Update ops-related pbtxt files.

PiperOrigin-RevId: 173690751

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

@@ -20723,6 +20723,54 @@ op {
     type: "type"
   }
 }
+op {
+  name: "ParallelInterleaveDataset"
+  input_arg {
+    name: "input_dataset"
+    type: DT_VARIANT
+  }
+  input_arg {
+    name: "other_arguments"
+    type_list_attr: "Targuments"
+  }
+  input_arg {
+    name: "cycle_length"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "block_length"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "sloppy"
+    type: DT_BOOL
+  }
+  output_arg {
+    name: "handle"
+    type: DT_VARIANT
+  }
+  attr {
+    name: "f"
+    type: "func"
+  }
+  attr {
+    name: "Targuments"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "output_types"
+    type: "list(type)"
+    has_minimum: true
+    minimum: 1
+  }
+  attr {
+    name: "output_shapes"
+    type: "list(shape)"
+    has_minimum: true
+    minimum: 1
+  }
+}
 op {
   name: "ParallelMapDataset"
   input_arg {

tensorflow/core/ops/ops.pbtxt

@@ -16181,6 +16181,57 @@ op {
   summary: "Interleave the values from the `data` tensors into a single tensor."
   description: "Builds a merged tensor such that\n\n```python\n    merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...]\n```\n\nFor example, if each `indices[m]` is scalar or vector, we have\n\n```python\n    # Scalar indices:\n    merged[indices[m], ...] = data[m][...]\n\n    # Vector indices:\n    merged[indices[m][i], ...] = data[m][i, ...]\n```\n\nEach `data[i].shape` must start with the corresponding `indices[i].shape`,\nand the rest of `data[i].shape` must be constant w.r.t. `i`.  That is, we\nmust have `data[i].shape = indices[i].shape + constant`.  In terms of this\n`constant`, the output shape is\n\n    merged.shape = [max(indices)] + constant\n\nValues may be merged in parallel, so if an index appears in both `indices[m][i]`\nand `indices[n][j]`, the result may be invalid. This differs from the normal\nDynamicStitch operator that defines the behavior in that case.\n\nFor example:\n\n```python\n    indices[0] = 6\n    indices[1] = [4, 1]\n    indices[2] = [[5, 2], [0, 3]]\n    data[0] = [61, 62]\n    data[1] = [[41, 42], [11, 12]]\n    data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]]\n    merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42],\n              [51, 52], [61, 62]]\n```\n\nThis method can be used to merge partitions created by `dynamic_partition`\nas illustrated on the following example:\n\n```python\n    # Apply function (increments x_i) on elements for which a certain condition\n    # apply (x_i != -1 in this example).\n    x=tf.constant([0.1, -1., 5.2, 4.3, -1., 7.4])\n    condition_mask=tf.not_equal(x,tf.constant(-1.))\n    partitioned_data = tf.dynamic_partition(\n        x, tf.cast(condition_mask, tf.int32) , 2)\n    partitioned_data[1] = partitioned_data[1] + 1.0\n    condition_indices = tf.dynamic_partition(\n        tf.range(tf.shape(x)[0]), tf.cast(condition_mask, tf.int32) , 2)\n    x = tf.dynamic_stitch(condition_indices, partitioned_data)\n    # Here x=[1.1, -1., 6.2, 5.3, -1, 8.4], the -1. values remain\n    # unchanged.\n```\n\n<div style=\"width:70%; margin:auto; margin-bottom:10px; margin-top:20px;\">\n<img style=\"width:100%\" src=\"https://www.tensorflow.org/images/DynamicStitch.png\" alt>\n</div>"
 }
+op {
+  name: "ParallelInterleaveDataset"
+  input_arg {
+    name: "input_dataset"
+    type: DT_VARIANT
+  }
+  input_arg {
+    name: "other_arguments"
+    type_list_attr: "Targuments"
+  }
+  input_arg {
+    name: "cycle_length"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "block_length"
+    type: DT_INT64
+  }
+  input_arg {
+    name: "sloppy"
+    type: DT_BOOL
+  }
+  output_arg {
+    name: "handle"
+    type: DT_VARIANT
+  }
+  attr {
+    name: "f"
+    type: "func"
+    description: "A function mapping elements of `input_dataset`, concatenated with\n`other_arguments`, to a Dataset variant that contains elements matching\n`output_types` and `output_shapes`."
+  }
+  attr {
+    name: "Targuments"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "output_types"
+    type: "list(type)"
+    has_minimum: true
+    minimum: 1
+  }
+  attr {
+    name: "output_shapes"
+    type: "list(shape)"
+    has_minimum: true
+    minimum: 1
+  }
+  summary: "Creates a dataset that applies `f` to the outputs of `input_dataset`."
+  description: "The resulting dataset is similar to the `InterleaveDataset`, with the exception\nthat if retrieving the next value from a dataset would cause the requester to\nblock, it will skip that input dataset. This dataset is especially useful\nwhen loading data from a variable-latency datastores (e.g. HDFS, GCS), as it\nallows the training step to proceed so long as some data is available.\n\n!! WARNING !! This dataset is not deterministic!"
+}
 op {
   name: "ParallelMapDataset"
   input_arg {
@@ -25772,53 +25823,6 @@ op {
   summary: "Return a slice from \'input\'."
   description: "The output tensor is a tensor with dimensions described by \'size\'\nwhose values are extracted from \'input\' starting at the offsets in\n\'begin\'.\n\n*Requirements*:\n  0 <= begin[i] <= begin[i] + size[i] <= Di  for i in [0, n)"
 }
-op {
-  name: "SloppyInterleaveDataset"
-  input_arg {
-    name: "input_dataset"
-    type: DT_VARIANT
-  }
-  input_arg {
-    name: "other_arguments"
-    type_list_attr: "Targuments"
-  }
-  input_arg {
-    name: "cycle_length"
-    type: DT_INT64
-  }
-  input_arg {
-    name: "block_length"
-    type: DT_INT64
-  }
-  output_arg {
-    name: "handle"
-    type: DT_VARIANT
-  }
-  attr {
-    name: "f"
-    type: "func"
-    description: "A function mapping elements of `input_dataset`, concatenated with\n`other_arguments`, to a Dataset variant that contains elements matching\n`output_types` and `output_shapes`."
-  }
-  attr {
-    name: "Targuments"
-    type: "list(type)"
-    has_minimum: true
-  }
-  attr {
-    name: "output_types"
-    type: "list(type)"
-    has_minimum: true
-    minimum: 1
-  }
-  attr {
-    name: "output_shapes"
-    type: "list(shape)"
-    has_minimum: true
-    minimum: 1
-  }
-  summary: "Creates a dataset that applies `f` to the outputs of `input_dataset`."
-  description: "The resulting dataset is similar to the `InterleaveDataset`, with the exception\nthat if retrieving the next value from a dataset would cause the requester to\nblock, it will skip that input dataset. This dataset is especially useful\nwhen loading data from a variable-latency datastores (e.g. HDFS, GCS), as it\nallows the training step to proceed so long as some data is available.\n\n!! WARNING !! This dataset is not deterministic!"
-}
 op {
   name: "Softmax"
   input_arg {