EXPECT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
  }

  void BuildExpectedGraph(bool grad_inputs_provided,
                          TF_Output* expected_grad_outputs) {
    // The expected graph looks like this if grad_inputs_provided.
    // If grad_inputs_provided is false, Const_0 will be a OnesLike op.
    //      ^             ^
    //    dy|           dx|        // MatMul Gradient Graph
    //      |             |

    bazel version  # Start the bazel server
}

# Do a bazel query specifically for the licenses checker. It searches for
# targets matching the provided query, which start with // or @ but not
# //tensorflow (so it looks for //third_party, //external, etc.), and then
# gathers the list of all packages (i.e. directories) which contain those
# targets.
license_query() {
 bazel cquery --experimental_cc_shared_library "$1" --keep_going \

  "$TF_ANY_SCRIPT"
elif [[ -n "${TF_ANY_TARGETS:-}" ]]; then
  source "${BASH_SOURCE%/*}/utilities/setup.sh"
  tfrun bazel "${TF_ANY_MODE:-test}" $TFCI_BAZEL_COMMON_ARGS $TF_ANY_TARGETS
else
  echo 'Looks like $TF_ANY_TARGETS are $TF_ANY_SCRIPT are both empty. That is an error.'
  exit 1

setup_file() {
    bazel version  # Start the bazel server
}

# Do a bazel query specifically for the licenses checker. It searches for
# targets matching the provided query, which start with // or @ but not
# //tensorflow (so it looks for //third_party, //external, etc.), and then
# gathers the list of all packages (i.e. directories) which contain those
# targets.
license_query() {
 bazel cquery --experimental_cc_shared_library "$1" --keep_going \

  // Builds a tensor filled with ones with the same shape and dtype as `t`.
  virtual Status BuildOnesLike(const TapeTensor& t,
                               Gradient** result) const = 0;

  // Looks up the ID of a Gradient.
  virtual int64_t TensorId(Gradient* tensor) const = 0;

  // Converts a Gradient to a TapeTensor.
  virtual TapeTensor TapeTensorFromGradient(Gradient* gradient) const = 0;

# words checklist
segfault_memory:
   - segfault
   - memory leaks
# assignees
filesystem_security_assignee:
   - mihaimaruseac
   
# Cuda Comment
cuda_comment: >
   From the template it looks like you are installing **TensorFlow** (TF) prebuilt binaries:
      * For TF-GPU - See point 1
      * For TF-CPU - See point 2
   -----------------------------------------------------------------------------------------------

# will be created.

name: Release Branch Cherrypick
on:
  workflow_dispatch:
    inputs:
      # We use this instead of the "run on branch" argument because GitHub looks
      # on that branch for a workflow.yml file, and we'd have to cherry-pick
      # this file into those branches.
      release_branch:
        description: 'Release branch name (e.g. r2.9)'
        required: true

    rare occasion we may reject it.

**3. Review**

-   For a valid PR, reviewer (person familiar with the code/functionality)
    checks if the PR looks good or needs additional changes.
-   If all looks good, the reviewer will approve the PR.
-   If a change is needed, the contributor is requested to make the suggested
    change.
-   You make the change and submit it for the review again.

  // Builds a tensor filled with ones with the same shape and dtype as `t`.
  Status BuildOnesLike(const TapeTensor& t,
                       AbstractTensorHandle** result) const override;

  // Looks up the ID of a Gradient.
  int64_t TensorId(AbstractTensorHandle* tensor) const override;

  // Converts a Gradient to a TapeTensor.
  TapeTensor TapeTensorFromGradient(AbstractTensorHandle* g) const override;

            # "-Wl,--undefined-version",
            # copybara:uncomment_end(google-only)
        ],
    }),
    linkstatic = 1,
    per_os_targets = True,
    roots = [
                "//tensorflow/c/experimental/filesystem:filesystem_interface",
                "//tensorflow/c/experimental/stream_executor:stream_executor",
                "//tensorflow/c:env",