of rank `Q`.
+`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
 
 `indices` must be integer tensor, containing indices into `ref`.
 It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
@@ -582,7 +582,7 @@ The innermost dimension of `indices` (with length `K`) corresponds to
 indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
 dimension of `ref`.
 
-`updates` is `Output` of rank `Q-1+P-K` with shape:
+`updates` is `Tensor` of rank `Q-1+P-K` with shape:
 
 ```
 [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
@@ -630,7 +630,7 @@ REGISTER_OP("ScatterNdSub")
 Applies sparse subtraction between `updates` and individual values or slices
 within a given variable according to `indices`.
 
-`ref` is an `Output` with rank `P` and `indices` is an `Output` of rank `Q`.
+`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
 
 `indices` must be integer tensor, containing indices into `ref`.
 It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
@@ -639,7 +639,7 @@ The innermost dimension of `indices` (with length `K`) corresponds to
 indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
 dimension of `ref`.
 
-`updates` is `Output` of rank `Q-1+P-K` with shape:
+`updates` is `Tensor` of rank `Q-1+P-K` with shape:
 
 ```
 [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
@@ -690,7 +690,7 @@ output_ref: Same as ref. Returned as a convenience for operations that want
 //         R"doc(Applies sparse subtraction between `updates` and individual
 //         values or slices within a given variable according to `indices`.
 
-// `ref` is an `Output` with rank `P` and `indices` is an `Output` of rank `Q`.
+// `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
 
 // `indices` must be integer tensor, containing indices into `ref`.
 // It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
@@ -699,7 +699,7 @@ output_ref: Same as ref. Returned as a convenience for operations that want
 // indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
 // dimension of `ref`.
 
-// `updates` is `Output` of rank `Q-1+P-K` with shape:
+// `updates` is `Tensor` of rank `Q-1+P-K` with shape:
 
 // ```
 // [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
@@ -746,7 +746,7 @@ output_ref: Same as ref. Returned as a convenience for operations that want
 //         R"doc(Applies sparse subtraction between `updates` and individual
 //         values or slices within a given variable according to `indices`.
 
-// `ref` is an `Output` with rank `P` and `indices` is an `Output` of rank `Q`.
+// `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
 
 // `indices` must be integer tensor, containing indices into `ref`.
 // It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
@@ -755,7 +755,7 @@ output_ref: Same as ref. Returned as a convenience for operations that want
 // indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
 // dimension of `ref`.
 
-// `updates` is `Output` of rank `Q-1+P-K` with shape:
+// `updates` is `Tensor` of rank `Q-1+P-K` with shape:
 
 // ```
 // [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
diff --git a/tensorflow/core/ops/string_ops.cc b/tensorflow/core/ops/string_ops.cc
index 266a54c768..53d75e4519 100644
--- a/tensorflow/core/ops/string_ops.cc
+++ b/tensorflow/core/ops/string_ops.cc
@@ -308,9 +308,9 @@ REGISTER_OP("Substr")
       return shape_inference::BroadcastBinaryOpShapeFn(c);
     })
     .Doc(R"doc(
-Return substrings from `Output` of strings.
+Return substrings from `Tensor` of strings.
 
-For each string in the input `Output`, creates a substring starting at index 
+For each string in the input `Tensor`, creates a substring starting at index 
 `pos` with a total length of `len`. 
 
 If `len` defines a substring that would extend beyond the length of the input