env:
  UV_SYSTEM_PYTHON: 1

jobs:
  changes:
    runs-on: ubuntu-latest
    # Required permissions
    permissions:
      pull-requests: read
    # Set job outputs to values from filter step
    outputs:
      docs: ${{ steps.filter.outputs.docs }}
    steps:
    - uses: actions/checkout@v4
    # For pull requests it's not necessary to checkout the code but for the main branch it is
    - uses: dorny/paths-filter@v3

            File outputDirectory = new File(project.getBuild().getOutputDirectory());

            // Check if the project is being built during this session, and if we can expect any output.
            // There is no need to check if the build has created any outputs, see MNG-2222.
            boolean projectCompiledDuringThisSession =
                    project.hasLifecyclePhase("compile") && COMPILE_PHASE_TYPES.contains(type);

            CACHEBUSTER=${{ steps.vars.outputs.DATE }}
          tags: |
            tensorflow/build:${{ steps.vars.outputs.REF }}-${{ matrix.python-version }}
            gcr.io/tensorflow-sigs/build:${{ steps.vars.outputs.REF }}-${{ matrix.python-version }}
          cache-from: type=registry,ref=tensorflow/build:${{ steps.vars.outputs.REF }}-${{ matrix.python-version }}
          cache-to: type=inline
      -

        absl::StrCat("ZerosLike", ToId(t)).c_str()));
  }
  TF_RETURN_IF_ERROR(op->AddInput(t));
  int num_outputs = 1;
  std::vector<AbstractTensorHandle*> outputs(num_outputs);
  TF_RETURN_IF_ERROR(
      op->Execute(absl::Span<AbstractTensorHandle*>(outputs), &num_outputs));
  *result = outputs[0];
  return absl::OkStatus();
}
}  // namespace

absl::Status GradientRegistry::Register(

  unwrap(o)->expected_num_outputs = num_outputs;
  unwrap(o)->outputs.clear();
  unwrap(o)->outputs.resize(num_outputs);
}
int TF_OutputListNumOutputs(TF_OutputList* o) {
  return unwrap(o)->outputs.size();
}
TF_AbstractTensor* TF_OutputListGet(TF_OutputList* o, int i) {
  return wrap(unwrap(o)->outputs[i]);
}
void TF_OutputListPushBack(TF_OutputList* o, TF_AbstractTensor* tensor,

  auto outputs = parallel_device.Execute(
      context.get(), {reduced_values.get(), run_collective.get()},
      "AssertAndCollective", TFE_OpGetAttrs(call_op.get()),
      /*expected_max_outputs=*/1, status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
  ASSERT_EQ(outputs->size(), 1);
  ParallelTensor* parallel_result = (*outputs)[0].get();

  int num_retvals = 1;
  std::vector<AbstractTensorHandle*> outputs(1);
  GradientRegistry registry;
  s = RegisterGradients(&registry);
  ASSERT_EQ(errors::OK, s.code()) << s.message();
  auto tape = std::make_unique<Tape>(/*persistent=*/false);
  s = Execute(check_numerics_op.get(), ctx.get(), absl::MakeSpan(outputs),
              &num_retvals, &forward_op, tape.get(), registry);

  std::unique_ptr<tensorflow::ServerInterface> server;
};
#endif  // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)

namespace tensorflow {

// Set the shapes and types of the output's handle.
//
// The lengths of the arrays pointed to by `shapes`, `ranks`, and `types` must
// all be equal to `num_shapes_and_types`. If `ranks[i] != -1`, (i.e., if the

        committer: TensorFlow Release Automation <******@****.***>
        token: ${{ secrets.JENKINS_TOKEN }}
        base: ${{ github.event.inputs.release_branch }}
        branch: ${{ github.event.inputs.release_branch }}-${{ steps.cherrypick.outputs.SHORTSHA }}
        reviewers: learning-to-play
        body: |

// 'retvals' must point to a pre-allocated array of TFE_TensorHandle* and
// '*num_retvals' should be set to the size of this array. It is an error if
// the size of 'retvals' is less than the number of outputs. This call sets
// *num_retvals to the number of outputs.
//
// If async execution is enabled, the call may simply enqueue the execution
// and return "non-ready" handles in `retvals`. Note that any handles contained