diff --git a/tensorflow/core/ops/compat/ops_history.v0.pbtxt b/tensorflow/core/ops/compat/ops_history.v0.pbtxt
index 75ce8ee598ef888664a6f4ea9445d61158fba251..0fb0a6ef0b0b5e3e3b27b5dd6f85b4a193c6ec1f 100644
--- a/tensorflow/core/ops/compat/ops_history.v0.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v0.pbtxt
@@ -14613,6 +14613,35 @@ op {
     type: "type"
   }
 }
+op {
+  name: "Pack"
+  input_arg {
+    name: "values"
+    type_attr: "T"
+    number_attr: "N"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "N"
+    type: "int"
+    has_minimum: true
+    minimum: 1
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  attr {
+    name: "axis"
+    type: "int"
+    default_value {
+      i: 0
+    }
+  }
+}
 op {
   name: "Pad"
   input_arg {
@@ -25202,6 +25231,34 @@ op {
     type: "type"
   }
 }
+op {
+  name: "Unpack"
+  input_arg {
+    name: "value"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+    number_attr: "num"
+  }
+  attr {
+    name: "num"
+    type: "int"
+    has_minimum: true
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  attr {
+    name: "axis"
+    type: "int"
+    default_value {
+      i: 0
+    }
+  }
+}
 op {
   name: "UnsortedSegmentSum"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 20811b6da85408ff4e8693b551b1e5f35cfbef38..3a8a5eed15293d84810fb80133d7d2b68a0048c3 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -8315,8 +8315,16 @@ op {
     name: "T"
     type: "type"
   }
+  attr {
+    name: "axis"
+    type: "int"
+    default_value {
+      i: 0
+    }
+    description: "Dimension along which to pack.  Negative values wrap around, so the\nvalid range is `[-(R+1), R+1)`."
+  }
   summary: "Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor."
-  description: "Packs the `N` tensors in `values` into a tensor with rank one higher than each\ntensor in `values` and shape `[N] + values[0].shape`. The output satisfies\n`output[i, ...] = values[i][...]`.\n\nThis is the opposite of `unpack`."
+  description: "Packs the `N` tensors in `values` into a tensor with rank one higher than each\ntensor in `values`, by packing them along the `axis` dimension.\nGiven a list tensors of shape (A, B, C);\n\nif `axis` == 0 then the `output` tensor will have the shape (`N`, A, B, C).\nif `axis` == 1 then the `output` tensor will have the shape (A, `N`, B, C).\nEtc.\n\nFor example:\n\n```prettyprint\n# \'x\' is [1, 4]\n# \'y\' is [2, 5]\n# \'z\' is [3, 6]\npack([x, y, z]) => [[1, 4], [2, 5], [3, 6]]  # Pack along first dim.\npack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]\n```\n\nThis is the opposite of `unpack`."
 }
 op {
   name: "Pad"
@@ -15009,7 +15017,7 @@ op {
   name: "Unpack"
   input_arg {
     name: "value"
-    description: "1-D or higher, with first dimension `num`."
+    description: "1-D or higher, with `axis` dimension size equal to `num`."
     type_attr: "T"
   }
   output_arg {
@@ -15027,8 +15035,16 @@ op {
     name: "T"
     type: "type"
   }
-  summary: "Unpacks the outer dimension of a rank-`R` tensor into `num` rank-`(R-1)` tensors."
-  description: "Unpacks `num` tensors from `value` by chipping it along the first dimension.\nThe i\'th tensor in `output` is the slice `value[i, ...]`. Each tensor in\n`output` has shape `value.shape[1:]`.\n\nThis is the opposite of `pack`."
+  attr {
+    name: "axis"
+    type: "int"
+    default_value {
+      i: 0
+    }
+    description: "Dimension along which to unpack.  Negative values wrap around, so the\nvalid range is `[-R, R)`."
+  }
+  summary: "Unpacks a given dimension of a rank-`R` tensor into `num` rank-`(R-1)` tensors."
+  description: "Unpacks `num` tensors from `value` by chipping it along the `axis` dimension.\nFor example, given a tensor of shape (A, B, C ,D);\n\nIf `axis` == 0 then the i\'th tensor in `output` is the slice `value[i, :, :, :]`\n  and each tensor in `output` will have shape `(B, C, D)`. (Note that the\n  dimension unpacked along is gone, unlike `split`).\n\nIf `axis` == 1 then the i\'th tensor in `output` is the slice `value[:, i, :, :]`\n  and each tensor in `output` will have shape `(A, C, D)`.\nEtc.\n\nThis is the opposite of `pack`."
 }
 op {
   name: "UnsortedSegmentSum"