Resolved merge conflicts

.github/workflows/arm-cd.yml

@@ -24,6 +24,9 @@ on:
   schedule:
     - cron: '0 8 * * *'
 
+permissions:
+  contents: read
+
 jobs:
   build:
     if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks

.github/workflows/arm-ci-extended-cpp.yml

@@ -22,6 +22,9 @@ on:
   schedule:
     - cron: '0 2 * * *'
 
+permissions:
+  contents: read
+
 jobs:
   build:
     if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks

.github/workflows/arm-ci-extended.yml

@@ -22,6 +22,9 @@ on:
   schedule:
     - cron: '0 4 * * *'
 
+permissions:
+  contents: read
+
 jobs:
   build:
     if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks

.github/workflows/arm-ci.yml

@@ -26,6 +26,9 @@ on:
       - master
       - r2.**
 
+permissions:
+  contents: read
+
 jobs:
   build:
     # Don't do this in forks, and if labeled, only for 'kokoro:force-run'

.github/workflows/cffconvert.yml

@@ -20,6 +20,9 @@ on:
     paths:
       - CITATION.cff
 
+permissions:
+  contents: read
+
 jobs:
   validate:
     if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks

.github/workflows/issue-on-pr-rollback.yml

@@ -18,10 +18,16 @@ on:
   push:
     branches:
       - master
+
+permissions: {}
 
 jobs:
   create-issue-on-pr-rollback:
     runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      issues: write
+      pull-requests: read
     if: |
       github.repository == 'tensorflow/tensorflow' &&
       startsWith(github.event.head_commit.message, 'Rollback of PR #')

.github/workflows/pylint-presubmit.yml

@@ -19,6 +19,9 @@ on:
     paths:
       - '**.py'
 
+permissions:
+  contents: read
+
 jobs:
   build:
     name: PyLint

.github/workflows/release-branch-cherrypick.yml

@@ -35,6 +35,9 @@ on:
         required: true
         type: string
 
+permissions:
+  contents: read
+
 jobs:
   cherrypick:
     name: Cherrypick to ${{ github.event.inputs.release_branch}} - ${{ github.event.inputs.git_commit }}

.github/workflows/sigbuild-docker-branch.yml

@@ -25,6 +25,9 @@ on:
     branches:
       - "r[1-9].[0-9]+"
 
+permissions:
+  contents: read
+
 jobs:
   docker:
     if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks

.github/workflows/sigbuild-docker-presubmit.yml

@@ -23,13 +23,19 @@ on:
       - 'tensorflow/tools/tf_sig_build_dockerfiles/**'
       - '!tensorflow/tools/tf_sig_build_dockerfiles/README.md'
 
+permissions:
+  contents: read
+
 jobs:
   docker:
     if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
     runs-on: ubuntu-latest
     strategy:
       matrix:
         python-version: [python3.9, python3.10, python3.11]
+    permissions:
+      contents: read
+      pull-requests: write
     steps:
       - name: Delete unnecessary tools folder
         run: |

.github/workflows/sigbuild-docker.yml

@@ -28,6 +28,9 @@ on:
     branches:
       - master
 
+permissions:
+  contents: read
+
 jobs:
   docker:
     if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks

.github/workflows/stale-issues.yml

@@ -18,6 +18,9 @@ on:
   schedule:
     - cron: "30 1 * * *"
 
+permissions:
+  contents: read
+
 jobs:
   close-issues:
     # Don't do this in forks

.github/workflows/trusted-partners.yml

@@ -17,6 +17,9 @@ name: Trusted Partner PR
 on:
   pull_request_target:
 
+permissions:
+  contents: read
+
 jobs:
   assign-partner-tags:
     runs-on: ubuntu-latest

.github/workflows/update-nightly.yml

@@ -18,10 +18,15 @@ on:
   schedule:
     - cron: 0 4 * * *  # 4am UTC is 9pm PDT and 8pm PST
 name: Set nightly branch to master HEAD
+
+permissions: {}
+
 jobs:
   master-to-nightly:
     if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
     runs-on: ubuntu-latest
+    permissions:
+      contents: write
     steps:
     - uses: zofrex/mirror-branch@a8809f0b42f9dfe9b2c5c2162a46327c23d15266 # v1.0.3
       name: Set nightly branch to master HEAD

.github/workflows/update-rbe.yml

@@ -20,6 +20,9 @@ name: Update RBE Configs
 on:
   workflow_dispatch:
 
+permissions:
+  contents: read
+
 jobs:
   rbe:
     name: Update RBE Configs

README.md

@@ -6,7 +6,7 @@
 [![PyPI](https://badge.fury.io/py/tensorflow.svg)](https://badge.fury.io/py/tensorflow)
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4724125.svg)](https://doi.org/10.5281/zenodo.4724125)
 [![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/1486/badge)](https://bestpractices.coreinfrastructure.org/projects/1486)
-[![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/tensorflow/tensorflow/badge)](https://api.securityscorecards.dev/projects/github.com/tensorflow/tensorflow)
+[![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/tensorflow/tensorflow/badge)](https://securityscorecards.dev/viewer/?uri=github.com/tensorflow/tensorflow)
 [![Fuzzing Status](https://oss-fuzz-build-logs.storage.googleapis.com/badges/tensorflow.svg)](https://bugs.chromium.org/p/oss-fuzz/issues/list?sort=-opened&can=1&q=proj:tensorflow)
 [![Fuzzing Status](https://oss-fuzz-build-logs.storage.googleapis.com/badges/tensorflow-py.svg)](https://bugs.chromium.org/p/oss-fuzz/issues/list?sort=-opened&can=1&q=proj:tensorflow-py)
 [![OSSRank](https://shields.io/endpoint?url=https://ossrank.com/shield/44)](https://ossrank.com/p/44)

RELEASE.md

@@ -45,7 +45,7 @@
 
 *   `tf.lite`:
 
-    *   `mul_op` supports broadcasting up to 6 dimensions.
+    *   `sub_op` and `mul_op` support broadcasting up to 6 dimensions.
 
     *  The `tflite::SignatureRunner` class, which provides support for named
        parameters and for multiple named computations within a single TF Lite
@@ -75,6 +75,9 @@
        *    `TfLiteSignatureRunnerInvoke`
        *    `TfLiteSignatureRunnerResizeInputTensor`
 
+    * New C API function `TfLiteExtensionApisVersion` added to
+      `tensorflow/lite/c/c_api.h`.
+
 * Android NDK r25 is supported.
 
 ### Bug Fixes and Other Changes
@@ -155,80 +158,38 @@ This release contains contributions from many people at Google, as well as:
 
 # Release 2.14.0
 
-<INSERT SMALL BLURB ABOUT RELEASE FOCUS AREA AND POTENTIAL TOOLCHAIN CHANGES>
+## Tensorflow
 
-# Breaking Changes
+### Breaking Changes
 
-* <DOCUMENT BREAKING CHANGES HERE>
-* <THIS SECTION SHOULD CONTAIN API, ABI AND BEHAVIORAL BREAKING CHANGES>
+*   Support for Python 3.8 has been removed starting with TF 2.14. The TensorFlow 2.13.1 patch release will still have Python 3.8 support.
 
 *  `tf.Tensor`
-    * The class hierarchy for `tf.Tensor` has changed, and there are now
-      explicit `EagerTensor` and `SymbolicTensor` classes for eager and
-      tf.function respectively. Users who relied on the exact type of Tensor
-      (e.g. `type(t) == tf.Tensor`) will need to update their code to use
-      `isinstance(t, tf.Tensor)`. The `tf.is_symbolic_tensor` helper added in
-      2.13 may be used when it is necessary to determine if a value is
-      specifically a symbolic tensor.
+    * The class hierarchy for `tf.Tensor` has changed, and there are now explicit `EagerTensor` and `SymbolicTensor` classes for eager and tf.function respectively. Users who relied on the exact type of Tensor (e.g. `type(t) == tf.Tensor`) will need to update their code to use `isinstance(t, tf.Tensor)`. The `tf.is_symbolic_tensor` helper added in 2.13 may be used when it is necessary to determine if a value is specifically a symbolic tensor.
 
 *   `tf.compat.v1.Session`
-    * `tf.compat.v1.Session.partial_run` and
-      `tf.compat.v1.Session.partial_run_setup` will be deprecated in the
-      next release.
+    * `tf.compat.v1.Session.partial_run` and `tf.compat.v1.Session.partial_run_setup` will be deprecated in the next release.
 
-*   `tf.estimator`
-    * `tf.estimator` API will be removed in the next release. TF Estimator
-       Python package will no longer be released.
-
-# Known Caveats
+### Known Caveats
 
-* <CAVEATS REGARDING THE RELEASE (BUT NOT BREAKING CHANGES).>
-* <ADDING/BUMPING DEPENDENCIES SHOULD GO HERE>
-* <KNOWN LACK OF SUPPORT ON SOME PLATFORM, SHOULD GO HERE>
 * `tf.lite`
-    * when converter flag "_experimenal_use_buffer_offset" is enabled,
-    additional metadata is automatically excluded from the generated model.
-    The behaviour is the same as "exclude_conversion_metadata" is set
-    * If the model is larger than 2GB, then we also require
-    "exclude_conversion_metadata" flag to be set
+    * when converter flag "_experimenal_use_buffer_offset" is enabled, additional metadata is automatically excluded from the generated model. The behaviour is the same as "exclude_conversion_metadata" is set
+    * If the model is larger than 2GB, then we also require "exclude_conversion_metadata" flag to be set
 
-# Major Features and Improvements
-
-*   <INSERT MAJOR FEATURE HERE, USING MARKDOWN SYNTAX>
-*   <IF RELEASE CONTAINS MULTIPLE FEATURES FROM SAME AREA, GROUP THEM TOGETHER>
+### Major Features and Improvements
 
-*   The `tensorflow` pip package has a new, optional installation method for
-    Linux that installs necessary Nvidia CUDA libraries through pip. As long as
-    the Nvidia driver is already installed on the system, you may now run `pip
-    install tensorflow[and-cuda]` to install TensorFlow's Nvidia CUDA library
-    dependencies in the Python environment. Aside from the Nvidia driver, no
-    other pre-existing Nvidia CUDA packages are necessary.
+*   The `tensorflow` pip package has a new, optional installation method for Linux that installs necessary Nvidia CUDA libraries through pip. As long as the Nvidia driver is already installed on the system, you may now run `pip install tensorflow[and-cuda]` to install TensorFlow's Nvidia CUDA library dependencies in the Python environment. Aside from the Nvidia driver, no other pre-existing Nvidia CUDA packages are necessary.
 
-* `tf.keras`
-    * `Model.compile` now support `steps_per_execution='auto'` as a parameter,
-    allowing automatic tuning of steps per execution during `Model.fit`,
-    `Model.predict`, and `Model.evaluate` for a significant performance boost.
-    * New `StepsPerExecutionTuner` class allows for `steps_per_execution` tuning
-    for custom training loops enabling performance boosts in custom workflows.
-    * Model now has two more settable parameters, `steps_per_execution` to set
-    this value to a manual heuristic, and `autotune_steps_per_execution` to
-    enable and disable tuning.
-
-*   Enable JIT-compiled i64-indexed kernels on GPU for large tensors with more
-    than 2**32 elements.
-    *   Unary GPU kernels: Abs, Atanh, Acos, Acosh, Asin, Asinh, Atan, Cos,
-        Cosh, Sin, Sinh, Tan, Tanh.
-    *   Binary GPU kernels: AddV2, Sub, Div, DivNoNan, Mul, MulNoNan, FloorDiv,
-        Equal, NotEqual, Greater, GreaterEqual, LessEqual, Less.
+*   Enable JIT-compiled i64-indexed kernels on GPU for large tensors with more than 2**32 elements.
+    *   Unary GPU kernels: Abs, Atanh, Acos, Acosh, Asin, Asinh, Atan, Cos, Cosh, Sin, Sinh, Tan, Tanh.
+    *   Binary GPU kernels: AddV2, Sub, Div, DivNoNan, Mul, MulNoNan, FloorDiv, Equal, NotEqual, Greater, GreaterEqual, LessEqual, Less.
 
 * `tf.lite`
-    * Add experimental supports conversion of models that may be larger than 2GB
-     before buffer deduplication
+    * Add experimental supports conversion of models that may be larger than 2GB before buffer deduplication
 
-# Bug Fixes and Other Changes
+### Bug Fixes and Other Changes
 
-* `tf.py_function` and `tf.numpy_function` can now be used as function
-   decorators for clearer code:
+* `tf.py_function` and `tf.numpy_function` can now be used as function decorators for clearer code:
    ```
    @tf.py_function(Tout=tf.float32)
    def my_fun(x):
@@ -237,59 +198,47 @@ This release contains contributions from many people at Google, as well as:
    ```
 
 * `tf.lite`
-    * `Strided_Slice` now supports `UINT32`.
-    * Add int8 and int16x8 support for LOG operator
-
-* <SIMILAR TO ABOVE SECTION, BUT FOR OTHER IMPORTANT CHANGES / BUG FIXES>
-* <IF A CHANGE CLOSES A GITHUB ISSUE, IT SHOULD BE DOCUMENTED HERE>
-* <NOTES SHOULD BE GROUPED PER AREA>
+    * Strided_Slice now supports `UINT32`.
 
 * `tf.config.experimental.enable_tensor_float_32_execution`
-    * Disabling TensorFloat-32 execution now causes TPUs to use float32
-      precision for float32 matmuls and other ops. TPUs have always used
-      bfloat16 precision for certain ops, like matmul, when such ops had float32
-      inputs. Now, disabling TensorFloat-32 by calling
-      `tf.config.experimental.enable_tensor_float_32_execution(False)` will
-      cause TPUs to use float32 precision for such ops instead of bfloat16.
+    * Disabling TensorFloat-32 execution now causes TPUs to use float32 precision for float32 matmuls and other ops. TPUs have always used bfloat16 precision for certain ops, like matmul, when such ops had float32 inputs. Now, disabling TensorFloat-32 by calling `tf.config.experimental.enable_tensor_float_32_execution(False)` will cause TPUs to use float32 precision for such ops instead of bfloat16.
 
 *  `tf.experimental.dtensor`
-    * API changes for Relayout. Added a new API, `dtensor.relayout_like`, for
-      relayouting a tensor according to the layout of another tensor.
-    * Added `dtensor.get_default_mesh`, for retrieving the current default
-      mesh under the dtensor context.
-    * \*fft\* ops now support dtensors with any layout. Fixed bug in 'fft2d/
-      fft3d', 'ifft2d/ifft3d', 'rfft2d/rfft3d', and 'irfft2d/irfft3d' for
-      sharded input.
+    * API changes for Relayout. Added a new API, `dtensor.relayout_like`, for relayouting a tensor according to the layout of another tensor.
+    * Added `dtensor.get_default_mesh`, for retrieving the current default mesh under the dtensor context.
+    * \*fft\* ops now support dtensors with any layout. Fixed bug in 'fft2d/fft3d', 'ifft2d/ifft3d', 'rfft2d/rfft3d', and 'irfft2d/irfft3d' for sharde input. Refer to this [blog post](https://blog.tensorflow.org/2023/08/distributed-fast-fourier-transform-in-tensorflow.html) for details.
 
 *  `tf.experimental.strict_mode`
-    * Added a new API, `strict_mode`, which converts all deprecation warnings
-      into runtime errors with instructions on switching to a recommended 
-      substitute.
+    * Added a new API, `strict_mode`, which converts all deprecation warnings into runtime errors with instructions on switching to a recommended  substitute.
 
 *   TensorFlow Debugger (tfdbg) CLI: ncurses-based CLI for tfdbg v1 was removed.
 
-*   TensorFlow now supports C++ RTTI on mobile and Android. To enable this
-    feature, pass the flag `--define=tf_force_rtti=true` to Bazel when building
-    TensorFlow. This may be needed when linking TensorFlow into RTTI-enabled
-    programs since mixing RTTI and non-RTTI code can cause ABI issues.
+*   TensorFlow now supports C++ RTTI on mobile and Android. To enable this feature, pass the flag `--define=tf_force_rtti=true` to Bazel when building TensorFlow. This may be needed when linking TensorFlow into RTTI-enabled programs since mixing RTTI and non-RTTI code can cause ABI issues.
+
+* `tf.ones`, `tf.zeros`, `tf.fill`, `tf.ones_like`, `tf.zeros_like` now take an additional Layout argument that controls the output layout of their results.
+
+* `tf.nest` and `tf.data` now support user defined classes implementing `__tf_flatten__` and `__tf_unflatten__` methods. See [nest_util code examples](https://github.com/tensorflow/tensorflow/blob/04869b4e63bfc03cb13627b3e1b879fdd0f69e34/tensorflow/python/util/nest_util.py#L97)
+for an example.
+
+*  TensorFlow IO support is now available for Apple Silicon packages.
 
-* `tf.ones`, `tf.zeros`, `tf.fill`, `tf.ones_like`, `tf.zeros_like` now take an
-    additional Layout argument that controls the output layout of their results.
+*  Refactor CpuExecutable to propagate LLVM errors.
 
-*  Limited support of unified n-d FFT Ops: `tf.signal.fftn`,
-   `tf.signal.ifftn`, `tf.signal.rfftn`, `tf.signal.irfftn`.
-   Note that they only support up to 3d and gradients are unsupported.
+## Keras
+
+Keras is a framework built on top of the TensorFlow. See more details on the Keras [website](https://keras.io/).
+
+### Major Features and Improvements
 
-* `tf.nest` and `tf.data` now support user defined classes implementing
-  `__tf_flatten__` and `__tf_unflatten__` methods. See [
-  nest_util code examples](https://github.com/tensorflow/tensorflow/blob/04869b4e63bfc03cb13627b3e1b879fdd0f69e34/tensorflow/python/util/nest_util.py#L97)
-  for an example.
+* `tf.keras`
+    * `Model.compile` now support `steps_per_execution='auto'` as a parameter, allowing automatic tuning of steps per execution during `Model.fit`,
+    `Model.predict`, and `Model.evaluate` for a significant performance boost.
 
-# Thanks to our Contributors
+## Thanks to our Contributors
 
 This release contains contributions from many people at Google, as well as:
 
-<INSERT>, <NAME>, <HERE>, <USING>, <GITHUB>, <HANDLE>
+Aakar Dwivedi, Adrian Popescu, ag.ramesh, Akhil Goel, Albert Zeyer, Alex Rosen, Alexey Vishnyakov, Andrew Goodbody, angerson, Ashiq Imran, Ayan Moitra, Ben Barsdell, Bhavani Subramanian, Boian Petkantchin, BrianWieder, Chris Mc, cloudhan, Connor Flanagan, Daniel Lang, Daniel Yudelevich, Darya Parygina, David Korczynski, David Svantesson, dingyuqing05, Dragan Mladjenovic, dskkato, Eli Kobrin, Erick Ochoa, Erik Schultheis, Frédéric Bastien, gaikwadrahul8, Gauri1 Deshpande, guozhong.zhuang, H. Vetinari, Isaac Cilia Attard, Jake Hall, Jason Furmanek, Jerry Ge, Jinzhe Zeng, JJ, johnnkp, Jonathan Albrecht, jongkweh, justkw, Kanvi Khanna, kikoxia, Koan-Sin Tan, Kun-Lu, ltsai1, Lu Teng, luliyucoordinate, Mahmoud Abuzaina, mdfaijul, Milos Puzovic, Nathan Luehr, Om Thakkar, pateldeev, Peng Sun, Philipp Hack, pjpratik, Poliorcetics, rahulbatra85, rangjiaheng, Renato Arantes, Robert Kalmar, roho, Rylan Justice, Sachin Muradi, samypr100, Saoirse Stewart, Shanbin Ke, Shivam Mishra, shuw, Song Ziming, Stephan Hartmann, Sulav, sushreebarsa, T Coxon, Tai Ly, talyz, Thibaut Goetghebuer-Planchon, Thomas Preud'Homme, tilakrayal, Tirumalesh, Tj Xu, Tom Allsop, Trevor Morris, Varghese, Jojimon, Wen Chen, Yaohui Liu, Yimei Sun, Zhoulong Jiang, Zhoulong, Jiang
 
 # Release 2.13.0