1 files changed, 0 insertions, 69 deletions
diff --git a/tensorflow/core/ops/sparse_ops.cc b/tensorflow/core/ops/sparse_ops.cc
index 6aca2c3b01..9722f0ee9a 100644
--- a/tensorflow/core/ops/sparse_ops.cc
+++ b/tensorflow/core/ops/sparse_ops.cc
@@ -710,75 +710,6 @@ a_shape: 1-D.  The `shape` of the `SparseTensor`, with shape `[ndims]`.
 b: `ndims`-D Tensor.  With shape `a_shape`.
 )doc");
 
-REGISTER_OP("SparseReduceMax")
-    .Input("input_indices: int64")
-    .Input("input_values: T")
-    .Input("input_shape: int64")
-    .Input("reduction_axes: int32")
-    .Attr("keep_dims: bool = False")
-    .Output("output: T")
-    .Attr("T: realnumbertype")
-    .SetShapeFn(shape_inference::UnknownShape)
-    .Doc(R"doc(
-Computes the max of elements across dimensions of a SparseTensor.
-
-This Op takes a SparseTensor and is the sparse counterpart to
-`tf.reduce_max()`.  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
-`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
-`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
-with length 1.
-
-If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
-with a single element is returned.  Additionally, the axes can be negative,
-which are interpreted according to the indexing rules in Python.
-
-input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
-  SparseTensor, possibly not in canonical ordering.
-input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
-input_shape: 1-D.  Shape of the input SparseTensor.
-reduction_axes: 1-D.  Length-`K` vector containing the reduction axes.
-keep_dims: If true, retain reduced dimensions with length 1.
-output: `R-K`-D.  The reduced Tensor.
-)doc");
-
-REGISTER_OP("SparseReduceMaxSparse")
-    .Input("input_indices: int64")
-    .Input("input_values: T")
-    .Input("input_shape: int64")
-    .Input("reduction_axes: int32")
-    .Attr("keep_dims: bool = False")
-    .Output("output_indices: int64")
-    .Output("output_values: T")
-    .Output("output_shape: int64")
-    .Attr("T: realnumbertype")
-    .SetShapeFn(shape_inference::UnknownShape)
-    .Doc(R"doc(
-Computes the max of elements across dimensions of a SparseTensor.
-
-This Op takes a SparseTensor and is the sparse counterpart to
-`tf.reduce_max()`.  In contrast to SparseReduceMax, this Op returns a
-SparseTensor.
-
-Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
-`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
-`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
-with length 1.
-
-If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
-with a single element is returned.  Additionally, the axes can be negative,
-which are interpreted according to the indexing rules in Python.
-
-input_indices: 2-D.  `N x R` matrix with the indices of non-empty values in a
-  SparseTensor, possibly not in canonical ordering.
-input_values: 1-D.  `N` non-empty values corresponding to `input_indices`.
-input_shape: 1-D.  Shape of the input SparseTensor.
-reduction_axes: 1-D.  Length-`K` vector containing the reduction axes.
-keep_dims: If true, retain reduced dimensions with length 1.
-)doc");
-
 REGISTER_OP("SparseReduceSum")
     .Input("input_indices: int64")
     .Input("input_values: T")