diff --git a/src/tf-metadata.json b/src/tf-metadata.json
index 2b27d97c58..2ea46a23ba 100644
--- a/src/tf-metadata.json
+++ b/src/tf-metadata.json
@@ -52431,6 +52431,94 @@
       "summary": "Outputs deterministic pseudorandom random integers from a uniform distribution."
     }
   },
+  {
+    "name": "StatelessSampleDistortedBoundingBox",
+    "schema": {
+      "attributes": [
+        {
+          "description": "Must be one of the following: `uint8`, `int8`, `int16`, `int32`, `int64`.",
+          "name": "T",
+          "type": "type"
+        },
+        {
+          "description": "Must be one of the following: `int32`, `int64`.",
+          "name": "Tseed",
+          "type": "type"
+        },
+        {
+          "default": [
+            0.75,
+            1.3300000429153442
+          ],
+          "description": "The cropped area of the image must have an aspect ratio =\nwidth / height within this range.",
+          "name": "aspect_ratio_range",
+          "type": "float32[]"
+        },
+        {
+          "default": [
+            0.05000000074505806,
+            1.0
+          ],
+          "description": "The cropped area of the image must contain a fraction of the\nsupplied image within this range.",
+          "name": "area_range",
+          "type": "float32[]"
+        },
+        {
+          "default": 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.",
+          "name": "max_attempts",
+          "type": "int64"
+        },
+        {
+          "default": 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.",
+          "name": "use_image_if_no_bounding_boxes",
+          "type": "boolean"
+        }
+      ],
+      "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 its\ncontent, i.e. *data augmentation*. This Op, given the same `seed`,\ndeterministically outputs a randomly distorted localization of an object, i.e.\nbounding box, given an `image_size`, `bounding_boxes` and a series of\nconstraints.\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\nthe height of the underlying image.\n\nThe output of this Op is guaranteed to be the same given the same `seed` and is\nindependent of how many times the function is called, and independent of global\nseed settings (e.g. `tf.random.set_seed`).\n\nExample usage:\n\n>>> image = np.array([[[1], [2], [3]], [[4], [5], [6]], [[7], [8], [9]]])\n>>> bbox = tf.constant(\n...   [0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])\n>>> seed = (1, 2)\n>>> # Generate a single distorted bounding box.\n>>> bbox_begin, bbox_size, bbox_draw = (\n...   tf.image.stateless_sample_distorted_bounding_box(\n...     tf.shape(image), bounding_boxes=bbox, seed=seed))\n>>> # Employ the bounding box to distort the image.\n>>> tf.slice(image, bbox_begin, bbox_size)\n<tf.Tensor: shape=(2, 2, 1), dtype=int64, numpy=\narray([[[1],\n        [2]],\n       [[4],\n        [5]]])>\n>>> # Draw the bounding box in an image summary.\n>>> colors = np.array([[1.0, 0.0, 0.0], [0.0, 0.0, 1.0]])\n>>> tf.image.draw_bounding_boxes(\n...   tf.expand_dims(tf.cast(image, tf.float32),0), bbox_draw, colors)\n<tf.Tensor: shape=(1, 3, 3, 1), dtype=float32, numpy=\narray([[[[1.],\n         [1.],\n         [3.]],\n        [[1.],\n         [1.],\n         [6.]],\n        [[7.],\n         [8.],\n         [9.]]]], dtype=float32)>\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.",
+      "inputs": [
+        {
+          "description": "1-D, containing `[height, width, channels]`.",
+          "name": "image_size",
+          "typeAttr": "T"
+        },
+        {
+          "description": "3-D with shape `[batch, N, 4]` describing the N bounding boxes\nassociated with the image.",
+          "name": "bounding_boxes",
+          "type": 1
+        },
+        {
+          "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.",
+          "name": "min_object_covered",
+          "type": 1
+        },
+        {
+          "description": "1-D with shape `[2]`. The seed to the random number generator. Must have dtype\n`int32` or `int64`. (When using XLA, only `int32` is allowed.)",
+          "name": "seed",
+          "typeAttr": "Tseed"
+        }
+      ],
+      "outputs": [
+        {
+          "description": "1-D, containing `[offset_height, offset_width, 0]`. Provide as input to\n`tf.slice`.",
+          "name": "begin",
+          "typeAttr": "T"
+        },
+        {
+          "description": "1-D, containing `[target_height, target_width, -1]`. Provide as input to\n`tf.slice`.",
+          "name": "size",
+          "typeAttr": "T"
+        },
+        {
+          "description": "3-D with shape `[1, 1, 4]` containing the distorted bounding box.\nProvide as input to `tf.image.draw_bounding_boxes`.",
+          "name": "bboxes",
+          "type": 1
+        }
+      ],
+      "summary": "Generate a randomly distorted bounding box for an image deterministically."
+    }
+  },
   {
     "name": "StatelessTruncatedNormal",
     "schema": {