Update ops-related pbtxt files.

PiperOrigin-RevId: 161478409

2 files changed, 473 insertions, 30 deletions

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index f872ef3cb5..0198d4ff22 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -166,6 +166,30 @@ op {
   }
 }
 op {
+  name: "Acosh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
+op {
   name: "Add"
   input_arg {
     name: "x"
@@ -1880,6 +1904,30 @@ op {
   }
 }
 op {
+  name: "Asinh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
+op {
   name: "Assert"
   input_arg {
     name: "condition"
@@ -2084,6 +2132,30 @@ op {
   }
 }
 op {
+  name: "Atanh"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+        type: DT_DOUBLE
+        type: DT_COMPLEX64
+        type: DT_COMPLEX128
+      }
+    }
+  }
+}
+op {
   name: "AudioSpectrogram"
   input_arg {
     name: "input"
@@ -9067,6 +9139,60 @@ op {
   is_stateful: true
 }
 op {
+  name: "FixedLengthRecordReaderV2"
+  output_arg {
+    name: "reader_handle"
+    type: DT_RESOURCE
+  }
+  attr {
+    name: "header_bytes"
+    type: "int"
+    default_value {
+      i: 0
+    }
+  }
+  attr {
+    name: "record_bytes"
+    type: "int"
+  }
+  attr {
+    name: "footer_bytes"
+    type: "int"
+    default_value {
+      i: 0
+    }
+  }
+  attr {
+    name: "hop_bytes"
+    type: "int"
+    default_value {
+      i: 0
+    }
+  }
+  attr {
+    name: "container"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  attr {
+    name: "shared_name"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  attr {
+    name: "encoding"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  is_stateful: true
+}
+op {
   name: "FixedUnigramCandidateSampler"
   input_arg {
     name: "true_classes"
@@ -18032,6 +18158,30 @@ op {
   is_stateful: true
 }
 op {
+  name: "QueueIsClosed"
+  input_arg {
+    name: "handle"
+    type: DT_STRING
+    is_ref: true
+  }
+  output_arg {
+    name: "is_closed"
+    type: DT_BOOL
+  }
+}
+op {
+  name: "QueueIsClosedV2"
+  input_arg {
+    name: "handle"
+    type: DT_RESOURCE
+  }
+  output_arg {
+    name: "is_closed"
+    type: DT_BOOL
+  }
+  is_stateful: true
+}
+op {
   name: "QueueSize"
   input_arg {
     name: "handle"
@@ -21968,6 +22118,95 @@ op {
   is_stateful: true
 }
 op {
+  name: "SampleDistortedBoundingBoxV2"
+  input_arg {
+    name: "image_size"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "bounding_boxes"
+    type: DT_FLOAT
+  }
+  input_arg {
+    name: "min_object_covered"
+    type: DT_FLOAT
+  }
+  output_arg {
+    name: "begin"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "size"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "bboxes"
+    type: DT_FLOAT
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_UINT8
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  attr {
+    name: "seed"
+    type: "int"
+    default_value {
+      i: 0
+    }
+  }
+  attr {
+    name: "seed2"
+    type: "int"
+    default_value {
+      i: 0
+    }
+  }
+  attr {
+    name: "aspect_ratio_range"
+    type: "list(float)"
+    default_value {
+      list {
+        f: 0.75
+        f: 1.33
+      }
+    }
+  }
+  attr {
+    name: "area_range"
+    type: "list(float)"
+    default_value {
+      list {
+        f: 0.05
+        f: 1
+      }
+    }
+  }
+  attr {
+    name: "max_attempts"
+    type: "int"
+    default_value {
+      i: 100
+    }
+  }
+  attr {
+    name: "use_image_if_no_bounding_boxes"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+  is_stateful: true
+}
+op {
   name: "Save"
   input_arg {
     name: "filename"
@@ -25924,6 +26163,45 @@ op {
   }
 }
 op {
+  name: "SparseSlice"
+  input_arg {
+    name: "indices"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "values"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "shape"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "start"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "size"
+    type: DT_INT64
+  }
+  output_arg {
+    name: "output_indices"
+    type: DT_INT64
+  }
+  output_arg {
+    name: "output_values"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output_shape"
+    type: DT_INT64
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+}
+op {
   name: "SparseSoftmax"
   input_arg {
     name: "sp_indices"
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 7270e31fe4..064b5c62bb 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -205,6 +205,7 @@ op {
     type: "type"
     allowed_values {
       list {
+        type: DT_HALF
         type: DT_FLOAT
         type: DT_DOUBLE
         type: DT_COMPLEX64
@@ -212,7 +213,7 @@ op {
       }
     }
   }
-  summary: "Computes acosh of x element-wise."
+  summary: "Computes inverse hyperbolic cosine of x element-wise."
 }
 op {
   name: "Add"
@@ -1924,6 +1925,7 @@ op {
     type: "type"
     allowed_values {
       list {
+        type: DT_HALF
         type: DT_FLOAT
         type: DT_DOUBLE
         type: DT_COMPLEX64
@@ -1931,7 +1933,7 @@ op {
       }
     }
   }
-  summary: "Computes asinh of x element-wise."
+  summary: "Computes inverse hyperbolic sine of x element-wise."
 }
 op {
   name: "Assert"
@@ -2179,6 +2181,7 @@ op {
     type: "type"
     allowed_values {
       list {
+        type: DT_HALF
         type: DT_FLOAT
         type: DT_DOUBLE
         type: DT_COMPLEX64
@@ -2186,7 +2189,7 @@ op {
       }
     }
   }
-  summary: "Computes atanh of x element-wise."
+  summary: "Computes inverse hyperbolic tangent of x element-wise."
 }
 op {
   name: "AudioSpectrogram"
@@ -8391,6 +8394,14 @@ op {
     }
     description: "If non-empty, this reader is named in the given bucket\nwith this shared_name. Otherwise, the node name is used instead."
   }
+  attr {
+    name: "encoding"
+    type: "string"
+    default_value {
+      s: ""
+    }
+    description: "The type of encoding for the file. Currently ZLIB and GZIP\nare supported. Defaults to none."
+  }
   summary: "A Reader that outputs fixed-length records from a file."
   is_stateful: true
 }
@@ -17533,6 +17544,36 @@ op {
   is_stateful: true
 }
 op {
+  name: "QueueIsClosed"
+  input_arg {
+    name: "handle"
+    description: "The handle to a queue."
+    type: DT_STRING
+    is_ref: true
+  }
+  output_arg {
+    name: "is_closed"
+    type: DT_BOOL
+  }
+  summary: "Returns true if queue is closed."
+  description: "This operation returns true if the queue is closed and false if the queue\nis open."
+}
+op {
+  name: "QueueIsClosedV2"
+  input_arg {
+    name: "handle"
+    description: "The handle to a queue."
+    type: DT_RESOURCE
+  }
+  output_arg {
+    name: "is_closed"
+    type: DT_BOOL
+  }
+  summary: "Returns true if queue is closed."
+  description: "This operation returns true if the queue is closed and false if the queue\nis open."
+  is_stateful: true
+}
+op {
   name: "QueueSize"
   input_arg {
     name: "handle"
@@ -21803,6 +21844,109 @@ op {
   is_stateful: true
 }
 op {
+  name: "SampleDistortedBoundingBoxV2"
+  input_arg {
+    name: "image_size"
+    description: "1-D, containing `[height, width, channels]`."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "bounding_boxes"
+    description: "3-D with shape `[batch, N, 4]` describing the N bounding boxes\nassociated with the image."
+    type: DT_FLOAT
+  }
+  input_arg {
+    name: "min_object_covered"
+    description: "The cropped area of the image must contain at least this\nfraction of any bounding box supplied. The value of this parameter should be\nnon-negative. In the case of 0, the cropped area does not need to overlap\nany of the bounding boxes supplied."
+    type: DT_FLOAT
+  }
+  output_arg {
+    name: "begin"
+    description: "1-D, containing `[offset_height, offset_width, 0]`. Provide as input to\n`tf.slice`."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "size"
+    description: "1-D, containing `[target_height, target_width, -1]`. Provide as input to\n`tf.slice`."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "bboxes"
+    description: "3-D with shape `[1, 1, 4]` containing the distorted bounding box.\nProvide as input to `tf.image.draw_bounding_boxes`."
+    type: DT_FLOAT
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_UINT8
+        type: DT_INT8
+        type: DT_INT16
+        type: DT_INT32
+        type: DT_INT64
+      }
+    }
+  }
+  attr {
+    name: "seed"
+    type: "int"
+    default_value {
+      i: 0
+    }
+    description: "If either `seed` or `seed2` are set to non-zero, the random number\ngenerator is seeded by the given `seed`.  Otherwise, it is seeded by a random\nseed."
+  }
+  attr {
+    name: "seed2"
+    type: "int"
+    default_value {
+      i: 0
+    }
+    description: "A second seed to avoid seed collision."
+  }
+  attr {
+    name: "aspect_ratio_range"
+    type: "list(float)"
+    default_value {
+      list {
+        f: 0.75
+        f: 1.33
+      }
+    }
+    description: "The cropped area of the image must have an aspect ratio =\nwidth / height within this range."
+  }
+  attr {
+    name: "area_range"
+    type: "list(float)"
+    default_value {
+      list {
+        f: 0.05
+        f: 1
+      }
+    }
+    description: "The cropped area of the image must contain a fraction of the\nsupplied image within in this range."
+  }
+  attr {
+    name: "max_attempts"
+    type: "int"
+    default_value {
+      i: 100
+    }
+    description: "Number of attempts at generating a cropped region of the image\nof the specified constraints. After `max_attempts` failures, return the entire\nimage."
+  }
+  attr {
+    name: "use_image_if_no_bounding_boxes"
+    type: "bool"
+    default_value {
+      b: false
+    }
+    description: "Controls behavior if no bounding boxes supplied.\nIf true, assume an implicit bounding box covering the whole input. If false,\nraise an error."
+  }
+  summary: "Generate a single randomly distorted bounding box for an image."
+  description: "Bounding box annotations are often supplied in addition to ground-truth labels\nin image recognition or object localization tasks. A common technique for\ntraining such a system is to randomly distort an image while preserving\nits content, i.e. *data augmentation*. This Op outputs a randomly distorted\nlocalization of an object, i.e. bounding box, given an `image_size`,\n`bounding_boxes` and a series of constraints.\n\nThe output of this Op is a single bounding box that may be used to crop the\noriginal image. The output is returned as 3 tensors: `begin`, `size` and\n`bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the\nimage. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize\nwhat the bounding box looks like.\n\nBounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The\nbounding box coordinates are floats in `[0.0, 1.0]` relative to the width and\nheight of the underlying image.\n\nFor example,\n\n```python\n    # Generate a single distorted bounding box.\n    begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(\n        tf.shape(image),\n        bounding_boxes=bounding_boxes)\n\n    # Draw the bounding box in an image summary.\n    image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),\n                                                  bbox_for_draw)\n    tf.image_summary(\'images_with_box\', image_with_box)\n\n    # Employ the bounding box to distort the image.\n    distorted_image = tf.slice(image, begin, size)\n```\n\nNote that if no bounding box information is available, setting\n`use_image_if_no_bounding_boxes = true` will assume there is a single implicit\nbounding box covering the whole image. If `use_image_if_no_bounding_boxes` is\nfalse and no bounding boxes are supplied, an error is raised."
+  is_stateful: true
+}
+op {
   name: "Save"
   input_arg {
     name: "filename"
@@ -26187,6 +26331,54 @@ op {
   description: "Read @{$math_ops#segmentation$the section on segmentation} for an explanation of\nsegments.\n\nLike `SegmentSum`, but `segment_ids` can have rank less than `data`\'s first\ndimension, selecting a subset of dimension 0, specified by `indices`.\n\nFor example:\n\n```python\nc = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])\n\n# Select two rows, one segment.\ntf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 0]))\n# => [[0 0 0 0]]\n\n# Select two rows, two segment.\ntf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 1]))\n# => [[ 1  2  3  4]\n#     [-1 -2 -3 -4]]\n\n# Select all rows, two segments.\ntf.sparse_segment_sum(c, tf.constant([0, 1, 2]), tf.constant([0, 0, 1]))\n# => [[0 0 0 0]\n#     [5 6 7 8]]\n\n# Which is equivalent to:\ntf.segment_sum(c, tf.constant([0, 0, 1]))\n```"
 }
 op {
+  name: "SparseSlice"
+  input_arg {
+    name: "indices"
+    description: "2-D tensor represents the indices of the sparse tensor."
+    type: DT_INT64
+  }
+  input_arg {
+    name: "values"
+    description: "1-D tensor represents the values of the sparse tensor."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "shape"
+    description: "1-D. tensor represents the shape of the sparse tensor."
+    type: DT_INT64
+  }
+  input_arg {
+    name: "start"
+    description: "1-D. tensor represents the start of the slice."
+    type: DT_INT64
+  }
+  input_arg {
+    name: "size"
+    description: "1-D. tensor represents the size of the slice.\noutput indices: A list of 1-D tensors represents the indices of the output\nsparse tensors."
+    type: DT_INT64
+  }
+  output_arg {
+    name: "output_indices"
+    type: DT_INT64
+  }
+  output_arg {
+    name: "output_values"
+    description: "A list of 1-D tensors represents the values of the output sparse\ntensors."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output_shape"
+    description: "A list of 1-D tensors represents the shape of the output sparse\ntensors."
+    type: DT_INT64
+  }
+  attr {
+    name: "T"
+    type: "type"
+  }
+  summary: "Slice a `SparseTensor` based on the `start` and `size`."
+  description: "For example, if the input is\n\n    input_tensor = shape = [2, 7]\n    [    a   d e  ]\n    [b c          ]\n\nGraphically the output tensors are:\n\n    sparse_slice([0, 0], [2, 4]) = shape = [2, 4]\n    [    a  ]\n    [b c    ]\n\n    sparse_slice([0, 4], [2, 3]) = shape = [2, 3]\n    [ d e  ]\n    [      ]"
+}
+op {
   name: "SparseSoftmax"
   input_arg {
     name: "sp_indices"
@@ -28193,33 +28385,6 @@ op {
   is_stateful: true
 }
 op {
-  name: "LMDBReader"
-  output_arg {
-    name: "reader_handle"
-    description: "The handle to reference the Reader."
-    type: DT_STRING
-    is_ref: true
-  }
-  attr {
-    name: "container"
-    type: "string"
-    default_value {
-      s: ""
-    }
-    description: "If non-empty, this reader is placed in the given container.\nOtherwise, a default container is used."
-  }
-  attr {
-    name: "shared_name"
-    type: "string"
-    default_value {
-      s: ""
-    }
-    description: "If non-empty, this reader is named in the given bucket\nwith this shared_name. Otherwise, the node name is used instead."
-  }
-  summary: "A Reader that outputs the records from a LMDB database."
-  is_stateful: true
-}
-op {
   name: "TakeDataset"
   input_arg {
     name: "input_dataset"