Update ops-related pbtxt files.

PiperOrigin-RevId: 162989008

tensorflow/core/ops/compat/ops_history.v1.pbtxt

@@ -6002,6 +6002,22 @@ op {
     }
   }
 }
+op {
+  name: "DebugGradientIdentity"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  allows_uninitialized_input: true
+}
 op {
   name: "DebugIdentity"
   input_arg {

tensorflow/core/ops/ops.pbtxt

@@ -5784,6 +5784,24 @@ op {
   summary: "Compute the cumulative sum of the tensor `x` along `axis`."
   description: "By default, this op performs an inclusive cumsum, which means that the first\nelement of the input is identical to the first element of the output:\n\n```python\ntf.cumsum([a, b, c])  # => [a, a + b, a + b + c]\n```\n\nBy setting the `exclusive` kwarg to `True`, an exclusive cumsum is\nperformed instead:\n\n```python\ntf.cumsum([a, b, c], exclusive=True)  # => [0, a, a + b]\n```\n\nBy setting the `reverse` kwarg to `True`, the cumsum is performed in the\nopposite direction:\n\n```python\ntf.cumsum([a, b, c], reverse=True)  # => [a + b + c, b + c, c]\n```\n\nThis is more efficient than using separate `tf.reverse` ops.\n\nThe `reverse` and `exclusive` kwargs can also be combined:\n\n```python\ntf.cumsum([a, b, c], exclusive=True, reverse=True)  # => [b + c, c, 0]\n```"
 }
+op {
+  name: "DebugGradientIdentity"
+  input_arg {
+    name: "input"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  summary: "Identity op for gradient debugging."
+  description: "This op is hidden from public in Python. It is used by TensorFlow Debugger to\nregister gradient tensors for gradient debugging."
+  allows_uninitialized_input: true
+}
 op {
   name: "DebugIdentity"
   input_arg {