Create Tensorflow public documentation for the new type promotion in …

…TF-NumPy.

See the [design doc](https://docs.google.com/document/d/17TPlPVSRL_JA9nZ53w4ztAGuG2C-Hyd3-WIcvw0QNMQ/edit?usp=sharing&resourcekey=0-92wfQRXLZqJQZTUnDy47oA) here for more details about the new type promotion.

PiperOrigin-RevId: 555729892

site/en/guide/_toc.yaml

@@ -55,6 +55,9 @@ toc:
 - title: "NumPy API"
   status: experimental
   path: /guide/tf_numpy
+- title: "NumPy API Type Promotion"
+  status: nightly
+  path: /guide/tf_numpy_type_promotion
 - title: "DTensor concepts"
   path: /guide/dtensor_overview
   status: experimental

site/en/guide/tf_numpy.ipynb

@@ -142,7 +142,7 @@
         "\n",
         "An instance of `tf.experimental.numpy.ndarray`, called **ND Array**, represents a multidimensional dense array of a given `dtype` placed on a certain device. It is an alias to `tf.Tensor`. Check out the ND array class for useful methods like `ndarray.T`, `ndarray.reshape`, `ndarray.ravel` and others.\n",
         "\n",
-        "First create an ND array object, and then invoke different methods. "
+        "First create an ND array object, and then invoke different methods."
       ]
     },
     {
@@ -170,11 +170,28 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "Mub8-dvJMUr4"
+        "id": "-BOY8CGRKEhE"
       },
       "source": [
         "### Type promotion\n",
         "\n",
+        "There are 4 options for type promotion in TensorFlow.\n",
+        "\n",
+        "- By default, TensorFlow raises errors instead of promoting types for mixed type operations.\n",
+        "- Running `tf.numpy.experimental_enable_numpy_behavior()` switches TensorFlow to use `NumPy` type promotion rules (described below).\n",
+        "- After TensorFlow 2.15, there are two new options (refer to [TF NumPy Type Promotion](tf_numpy_type_promotion.ipynb) for details):\n",
+        "  - `tf.numpy.experimental_enable_numpy_behavior(dtype_conversion_mode=\"all\")` uses Jax type promotion rules.\n",
+        "  - `tf.numpy.experimental_enable_numpy_behavior(dtype_conversion_mode=\"safe\")` uses Jax type promotion rules, but disallows certain unsafe promotions."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SXskSHrX5J45"
+      },
+      "source": [
+        "#### NumPy Type Promotion\n",
+        "\n",
         "TensorFlow NumPy APIs have well-defined semantics for converting literals to ND array, as well as for performing type promotion on ND array inputs. Please see [`np.result_type`](https://numpy.org/doc/1.16/reference/generated/numpy.result_type.html) for more details."
       ]
     },
@@ -200,7 +217,7 @@
         "          (tnp.int32, tnp.int64, tnp.float32, tnp.float64)]\n",
         "for i, v1 in enumerate(values):\n",
         "  for v2 in values[i + 1:]:\n",
-        "    print(\"%s + %s => %s\" % \n",
+        "    print(\"%s + %s => %s\" %\n",
         "          (v1.dtype.name, v2.dtype.name, (v1 + v2).dtype.name))"
       ]
     },
@@ -932,8 +949,8 @@
   "metadata": {
     "accelerator": "GPU",
     "colab": {
-      "collapsed_sections": [],
       "name": "tf_numpy.ipynb",
+      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {