with:
        ref: ${{ github.event.inputs.release_branch }}
    - name: Get some helpful info for formatting
      id: cherrypick
      run: |
          git config --global user.name "TensorFlow Release Automation"
          git config --global user.email "******@****.***"
          git fetch origin master
          git cherry-pick ${{ github.event.inputs.git_commit }}

//              creating a function with no body (e.g. identity or permutation)
//              and the case of creating a function whose body contains all
//              the nodes in the graph (except for the automatic skipping, see
//              below).
//  opers - Array of operations to become the body of the function or null.
//          - If no array is given (`num_opers` = -1), all the

      uses: peter-evans/create-pull-request@2b011faafdcbc9ceb11414d64d0573f37c774b04 # v4.2.3
      with:
        title: Update the RBE images to the latest container versions
        committer: TensorFlow Release Automation <******@****.***>
        token: ${{ secrets.JENKINS_TOKEN }}
        reviewers: mihaimaruseac,learning-to-play,nitins17
        body: |

      : std::function<Status(const std::vector<Gradient*>&,
                             std::vector<Gradient*>*, bool)>(lambda) {}
};

// Computes Jacobian-vector products using forward-mode automatic
// differentiation.
//
// While GradientTape's RecordOperation is trivial, ForwardAccumulator's
// Accumulate runs the gradient computation immediately.
//

#
#    - New environments like new CUDA versions, changes to manylinux,
#      compilers, etc. can cause undefined behavior such as build failures
#      or tests passing incorrectly.
#    - Automatic LLVM updates are known to extend build time even with
#      the cache; this is unavoidable.
export TFCI=py311,linux_x86,public_cache,disk_cache

# Recommended: Configure Docker. (Linux only)
#

### Major Features and Improvements

* `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