Update ops-related pbtxt files.

Change: 120184745

tensorflow/core/ops/compat/ops_history.v0.pbtxt

@@ -17056,6 +17056,58 @@ op {
     }
   }
 }
+op {
+  name: "SparseReduceSum"
+  input_arg {
+    name: "input_indices"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "input_values"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "input_shape"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "reduction_axes"
+    type: DT_INT32
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "keep_dims"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT64
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_UINT16
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_HALF
+      }
+    }
+  }
+}
 op {
   name: "SparseReorder"
   input_arg {

tensorflow/core/ops/ops.pbtxt

@@ -10363,6 +10363,66 @@ op {
   summary: "Multiply matrix \"a\" by matrix \"b\"."
   description: "The inputs must be two-dimensional matrices and the inner dimension of \"a\" must\nmatch the outer dimension of \"b\". This op is optimized for the case where at\nleast one of \"a\" or \"b\" is sparse. The breakeven for using this versus a dense\nmatrix multiply on one platform was 30% zero values in the sparse matrix."
 }
+op {
+  name: "SparseReduceSum"
+  input_arg {
+    name: "input_indices"
+    description: "2-D.  `N x R` matrix with the indices of non-empty values in a\nSparseTensor, possibly not in canonical ordering."
+    type: DT_INT64
+  }
+  input_arg {
+    name: "input_values"
+    description: "1-D.  `N` non-empty values corresponding to `input_indices`."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "input_shape"
+    description: "1-D.  Shape of the input SparseTensor."
+    type: DT_INT64
+  }
+  input_arg {
+    name: "reduction_axes"
+    description: "1-D.  Length-`K` vector containing the reduction axes."
+    type: DT_INT32
+  }
+  output_arg {
+    name: "output"
+    description: "`R-K`-D.  The reduced Tensor."
+    type_attr: "T"
+  }
+  attr {
+    name: "keep_dims"
+    type: "bool"
+    default_value {
+      b: false
+    }
+    description: "If true, retain reduced dimensions with length 1."
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_INT64
+        type: DT_INT32
+        type: DT_UINT8
+        type: DT_UINT16
+        type: DT_INT16
+        type: DT_INT8
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+        type: DT_QINT8
+        type: DT_QUINT8
+        type: DT_QINT32
+        type: DT_HALF
+      }
+    }
+  }
+  summary: "Computes the sum of elements across dimensions of a SparseTensor."
+  description: "This Op takes a SparseTensor and is the sparse counterpart to\n`tf.reduce_sum()`.  In particular, this Op also returns a dense `Tensor`\ninstead of a sparse one.\n\nReduces `sp_input` along the dimensions given in `reduction_axes`.  Unless\n`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in\n`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained\nwith length 1.\n\nIf `reduction_axes` has no entries, all dimensions are reduced, and a tensor\nwith a single element is returned."
+}
 op {
   name: "SparseReorder"
   input_arg {