Update ops-related pbtxt files.

PiperOrigin-RevId: 173145770

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

@@ -9582,6 +9582,18 @@ op {
     }
   }
 }
+op {
+  name: "DeserializeIterator"
+  input_arg {
+    name: "resource_handle"
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "serialized"
+    type: DT_VARIANT
+  }
+  is_stateful: true
+}
 op {
   name: "DeserializeManySparse"
   input_arg {
@@ -31465,6 +31477,18 @@ op {
     }
   }
 }
+op {
+  name: "SerializeIterator"
+  input_arg {
+    name: "resource_handle"
+    type: DT_RESOURCE
+  }
+  output_arg {
+    name: "serialized"
+    type: DT_VARIANT
+  }
+  is_stateful: true
+}
 op {
   name: "SerializeManySparse"
   input_arg {

tensorflow/core/ops/ops.pbtxt

@@ -6997,6 +6997,21 @@ op {
   summary: "Dequantize the \'input\' tensor into a float Tensor."
   description: "[min_range, max_range] are scalar floats that specify the range for\nthe \'input\' data. The \'mode\' attribute controls exactly which calculations are\nused to convert the float values to their quantized equivalents.\n\nIn \'MIN_COMBINED\' mode, each value of the tensor will undergo the following:\n\n```\nif T == qint8, in[i] += (range(T) + 1)/ 2.0\nout[i] = min_range + (in[i]* (max_range - min_range) / range(T))\n```\nhere `range(T) = numeric_limits<T>::max() - numeric_limits<T>::min()`\n\n*MIN_COMBINED Mode Example*\n\nIf the input comes from a QuantizedRelu6, the output type is\nquint8 (range of 0-255) but the possible range of QuantizedRelu6 is\n0-6.  The min_range and max_range values are therefore 0.0 and 6.0.\nDequantize on quint8 will take each value, cast to float, and multiply\nby 6 / 255.\nNote that if quantizedtype is qint8, the operation will additionally add\neach value by 128 prior to casting.\n\nIf the mode is \'MIN_FIRST\', then this approach is used:\n\n```c++\nnumber_of_steps = 1 << (# of bits in T)\nrange_adjust = number_of_steps / (number_of_steps - 1)\nrange = (range_max - range_min) * range_adjust\nrange_scale = range / number_of_steps\nconst double offset_input = static_cast<double>(input) - lowest_quantized;\nresult = range_min + ((input - numeric_limits<T>::min()) * range_scale)\n```\n\n*SCALED mode Example*\n\n`SCALED` mode matches the quantization approach used in\n`QuantizeAndDequantize{V2|V3}`.\n\nIf the mode is `SCALED`, we do not use the full range of the output type,\nchoosing to elide the lowest possible value for symmetry (e.g., output range is\n-127 to 127, not -128 to 127 for signed 8 bit quantization), so that 0.0 maps to\n0.\n\nWe first find the range of values in our tensor. The\nrange we use is always centered on 0, so we find m such that\n```c++\n  m = max(abs(input_min), abs(input_max))\n```\n\nOur input tensor range is then `[-m, m]`.\n\nNext, we choose our fixed-point quantization buckets, `[min_fixed, max_fixed]`.\nIf T is signed, this is\n```\n  num_bits = sizeof(T) * 8\n  [min_fixed, max_fixed] =\n      [-(1 << (num_bits - 1) - 1), (1 << (num_bits - 1)) - 1]\n```\n\nOtherwise, if T is unsigned, the fixed-point range is\n```\n  [min_fixed, max_fixed] = [0, (1 << num_bits) - 1]\n```\n\nFrom this we compute our scaling factor, s:\n```c++\n  s = (2 * m) / (max_fixed - min_fixed)\n```\n\nNow we can dequantize the elements of our tensor:\n```c++\nresult = input * s\n```"
 }
+op {
+  name: "DeserializeIterator"
+  input_arg {
+    name: "resource_handle"
+    description: "A handle to an iterator resource."
+    type: DT_RESOURCE
+  }
+  input_arg {
+    name: "serialized"
+    description: "A variant tensor storing the state of the iterator contained in the\nresource."
+    type: DT_VARIANT
+  }
+  summary: "Converts the given variant tensor to an iterator and stores it in the given resource."
+  is_stateful: true
+}
 op {
   name: "DeserializeManySparse"
   input_arg {
@@ -23025,19 +23040,6 @@ op {
   description: "Reads a tensor stored in one or several files. If there are several files (for\ninstance because a tensor was saved as slices), `file_pattern` may contain\nwildcard symbols (`*` and `?`) in the filename portion only, not in the\ndirectory portion.\n\nIf a `file_pattern` matches several files, `preferred_shard` can be used to hint\nin which file the requested tensor is likely to be found. This op will first\nopen the file at index `preferred_shard` in the list of matching files and try\nto restore tensors from that file.  Only if some tensors or tensor slices are\nnot found in that first file, then the Op opens all the files. Setting\n`preferred_shard` to match the value passed as the `shard` input\nof a matching `Save` Op may speed up Restore.  This attribute only affects\nperformance, not correctness.  The default value -1 means files are processed in\norder.\n\nSee also `RestoreSlice`."
   is_stateful: true
 }
-op {
-  name: "RestoreIterator"
-  input_arg {
-    name: "iterator"
-    type: DT_RESOURCE
-  }
-  input_arg {
-    name: "path"
-    type: DT_STRING
-  }
-  summary: "Restores the state of the `iterator` from the checkpoint saved at `path` using \"SaveIterator\"."
-  is_stateful: true
-}
 op {
   name: "RestoreSlice"
   input_arg {
@@ -23632,20 +23634,6 @@ op {
   description: "The size of `tensor_names` must match the number of tensors in `data`. `data[i]`\nis written to `filename` with name `tensor_names[i]`.\n\nSee also `SaveSlices`."
   is_stateful: true
 }
-op {
-  name: "SaveIterator"
-  input_arg {
-    name: "iterator"
-    type: DT_RESOURCE
-  }
-  input_arg {
-    name: "path"
-    type: DT_STRING
-  }
-  summary: "Saves the state of the `iterator` at `path`."
-  description: "This state can be restored using \"RestoreIterator\"."
-  is_stateful: true
-}
 op {
   name: "SaveSlices"
   input_arg {
@@ -24990,6 +24978,21 @@ op {
   }
   summary: "Computes gradients for the scaled exponential linear (Selu) operation."
 }
+op {
+  name: "SerializeIterator"
+  input_arg {
+    name: "resource_handle"
+    description: "A handle to an iterator resource."
+    type: DT_RESOURCE
+  }
+  output_arg {
+    name: "serialized"
+    description: "A variant tensor storing the state of the iterator contained in the\nresource."
+    type: DT_VARIANT
+  }
+  summary: "Converts the given `resource_handle` representing an iterator to a variant tensor."
+  is_stateful: true
+}
 op {
   name: "SerializeManySparse"
   input_arg {